Showing papers in "IEEE Transactions on Automation Science and Engineering in 2019"

Theory and Experiment on Formation-Containment Control of Multiple Multirotor Unmanned Aerial Vehicle Systems

Abstract: Formation-containment control problems for multiple multirotor unmanned aerial vehicle (UAV) systems with directed topologies are studied, where the states of leaders form desired formation and the states of followers converge to the convex hull spanned by those of the leaders. First, formation-containment protocols are constructed based on the neighboring information of UAVs. Then, sufficient conditions for multi-UAV systems to achieve formation-containment are presented. An explicit expression to describe the relationship among the states of followers, the time-varying formation for the leaders and the formation reference is derived. It is shown that the states of followers not only converge to the convex hull formed by those of leaders but also keep certain formation specified by the convex combination of the formation for the leaders. Moreover, an approach to determine the gain matrices of the formation-containment protocol is proposed by solving an algebraic Riccati equation. Finally, a formation-containment platform with five quadrotor UAVs is introduced, and both the simulation and experimental results are presented to demonstrate the effectiveness of the obtained results. Note to Practitioners —This paper addresses the problem of formation-containment control for multi-UAV systems over directed topologies. In practical applications, there may exist multiple leaders and multiple followers in a multi-UAV system. Formation-containment means that the states of leaders form the desired time-varying formation and at the same time the states of the followers converge to the convex hull spanned by those of the leaders. Formation-containment control provides a unified framework for formation control and containment control, and has potential applications in the cooperative source seeking, load transportation, and surveillance. Although formation control and containment control problems have been studied a lot, the formation-containment control problem for multi-UAV system is still open and challenging. This paper proposed a distributed formation-containment protocol for the multi-UAV system using local neighboring information. Sufficient conditions for multi-UAV systems to achieve formation-containment are presented. It is proven that the states of followers not only converge to the convex hull formed by those of leaders but also keep certain formation specified by the convex combination of the formation for the leaders. An approach to design the formation-containment protocol is given. A remarkable point for this paper is that the obtained results are demonstrated by practical experiments with five quadrotor UAVs.

TL-GDBN: Growing Deep Belief Network With Transfer Learning

Abstract: A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. Note to Practitioners —Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.

Multi-UAV Oxyrrhis Marina-Inspired Search and Dynamic Formation Control for Forest Firefighting

Abstract: This paper presents an Oxyrrhis Marina-inspired search and dynamic formation control (OMS-DFC) framework for multi-unmanned aerial vehicle (UAV) systems to efficiently search and neutralize a dynamic target (forest fire) in an unknown/uncertain environment. The OMS-DFC framework consists of two stages, viz., the target identification stage without communication between UAVs and the mitigation stage with restricted communication. In the first stage, each UAV adapts proposed OMS with three levels to select between Levy flight, Brownian search, and directionally driven Brownian (DDB) search for accurate target identification (“fire location”). The selection of each level is based on the available sensor information about the possible fire location. In the second stage, the UAVs that identified a fire location fly in a dynamic formation to quench the fire using water. The proposed formation is achieved through decentralized control, where a UAV computes the control action based on the fire profile and also the angular position and angular separation with its succeeding neighbor. The proposed formation control law guarantees asymptotic convergence to the desired time-varying angular position profile of UAVs based on the nature of fire spread (circular/elliptical). To evaluate the performance of the proposed OMS-DFC for the multi-UAV system, a search and fire quenching mission in a typical pine forest is simulated. A Monte Carlo simulation study is conducted to evaluate the average performance of the proposed OMS-DFC-based multi-UAV mission, and the results clearly highlight the advantages of the proposed OMS-DFC in forest firefighting. Note to Practitioners —Searching and mitigating dynamic targets like the forest fire is a challenging task due to the large area involved and also the time-varying nature of fire spread. The use of a cooperative multi-unmanned aerial vehicle (UAV) system for searching targets in large area poses difficulties in maintaining persistent long distance communication between them. Moreover, the elliptical fire profile demands a time-varying angular displacement formation control of UAVs for effective fire mitigation. In this paper, we present a two-stage framework for search and mitigation of forest fire. The first stage provides a decentralized, noniterative stochastic search algorithm that requires no information sharing between the UAVs. The proposed search algorithm can be implemented without much computational efforts using a temperature measuring sensor and a thermal imaging sensor. The second stage provides a decentralized time-varying angular displacement formation control law efficient for tracking elliptical targets. The formation control law only assumes the availability of restricted UAV communication. The proposed formation control law can handle any targets that demand time-varying angular displacement formation for UAVs. The proposed algorithm is suitable for multi-UAV missions involving search and mitigation of dynamic targets distributed over a large area.

