Showing papers in "IEEE Transactions on Automation Science and Engineering in 2018"

Analysis and Observations From the First Amazon Picking Challenge

Abstract: This paper presents an overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team’s background, mechanism design, perception apparatus, planning, and control approach. We identify trends in this data, correlate it with each team’s success in the competition, and discuss observations and lessons learned based on survey results and the authors’ personal experiences during the challenge. Note to Practitioners —Perception, motion planning, grasping, and robotic system engineering have reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semistructured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.

Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy

Abstract: To maximize task scheduling performance and minimize nonreasonable task allocation in clouds, this paper proposes a method based on a two-stage strategy At the first stage, a job classifier motivated by a Bayes classifier’s design principle is utilized to classify tasks based on historical scheduling data A certain number of virtual machines (VMs) of different types are accordingly created This can save time of creating VMs during task scheduling At the second stage, tasks are matched with concrete VMs dynamically Dynamic task scheduling algorithms are accordingly proposed Experimental results show that they can effectively improve the cloud’s scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods Note to Practitioners — Task scheduling is one of the challenging problems in cloud computing, especially when deadline and cost are considered As an important actuator, virtual machines (VMs) play a vital role for cloud task scheduling To meet task deadlines, one needs to save the time of creating VMs, task waiting time, and executing time To minimize the task execution cost, one needs to schedule tasks onto their most suitable VMs for execution We propose a cloud task scheduling framework based on a two-stage strategy to do so It precreates VMs according to historical scheduling data, therefore saving time for tasks to wait for creating VMs It matches tasks with their most suitable VMs dynamically, therefore saving their execution cost Under the premise of meeting task deadlines, it minimizes the waiting time of VMs to schedule tasks, thus minimizing the cost to be paid by users who utilize VMs The readily deployable algorithms are designed and illustrated to improve cloud task scheduling and execution results in comparison with those using traditional methods

Disassembly Sequence Planning Considering Fuzzy Component Quality and Varying Operational Cost

Abstract: Disassembly planning aims to search the best disassembly sequences of a given obsolete/used product in terms of economic and environmental performances. A practical disassembly process may face great uncertainty owing to various unpredictable factors. To handle it, researchers have addressed the stochastic cost and time problems of product disassembly. In reality, the uncertain environment of product disassembly is associated with both randomness and fuzziness. Besides uncertain disassembly cost and time, the quality of disassembled components/parts in a process has uncertainty and thus needs to be assessed via expert opinions/subjects. To do so, this paper presents a new AND/OR-graph-based disassembly sequence planning problem by considering uncertain component quality and varying disassembly operational cost. Important disassembly planning models are built on the basis of different disassembly criteria. A novel hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony is proposed to solve them. Its effectiveness is well illustrated through several numerical cases and comparison with a prior method, i.e., fuzzy-simulation-based genetic algorithm. Note to Practitioners —This paper deals with the uncertainty management problem of product disassembly. It builds some fuzzy programming models for product disassembly and proposes a hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony to solve them. Previously, such a problem was handled through a methodology based on stochastic planning, which was ineffective without considering the fuzzy characteristic of completing a disassembly task. The goal of this paper is to analyze the disassembly uncertainty feature from the perspective of fuzzy programming. Both theoretical and simulation results demonstrate that the proposed approach is highly effective. The obtained results can help decision-makers better determine a disassembly process of a used/returned/obsolete product.

Interface Design of a Physical Human–Robot Interaction System for Human Impedance Adaptive Skill Transfer

Abstract: It has been established that the transfer of human adaptive impedance is of great significance for physical human–robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor’s arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance. Note to Practitioners —This paper is motivated by the limited performance of skill transfer in the existing human–robot interfaces. Conventional robots perform tasks independently without interaction with humans. However, the new generation of robots with the characteristics, such as flexibility and compliance, become more involved in interacting with humans. Thus, advanced human robot interfaces are required to enable robots to learn human manipulation skills. In this paper, we propose a novel interface for human impedance adaptive skill transfer in a natural and intuitive manner. The developed interface has the following functionalities: 1) it transfers human arm impedance adaptive motion to the robot intuitively; 2) it senses human motion signals that are decoded into human hand gesture and arm endpoint stiffness that ia employed for natural human robot interaction; and 3) it provides human tutor haptic feedback for enhanced teaching experience. The interface can be potentially used in pHRI, teleoperation, human motor training systems, etc.