Scheduling Semiconductor Testing Facility by Using Cuckoo Search Algorithm With Reinforcement Learning and Surrogate Modeling

Abstract: A semiconductor final testing scheduling problem with multiresource constraints is considered in this paper, which is proved to be NP-hard. To minimize the makespan for this scheduling problem, a cuckoo search algorithm with reinforcement learning (RL) and surrogate modeling is presented. A parameter control scheme is proposed to ensure the desired diversification and intensification of population on the basis of RL, which uses the proportion of beneficial mutation as feedback information according to Rechenberg’s 1/5 criterion. To reduce computational complexity, a surrogate model is employed to evaluate the relative ranking of solutions. A heuristic approach based on the relative ranking of encoding value and a modular function is proposed to convert continuous solutions obtained from Levy flight into discrete ones. The computational complexity and convergence analysis results are presented. The proposed algorithm is validated with benchmark and randomly generated cases. Various simulation experiments and comparison between the proposed algorithm and several popular methods are performed to validate its effectiveness. Note to Practitioners —Scheduling of semiconductor final testing is usually solved via intelligent optimization algorithms. Nevertheless, most of them are parameter-sensitive, and thus, selecting their proper parameters is a huge challenge. In order to deal with the parameter selection issue, we propose a reinforcement learning (RL) algorithm to self-adjust their parameters. To reduce the computational burden, we propose to use surrogate modeling of the reward function in RL and determine which nests should be reserved in cuckoo search. As a result, our algorithm possesses higher robustness and can obtain a high-quality schedule than the existing algorithms for semiconductor final testing facility. In addition, it has a lower computational complexity via the proposed surrogate model, and thus, a feasible solution can be obtained in a short time for real-time scheduling. Experimental results show that the proposed method well outperforms some existing algorithms. Hence, it can be readily applied to industrial semiconductor final testing facility scheduling problems.

Automatic Detection and Classification of Sewer Defects via Hierarchical Deep Learning

Abstract: Video and image sources are frequently applied in the area of defect inspection in industrial community. For the recognition and classification of sewer defects, a significant number of videos and images of sewers are collected. These data are then checked by human and some traditional methods to recognize and classify the sewer defects, which is inefficient and error-prone. Previously developed features like SIFT are unable to comprehensively represent such defects. Therefore, feature representation is especially important for defect autoclassification. In this paper, we study the automatic extraction of feature representation for sewer defects via deep learning. Moreover, a complete automatic system for classifying sewer defects is proposed built on a two-level hierarchical deep convolutional neural network, which shows high performance with respect to classification accuracy. The proposed network is trained on a novel data set with over 40 000 sewer images. The system has been successfully applied in the practical production, confirming its robustness and feasibility to real-world applications. The source code and trained model are available at the project website. 1 Note to Practitioners —Automatic defect inspection has become a fundamental research topic in engineering application field. Specifically, sewer defect detection is an important measure for maintenance, renewal, and rehabilitation activities of sewer infrastructure. In the current operation procedure, all the captured videos need to be inspected by experts frame by frame to recognize defects, yielding a significant low inspection rate with a significant amount of time. Previous work has attempted to employ traditional image processing methods for automated sewer defect classification. However, these methods get poor generalization capabilities since they use pre-engineered features. In most cases, sewerage inspection companies have to hire numerous professional inspectors to do this job, thereby consuming a lot of human and material resources. To address this problem, the authors propose an automatic detection and classification method for sewer defects based on hierarchical deep learning. Demonstrated by various experiments, the designed framework achieves a high defect classification accuracy, which can be easily integrated into an automatic sewer defect inspection system. 1 https://github.com/NUAAXQ/SewerDefectDetection

Coarse-to-Fine UAV Target Tracking With Deep Reinforcement Learning

Abstract: The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV) tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle from such a problem as they mainly focus on the scale variation issue by maintaining a certain aspect ratio. In this paper, we propose a coarse-to-fine deep scheme to address the aspect ratio variation in UAV tracking. The coarse-tracker first produces an initial estimate for the target object, then a sequence of actions are learned to fine-tune the four boundaries of the bounding box. The coarse-tracker and the fine-tracker are designed to have different action spaces and operating target. The former dominates the entire bounding box and the latter focuses on the refinement of each boundary. They are trained jointly by sharing the perception network with an end-to-end reinforcement learning architecture. Experimental results on benchmark aerial data set prove that the proposed approach outperforms existing trackers and produces significant accuracy gains in dealing with the aspect ratio variation in UAV tracking. Note to Practitioners —During the past years, unmanned aerial vehicle (UAV) have gained much attention for both industrial and consumer uses. It is in urgent demand to endow the UAV with intelligent vision-based techniques, and the automatic target following via visual tracking methods as one of the most fundamental intelligent features could promote various applications of UAVs, such as surveillance, augmented reality, and behavior modeling. Nonetheless, the primary issue of a UAV-based tracking method is the platform itself: it is not stable, it tends to have sudden movements, it generates nonhomogeneous data (scale, angle, rotation, depth, and so on), all of them tend to change the aspect ratio of the target frequently and further increase the difficulty of object tracking. This paper aims to address the aspect ratio change (ARC) problem in UAV tracking. We present a coarse-to-fine strategy for UAV tracking. Specifically, the coarse bounding box is obtained to locate the target firstly. Then, a refinement scheme is performed on each boundary to further improve the position estimate. The tracker is proved to be effective to increase the resistance to the ARC. Such a method can be implemented on UAV to improve the target-following performance.

Facilitating Human–Robot Collaborative Tasks by Teaching-Learning-Collaboration From Human Demonstrations

Abstract: Collaborative robots are widely employed in strict hybrid assembly tasks involved in intelligent manufacturing. In this paper, we develop a teaching-learning-collaboration (TLC) model for the collaborative robot to learn from human demonstrations and assist its human partner in shared working situations. The human could program the robot using natural language instructions according to his/her personal working preferences via this approach. Afterward, the robot learns from human assembly demonstrations by taking advantage of the maximum entropy inverse reinforcement learning algorithm and updates its task-based knowledge using the optimal assembly strategy. In the collaboration process, the robot is able to leverage its learned knowledge to actively assist the human in the collaborative assembly task. Experimental results and analysis demonstrate that the proposed approach presents considerable robustness and applicability in human–robot collaborative tasks. Note to Practitioners —This paper is motivated by the human–robot collaborative assembly problem in the context of advanced manufacturing. Collaborative robotics makes a huge shift from the traditional robot-in-a-cage model to robots interacting with people in an open working environment. When the human works with the robot in the shared workspace, it is significant to lessen human programming effort and improve the human–robot collaboration efficiency once the task is updated. We develop a TLC model for the robot to learn from human demonstrations and assist its human partner in collaborative tasks. Once the task is changed, the human may code the robot via natural language instructions according to his/her personal working preferences. The robot can learn from human assembly demonstrations to update its task-based knowledge, which can be leveraged by the robot to actively assist the human to accomplish the collaborative task. We demonstrate the advantages of the proposed approach via a set of experiments in realistic human–robot collaboration contexts.

Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects

Abstract: Visual inspection of texture surface defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture defects based on a small number of defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background images. The residual images are obtained by subtracting these texture backgrounds from the input image individually; then, they are fused into one defect image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no defect samples. This is extremely important for industrial applications because identifying and labeling defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.

Haptics Electromyogrphy Perception and Learning Enhanced Intelligence for Teleoperated Robot

Abstract: Due to the lack of transparent and friendly human–robot interaction (HRI) interface, as well as various uncertainties, it is usually a challenge to remotely manipulate a robot to accomplish a complicated task. To improve the teleoperation performance, we propose a new perception mechanism by integrating a novel learning method to operate the robots in the distance. In order to enhance the perception of the teleoperation system, we utilize a surface electromyogram signal to extract the human operator’s muscle activation. As a response to the changes in the external environment, as sensed through haptic and visual feedback, a human operator naturally reacts with various muscle activations. By imitating the human behaviors in task execution, not only motion trajectory but also arm stiffness adjusted by muscle activation, it is expected that the robot would be able to carry out the repetitive tasks autonomously or uncertain tasks with improved intelligence. To this end, we develop a robot learning algorithm based on probability statistics under an integrated framework of the hidden semi-Markov model (HSMM) and the Gaussian mixture method. This method is employed to obtain a generative task model based on the robot’s trajectory. Then, Gaussian mixture regression based on HSMM is applied to correct the robot trajectory with the reproduced results from the learned task model. The execution procedures consist of a learning phase and a reproduction phase . To guarantee the stability, immersion, and maneuverability of the teleoperation system, a variable gain control method that involves electromyography (EMG) is introduced. Experimental results have demonstrated the effectiveness of the proposed method. Note to Practitioners —This paper is inspired by the limitations of teleoperation to perform a task with unfriendly HRI and lack of intelligence. The human operators need to concentrate on the manipulation in the traditional setup of a teleoperation system; thus, it is quite a labor intensive for a human operator. This is a huge challenge for the requirement of increasingly complicated, diverse tasks in teleoperation. Therefore, efficient ways of the robot intelligence need to be urgently developed for the telerobots. In this paper, we develop a robot intelligence framework by merging robot learning technology and perception mechanism. The proposed framework is effective where the task performed with repeatability and rapidity in a teleoperated mode. The proposed method includes three following ideas: 1) remote operation information can be actively sensed by infusing muscle activation with a haptics EMG perception mechanism; 2) the robot intelligence can be enhanced by employing a robot learning method. The developed approach is verified by the experimental results; and 3) the proposed method can be potentially used for telemanufacturing, teletehabilitation, and telemedicine, and so on. In our future work, more interactive information between humans and telerobots should be taken into consideration in the telerobot perception system to enhance the robot intelligence.

Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds

Abstract: With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky–Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods. Note to Practitioners —Workload prediction plays an important role in constructing scalable and green distributed cloud data centers. This paper presents a novel and fundamental methodology to achieve accuracy and fast learning for workload prediction. It develops an integrated prediction approach that combines the Savitzky–Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In order to establish a fine prediction model for the obtained information while achieving better prediction results and faster learning speed, this paper proposes an integrated method, SGW-S, to build a prediction model of a task time series and determine its optimal model parameters. The experimental results in the real-world data set show that the proposed method outperforms baseline methods in predicting the large-scale task time series. The proposed approach can aid the design and optimization of industrial cloud data centers and practitioners’ prediction of different types of task time series.

A Pareto-Based Estimation of Distribution Algorithm for Solving Multiobjective Distributed No-Wait Flow-Shop Scheduling Problem With Sequence-Dependent Setup Time

Abstract: Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST. Note to Practitioners —This paper is motivated by the process cycles in multiproduction factories (or lines) of baker production, surgery scheduling, and parallel computer systems. In these process cycles, jobs are assigned to multiproduction factories (or lines), and no interruption exists between consecutive operations. This paper models this process as a multiobjective distributed no-wait flow-shop scheduling with SDST. Scheduling becomes more challenging when facing distributed factories. This paper provides an estimation of distributed algorithm with Pareto dominate concept which uses a probabilistic model to generate offspring. Experiment results suggest that the proposed algorithm can find superior solutions of large-scale instances. This scheduling model can be extended to practical problems by considering other constraints, such as assembly process, mixed no-wait, and transporting times. Besides, the proposed algorithm can be applied to solve other distributed scheduling problems and industrial cases, once their constraints are known, i.e., the processing time of operations, the setup time of machines.

Charging Automation for Electric Vehicles: Is a Smaller Battery Good for the Wireless Charging Electric Vehicles?

Abstract: Dynamic wireless charging (DWC) is an emerging technology that enables the batteries of electric vehicles (EVs) to charge automatically while the vehicles are in motion. The DWC-EV system addresses the challenges inherent in battery technology, such as the short driving range, long recharging time, and high price. Compared with conventional plug-in EVs, the DWC-EV can charge a battery more frequently because it can be done while the EV is in motion from the charging infrastructure installed on the road. In this paper, we analyze how this frequent-charging characteristic of DWC-EV can affect the battery lifetime in the DWC-EV. We first introduce a mathematical model to evaluate the economic cost of the DWC-EV for a given battery size. A battery degradation model is incorporated to account for the quantitative relationship between the installation of the charging infrastructure and battery life extension. We then use the model to analyze how the economic cost varies with the size of the battery. Our preliminary findings provide insight into the relationship between DWC from the charging infrastructure and the battery’s lifetime. Note to Practitioners —There is a tradeoff between power track allocation and battery size in DWC buses. It has been known that DWC buses need not equip a large battery because the battery can be charged frequently from the power track along the route. Also, it has been agreed that frequent shallow charging with DWC can improve the battery’s lifetime compared to infrequent deep charging. We verify these common beliefs and agreement with a mathematical approach with a widely used battery lifetime estimation model. Our quantitative model indicates that the DWC can positively improve battery lifetime, but this positive effect is always true only when the battery is large enough, even if the battery is charged frequently with DWC. The presented optimization model suggests that the lifelong economic benefit of DWC-based bus transit can be realized with optimal allocation of the power track with a sufficiently large battery.

Decentralized EV-Based Charging Optimization With Building Integrated Wind Energy

Abstract: Electric vehicles (EVs) have experienced a rapid growth due to the economic and environmental benefits However, the substantial charging load brings challenging issues to the power grid Modern technological advances and the huge number of high-rise buildings have promoted the development of distributed energy resources, such as building integrated/mounted wind turbines The issue to coordinate EV charging with locally generated wind power of buildings can potentially reduce the impacts of EV charging demand on the power grid As a result, this paper investigates this important problem and three contributions are made First, the real-time scheduling of EV charging is addressed in a centralized framework based on the ideas of model predictive control, which incorporates the volatile wind power supply of buildings and the random daily driving cycles of EVs among different buildings Second, an EV-based decentralized charging algorithm (EBDC) is developed to overcome the difficulties due to: 1) the possible lack of global information regarding the charging requirements of all EVs and 2) the computational burden with the increasing number of EVs Third, we prove that the EBDC method can converge to the optimal solution of the centralized problem over each planning horizon Moreover, the performance of the EBDC method is assessed through numeric comparisons with an optimal and two heuristic charging strategies (ie, myopic and greedy) The results demonstrate that the EBDC method can achieve a satisfactory performance in improving the scalability and the balance between the EV charging demand and wind power supply of buildings Note to Practitioners —This paper is motivated by the challenging problem due to the substantial charging load of electric vehicles (EVs) on the power grid Nowadays, modern technological advances and the rapid increase of high-rise buildings have promoted the development of building integrated/mounted wind turbines As the EVs are usually parked in buildings for a large proportion of time every day, the issue to best utilize locally generated wind power of buildings to suffice EV travelling requirements shows vital significance in reducing their dependence on the power grid However, there exist two main challenges including: 1) the multiple uncertainties regarding the uncertain wind power generation and the random driving behaviors of EVs and 2) the scalability of the solution method To tackle the first challenge, the idea of model predictive control is introduced to make charging decisions at each stage based on a short-term prediction of the on-site wind power and the current collection of EVs parked there To consider the scalability and overcome the lack of global charging information of all EVs in practical deployment, an iterative EV-based decentralized charging algorithm (EBDC) is derived, in which each EV can dynamically update its own charging decisions according to a dynamic charging “price” announced by the buildings Alternatively, the buildings dynamically adjust the charging “price” to motivate the EVs to get charged during the time periods with sufficient wind power supply Numeric results demonstrate that the EBDC method is scalable and performs well in improving the balance between the EV charging demand and the wind power supply of buildings

Antiswing Cargo Transportation of Underactuated Tower Crane Systems by a Nonlinear Controller Embedded With an Integral Term

Abstract: A tower crane is a nonlinear mechatronic system with complicated underactuated characteristics, which is widely used in modern construction sites. At present, most existing methods for tower cranes are proposed by linearizing the original nonlinear dynamics near equilibrium points, which are, thus, prone to suffering from unexpected steady errors due to such factors as unmodeled dynamics, imperfect friction compensation, etc., since they have not included integral terms in either controller design or stability analysis. Therefore, in this paper, an improved feedback controller with an elaborately constructed integral term is proposed for 3-D tower cranes without linearization, which can achieve both antiswing and positioning control while being able to effectively reduce steady errors in the presence of, e.g., inaccurate friction compensation. Furthermore, asymptotic stability results are proven through rigorous theoretical analysis. Owing to no linearization, the proposed controller is applicable when state variables (e.g., cargo swing angles) are not close enough to the equilibrium points, which makes it suitable for complicated working conditions. Hardware experimental results are included to verify the effectiveness of the proposed controller. Note to Practitioners —This paper is motivated by the requirement of effective control methods for tower cranes. Tower cranes are widely applied in modern construction sites to fulfill cargo transportation tasks. For such systems, the jib slew motion not only enlarges the workspace but also brings more difficulties to suppress unexpected cargo swing during the transportation process. Up until now, most existing methods use simplified system models or need exact model knowledge, which are difficult to reflect real dynamics in many practical situations. To handle these existing problems, in this paper, a novel feedback control approach embedded with an elaborately constructed integral term is presented without model linearization. By introducing the integral term, even when frictions are inaccurately compensated, steady errors can be reduced effectively and, hence, the positioning accuracy can be improved. Also, the proposed controller can handle parametric uncertainties. By applying the proposed controller, the closed-loop system achieves asymptotic results, which is rigorously proven theoretically. Finally, the effectiveness of the proposed controller is verified by implementing several groups of hardware experiments. In the future studies, we will apply the proposed method in practical applications.

Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers

Abstract: The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks’ delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods. Note to Practitioners —This paper investigates the cost minimization problem for DGCDCs while meeting delay-bound constraints for all arriving tasks. Previous task scheduling methods do not jointly investigate the spatial diversity in bandwidth prices of ISPs, electricity prices, and the availability of renewable green energy. Therefore, they fail to cost-effectively schedule all arriving tasks of heterogeneous applications during their delay-bound constraints. In this paper, a new method that overcomes the shortcomings of the existing methods is proposed. It is obtained by using the proposed SBA that solves a constrained optimization problem. Simulation results demonstrate that compared with two typical scheduling methods, it increases the throughput and decreases the cost. It can be readily implemented and integrated into real-world industrial DGCDCs. The future work needs to investigate the indeterminacy of renewable energy and the uncertainty in arriving tasks with rough deep neural network approaches on STSRO.

Finite-Time PLOS-Based Integral Sliding-Mode Adaptive Neural Path Following for Unmanned Surface Vessels With Unknown Dynamics and Disturbances

Abstract: Unmanned surface vessels (USVs) are supposed to be able to adapt unstructured environments by means of multi-sensor active perception without any human interference, and high-accuracy path following is achieved for USVs by effective control strategies and intelligent devices of e-navigation. This paper proposes a finite-time predictor line-of-sight (LOS)-based integral sliding-mode adaptive neural (FPISAN) scheme for the path following of USVs in the presence of unknown dynamics and external disturbances, which copies with the problem of merging with the kinematic level and the kinetic level of USVs. From the point of view of USVs’ practical engineering, the inertia matrix of USVs maintains nonzero off-diagonal. In order to ensure that USVs can converge to and follow a defined path, a novel LOS-based guidance law that can acquire sideslip angles by error predictors within a finite time is presented, called finite-time predictor-based LOS (FPLOS). Then, the path-following control laws are designed by combining the neural network (NN) technique with the integral sliding-mode method, where radial basis function NN (RBFNN) is applied to approximate lumped unknown dynamics induced by nonparametric uncertainties and external disturbances. The theoretical analysis verifies that the path-following guidance-control system of USVs is semiglobally uniformly ultimately bounded (SGUUB) with the aid of Lyapunov stability theory. The effectiveness and performance of this presented scheme are illustrated by simulation experiments with the comparison. Note to Practitioners —The design of heading guidance laws and path-following control laws for path following of USVs subject to unknown dynamics and external disturbances is a critical problem, which affects the development of USVs. This problem associated with practical engineering of USVs due to the actual navigation environment that is complex, diversified, and highly unstructured. This paper presents a wholly tight strategy to compensate for unknown sideslip angles and approximate lumped unknown dynamics. Hence, an effective scheme being denoted FPISAN mentioned above is developed for path following of USVs.

Flexible Process Planning and End-of-Life Decision-Making for Product Recovery Optimization Based on Hybrid Disassembly

Abstract: With growing environmental and sustainability-related concerns, recovery optimization of mechanical products has been gaining increased exposure. It facilitates environmental sustainability through the improvement in the life-cycle material efficiency and reduction in environmental impact with disassembly sequence planning, component reuse, and material recycling. Traditional product recovery separates end-of-life (EOL) products into components and selects EOL options of components. However, there are many practical cases in which the recovery of a set of subassemblies and components leads to better net revenue than that of a complete set of single components. This paper proposes to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. Finally, a case study is described to illustrate the proposed method and the influence on decision variables of the tradeoff between the recovery profit and environmental impact is discussed. Note to Practitioners —This paper deals with the process planning and EOL decision-making problem of product recovery. Based on hybrid disassembly, this paper proposes a flexible process planning and EOL decision-making method for product recovery. Flexible process planning of hybrid disassembly determines a disassembly level by identifying the reusability of subassemblies and disassembly sequences mixed with subassemblies and components. Optimal EOL decisions for each subassembly and component are investigated such that the economic and environmental objectives can be achieved. The goal of this paper is to model and optimize hybrid disassembly and EOL operations of product recovery to maximize the recovery profit and minimize the environmental impact. The results demonstrate that the proposed method can leads to small environmental impact and low cost. Subassemblies and components with high reliability and expensive price are suggested to be destined for reuse. Minimizing transportation distances is more effective to reduce product recovery cost. Such results can help decision makers to perform better judgments when a disassembly process of an EOL product is executed.

Generalized Linear Quaternion Complementary Filter for Attitude Estimation From Multisensor Observations: An Optimization Approach

Abstract: Focusing on generalized sensor combinations, this paper deals with the attitude estimation problem using a linear complementary filter (CF). The quaternion observation model is obtained via a gradient descent algorithm. An additive measurement model is then established according to derived results. The filter is named as the generalized CF where the observation model is simplified as a linear one that is quite different from previous-reported brute-force nonlinear results. Moreover, we prove that representative derivative-based optimization algorithms are essentially equivalent to each other. Derivations are given to establish the state model based on the quaternion kinematic equation. The proposed algorithm is validated under several experimental conditions involving the free-living environment, harsh external field disturbances, and aerial flight test aided by robotic vision. Using the specially designed experimental devices, data acquisition and algorithm computations are performed to give comparisons on accuracy, robustness, time-consumption, and so on with representative methods. The results show that not only the proposed filter can give fast, accurate, and stable estimates in terms of various sensor combinations but also produces robust attitude estimation in the scenario of harsh situations, e.g., irregular magnetic distortion. Note to Practitioners —Multisensor attitude estimation is a crucial technique in robotic devices. Many existing methods focus on the orientation fusion of specific sensor combinations. In this paper, we make the problem more concise. The results given in this paper are very general and can significantly decrease the space consumption and computation burden without losing the original estimation accuracy. Such performance will be of benefit to robotic platforms requiring flexible and easy-to-tune attitude estimation in the future.

A Fast UHF RFID Localization Method Using Unwrapped Phase-Position Model

Abstract: A novel ultrahigh-frequency (UHF) radio frequency identification (RFID) localization method is proposed in this paper, by which the location of a static passive tag can be easily obtained using a mobile RFID antenna. An unwrapped phase-position model with three parameters is built, and the location of the tag can be pinpointed through an ordinary nonlinear least-squares algorithm. The main advantage of this method is that it is cheap in computation cost compared with the existing grid-based methods. The experimental tests confirm that the proposed method can localize the RFID tags with a competitive computational efficiency and accuracy performance, i.e., millisecond-level computing time and centimeter-level location accuracy. The proposed UHF RFID localization method is well suited to the pervasive location-aware applications, searching RFID-tagged item in the intelligent warehouse by the mobile robot with an onboard RFID system, for example. Note to Practitioners —Ultrahigh-frequency (UHF) radio frequency identification (RFID) has become an efficient booster for the warehouse logistics management due to its low cost, battery-free, and unique identification. The location-aware technology based on RFID is an enabling technology for intelligent warehouse logistics. This paper proposes a passive UHF RFID tag localization method, by which the tags can be easily localized by the use of a mobile RFID system. The experimental results show that the proposed method can localize the static tags with good performance, especially low computation cost. The proposed localization method contributes to the pervasive location-aware application, in which their computation ability is usually limited. For example, mobile robots with the RFID system autonomously seek the RFID-tagged stocks in a warehouse.

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Abstract: Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners —Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product.

Learning Physical Human–Robot Interaction With Coupled Cooperative Primitives for a Lower Exoskeleton

Abstract: Human-powered lower exoskeletons have received considerable interests from both academia and industry over the past decades, and encountered increasing applications in human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical human–robot interaction (pHRI). As a powerful representative tool for motion trajectories, dynamic movement primitives (DMP) have been used to model human movement trajectories. However, canonical DMP only offers a general representation of human movement trajectory and may neglects the interactive term, therefore it cannot be directly applied to lower exoskeletons which need to track human joint trajectories online, because different pilots have different trajectories and even same pilot might change his/her motion during walking. This paper presents a novel coupled cooperative primitive (CCP) strategy, which aims at modeling the motion trajectories online. Besides maintaining canonical motion primitives, we model the interaction term between the pilot and exoskeletons through impedance models, and propose a reinforcement learning method based on policy improvement and path integrals (PI2) to learn the parameters online. Experimental results on both a single degree-of-freedom platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme. Note to Practitioners —This paper was motivated by the problem of lower exoskeleton when it interacts with different pilots. In both military and industrial applications of lower exoskeleton for strength augmentation, a most challenge problem is how to deal with the pHRI caused by different pilots. This paper suggests a new learning-based strategy, which modeled the pilot’s motion with movement primitives and update through the pHRI between the pilot and the lower exoskeleton with online reinforcement learning method. In order to employ the proposed CCP into the real-time application, we also combine the CCP with a hierarchical control framework, and applied on a lower exoskeleton system which we built for strength augmentation application (which named as HUALEX). In the experiments of this paper, we validate the proposed CCP on different pilots with HUALEX system, the proposed CCP also achieve a good performance on the online learning and adaptation of the pilot’s gait. In the future, we will extend this algorithm for adapting complex environment in both industrial and military applications, such as in different terrains, stairs, and slopes scenarios, and so on.

Multimode Process Monitoring Using Variational Bayesian Inference and Canonical Correlation Analysis

Abstract: Industrial processes generally have various operation modes, and fault detection for such processes is important. This paper proposes a method that integrates a variational Bayesian Gaussian mixture model with canonical correlation analysis (VBGMM-CCA) for efficient multimode process monitoring. The proposed VBGMM-CCA method maximizes the advantage of VBGMM in automatic mode identification and the superiority of CCA in local fault detection. First, VBGMM is applied to unlabeled historical process data to determine the number of operation modes and cluster the data in each mode. Second, local CCA models that explore input and output relationships are established. Fault detection residuals are generated in each local CCA model, and monitoring statistics are derived. Finally, a Bayesian inference probability index that integrates monitoring results from all local models is developed to increase the monitoring robustness. The effectiveness of the proposed monitoring scheme is verified through experimental studies on a numerical example and the multiphase batch-fed penicillin fermentation process. Note to Practitioners —Process monitoring is important in guaranteeing process safety and improving product quality. Large amounts of unlabeled process data with multiple operation modes generally exist in industrial processes. Labeling these data is difficult or costly. Hence, this paper presents a VBGMM-CCA method for monitoring multimode processes. The key advantage of the proposed method is that it automatically identifies the number of operation modes in historical data and clusters the data. Then, local CCA monitors are established to model the process input and output relationships. During online monitoring, the running-on operation mode is identified through a density function, and the process status is evaluated by the corresponding CCA monitor. A probabilistic monitoring index is also developed to increase the robustness of the monitoring. In comparison with the results of conventional methods, the monitoring results of the proposed approach are more reliable and informative because the process status and the type of the detected fault are presented.

Integration of Robotic Vision and Tactile Sensing for Wire-Terminal Insertion Tasks

Abstract: This paper reports the development of a manipulation system for electric wires, implemented by means of a commercial gripper installed on an industrial manipulator and equipped with cameras and suitably designed tactile sensors. The purpose of this system is the execution of wire insertion on commercial electromechanical components. The synergy between computer vision and tactile sensing is necessary because, in a real environment, the tight spaces very often prevent the possibility to use the vision system, also when the same task is performed by a human being. A novel technique to speed up the generation of training data sets for convolutional neural networks (CNNs) is proposed. Therefore, this technique is used to train a CNN in order to detect small objects (such as wire terminals). Moreover, aiming to prevent faults during the task and to interact with the environment safely, several machine learning approaches are used to produce an affordable output from the tactile sensor. The proposed approach shows how a cheap sensor embedded with suitable intelligence can provide information comparable to a more expensive force sensor. Note to Practitioners —This paper was motivated by the lack of commercial solution for the automatic cabling of switchgears. Existing approaches to this problem are in some way limited to specific large-scale products or simple layouts. This paper investigated a robust and flexible solution, based on the exploitation of multiple sensors and machine learning algorithms, for wire detection, grasping, and connection. The proposed approach is characterized by simple design and self-tuning capabilities, and it can be easily employed on a wide range of switchgear layouts thanks to the large workspace of the manipulator. Experimental results show that the proposed system is able to achieve a 95% success rate within a realistic admissible region. In the future research, we will integrate the proposed solution with an electromechanical component localization module and a terminal fastening system to evaluate the performance on the real production line.

An Echo State Gaussian Process-Based Nonlinear Model Predictive Control for Pneumatic Muscle Actuators

Abstract: Pneumatic muscle actuators (PMAs), a kind of soft/compliant actuators, have been attracted a great deal of attention in the studies of rehabilitation robots. However, the nonlinearities, uncertainties, hysteresis, and time-varying features of PMAs bring a lot of difficulties in their high-precision trajectory tracking tasks. In this paper, an echo state Gaussian process-based nonlinear model predictive control (ESGP-NMPC) is designed for the PMAs. The proposed strategy is comprised of an ESGP, which is suitable for modeling unknown nonlinear systems as well as measuring their uncertainties, and a gradient descent optimization algorithm for calculating the control signal sequences. Based on the Lyapunov theorem, characteristics of the closed-loop system are analyzed to guarantee the asymptotical stability. Both simulations and physical experiments are carried out to illustrate the validity of the proposed control strategy. Compared with other conventional methods, the ESGP-NMPC can achieve a better model fitting for the PMA and control performance for the high-precision tracking tasks. Note to Practitioners —High-precision control of pneumatic muscle actuators (PMAs) is a vital problem when PMAs are utilized as actuators of rehabilitation robots since the patient’s safety and the performance of rehabilitation tasks are largely dependent on the accuracy of the actuators. Conventional model-based control approaches usually require relatively accurate identification of system parameters, which is difficult for the PMA, owing to its strong nonlinear and time-varying characteristics. This paper proposes a new model predictive control method based on an echo state Gaussian process that can describe the unknown dynamics of a PMA due to its universal approximation property. Through the optimization method, the controller can be efficiently realized and presents better performances than some comparatives. By applying this approach, it is possible to achieve not only high-precision control of PMAs but also a certain degree of robustness to the load.

RSS-Based Indoor Localization Using Belief Function Theory

Abstract: Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners —This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques.

Mobile Phone Data Analysis: A Spatial Exploration Toward Hotspot Detection

Abstract: The percentage of processed large-scale heterogeneous data is exploding and technology is the most obvious reason for the big data issue. Nowadays, the results of data expansion are showing up in different fields. The users’ contextual data are valuable in engineering and business domains, e.g., transportation, location-based services, and advertisement industry. Take mobile phones as an example. There are billions of subscriptions worldwide and sensor devices are digitizing people interactions. The data volume generated by mobile phones and the need to make better, fact-based, and real-time decisions, are the challenges facing researchers. Recently, new technologies based on cloud computing have emerged to process and analyze a large volume of data. We have utilized such technologies for the analysis of call detail records with the collaboration with a telecommunications company. We present an exploratory spatial data analysis algorithm and its analysis results. To prioritize different areas, detecting hotspots in a fast and accurate way is our objective. The findings of this research work can be helpful for urban planning and development as well as telecommunication infrastructure upgrading. Note to Practitioners —This paper proposes novel spatial analysis to detect the spatial distribution of mobile phones. It concerns the correlation of various spatial objects regarding their spatial and nonspatial dimensions to detect dependency among them. We have constructed a weighted neighbor list of spatial objects and implemented different autocorrelation tests regarding the variable of interests to compute spatial dependencies. By applying a Kernel Density method, we have identified the distributions. The results of density estimation demonstrate that the proposed approach is effective and feasible. Such analysis can help organizations better implement monitoring and evaluation plans at all levels and make the necessary infrastructure improvement to satisfy user requirements.

Multilevel Humanlike Motion Planning for Mobile Robots in Complex Indoor Environments

Abstract: In this paper, a multilevel humanlike motion planning approach is proposed for indoor mobile robots. Compared with existing approaches, the novelty of this paper is twofold: 1) the proposed path planning framework is multilevel and humanlike to ensure both foreseeability and flexibility , wherein functions of human brain, eyes, and legs are corresponding to global path planning, sensor-level path planning, and action-level path planning, respectively, and 2) along the planned path, a new velocity-adjustable trajectory planning algorithm is put forward which is provably complete and time optimal considering multiple constraints from both the robot and the environment. Experimental results show that the proposed approach has a better performance in terms of efficiency, smoothness, foreseeability, and flexibility, and autonomous navigation is realized in large-scale, dynamic, partially unknown, and unstructured indoor environments. Note to Practitioners —This paper was motivated by the motion planning problem for mobile robots in complex indoor environments. Existing approaches generally implement a two-level planning scheme, namely, global path planning and local trajectory planning. This paper proposes a novel multilevel humanlike motion planning approach, which is able to guarantee efficiency, smoothness, foreseeability, and flexibility in the presence of kinematic and environmental constraints. Experimental results show that autonomous navigation with the proposed approach is realized in large-scale, dynamic, partially unknown, and unstructured indoor environments, but human motion prediction has not yet been incorporated. In the future research, we will concentrate on human–robot interaction and environment-adaptive planning.

A Survey of Force-Assisted Robotic Cell Microinjection Technologies

Abstract: Cell injection plays an important role in genetics, transgenics, molecular biology, drug discovery, reproductive study, and other biomedical fields. Compared with manual cell microinjection and robotic cell microinjection with sole position feedback, force-assisted robotic cell microinjection can improve the success rate and survival rate of cell injection. In this paper, the state-of-the-art research on microinjection of both adherent cells and suspended cells with microforce sensing techniques is reviewed. The significance of force sensors in the robotic cell injection system is also discussed. Five types of prevalent force sensing methods and their applications in cell microinjection are reviewed. The challenges and promising solutions in automating the cell microinjection process are addressed. Note to Practitioners —Microinjection process is a complex task to perform. As the advance progress of high-performance microforce sensors, force-assisted robotic cell injection has been a hot topic in recent years. This paper presents the state-of-the-art survey of recent developments on microforce sensing for robotic cell microinjection to address the research challenges. Based on microforce sensing and control, microinjection of both adherent and suspended cells can benefit from the force-assisted robotic cell injection process. The main challenges and promising solutions in terms of micromanipulator design, injection control design, cell holder design, penetration scheme design, injecting pipette maintenance, injection volume, cost reduction, and microforce sensor calibration issues have been discussed. The related research trends are summarized.

Risk-DTRRT-Based Optimal Motion Planning Algorithm for Mobile Robots

Abstract: In a human–robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments. Note to Practitioners —This paper is motivated by planning optimized trajectories for the mobile service robots in dynamic environments with pedestrians. In this area, the sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, the real-time optimality of the motion planning cannot be guaranteed due to the challenges caused by the moving pedestrians. In this paper, we propose a dual-tree framework to solve this problem. First, a classic Rapidly exploring Random Tree (RRT) is constructed to generate a heuristic trajectory. Then, instead of reconnecting the nodes on the heuristic trajectory directly, a rewired tree is built to optimize the heuristic trajectory. This proposed dual-tree framework can fully exploit the information of the RRT tree and ensure the completeness of the motion planning. The proposed motion planning algorithm also considers the constraints of the nonholonomic mobile robots, and it can be applied in most mobile service robots to improve their motion planning quality.

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

Abstract: This paper presents a joint trajectory smoothing and tracking framework for a specific class of targets with smooth motion. We model the target trajectory by a continuous function of time (FoT), which leads to a curve fitting approach that finds a trajectory FoT fitting the sensor data in a sliding time-window. A simulation study is conducted to demonstrate the effectiveness of our approach in tracking a maneuvering target, in comparison with the conventional filters and smoothers. Note to Practitioners —Estimation, such as automatically tracking and predicting the movement of an aircraft, a train, or a bus, plays a key role in our daily life. In this paper, we provide a new approach for the online estimation of the target trajectory function by means of fitting the time-series observation, which accommodates the lack of quantifiable knowledge about the target motion and of the statistical property of the sensor observation noise. The resulting trajectory function can be used to infer either the past or the present state of the target. Engineering-friendly strategies are provided for computationally efficient implementation. The proposed approach is particularly appealing to a broad range of real-world targets that move in smooth courses, such as passenger aircraft and ships.