Nonlinear Antiswing Control for Crane Systems With Double-Pendulum Swing Effects and Uncertain Parameters: Design and Experiments

Abstract: In practical applications, industrial cranes may exhibit double-pendulum swing effects, due to many factors, such as large payload scales and non-negligible hook masses. Currently, for double-pendulum cranes, most available methods are open-loop controllers designed based on linearized crane dynamics; even for existing closed-loop approaches, they are also mostly developed using linearized dynamics and require the exact knowledge of system parameters, which makes them sensitive to parametric uncertainties. To handle these issues, we present an adaptive antiswing control strategy for crane systems with double-pendulum swing effects and uncertain/unknown parameters, which can make the trolley accurately reach the target position with reduced overshoots and effectively eliminate the double-pendulum swing angles at the same time. A complete stability analysis, based upon the full nonlinear dynamics (i.e., without linearizing the dynamics), is included to support the theoretical derivations. We present hardware experimental results to demonstrate that the proposed controller achieves better performance than existing ones and exhibits good robustness. Note to Practitioners —This paper is motivated by the issue of controlling a crane system when double-pendulum swing effects are excited and present. The double-pendulum effects can happen in many practical scenarios and make the crane manual operation very challenging. Moreover, most existing crane control approaches are developed based upon single-pendulum crane models and they may not work normally in the presence of the double-pendulum phenomenon. In addition, usually, the model parameters, including rope length and trolley/hook/payload masses, are not exactly known in practice, which may badly degrade the performance of the control approaches requiring exact model knowledge. Toward this end, we suggest a new control method for cranes suffering from double-pendulum effects to achieve satisfactory performance. The presented control method is robust against parametric uncertainties and it can suppress the double-pendulum swing, reduce the trolley overshoots, and improve the efficiency. Preliminary physical experiments carried out on a double-pendulum crane hardware test bed indicate the effectiveness of the proposed method. In our future work, we will apply the suggested control approach to industrial crane systems to improve their working efficiency.

Dual-Objective Program and Scatter Search for the Optimization of Disassembly Sequences Subject to Multiresource Constraints

Abstract: The effective dismantling of discarded products regardless being used or not is critically important to their reuse, recovery, and recycling. However, the existing product disassembly planning methods pay little or no attention to resource constraints, e.g., limited numbers of disassembly operators and tools. Thus, a resulting plan when being executed may be ineffective in practice. This paper presents a dual-objective optimization model for selective disassembly sequences by considering multiresource constraints such that disassembly profit is maximized and time is minimized. A scatter search is adopted to solve the proposed dual-objective optimization model. It embodies the generation of diverse initial solutions, global assessment of objective functions, a crossover combination operator, a local search strategy for improved solutions, and a reference set update method. To analyze the effect of different weights on its performance, simulations are conducted on different products. Its effectiveness is verified by comparing its optimization results and those of genetic local search. Note to Practitioners —This work deals with a sequence modeling and planning problem of product disassembly. It establishes a novel dual-objective optimization model for product disassembly subject to multiresource constraints. Previously, such a problem is handled through a methodology based on the optimization of a single objective, i.e., disassembly time or cost. The resultant solution is insufficient without fully considering disassembly resources, e.g., labors and tools. Also, in an actual disassembly process, a decision-maker may want to maximize disassembly profit, as well as minimize disassembly time. This work considers both objectives and proposes scatter search to solve disassembly problems. The results demonstrate that the proposed approach can solve them effectively. The obtained solutions give decision makers some desired choices to select a right disassembly process when an actual product is disassembled.

Correctness Guarantees for the Composition of Lane Keeping and Adaptive Cruise Control

Abstract: This paper develops a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control. The safety specifications for these driver assistance modules are expressed in terms of set invariance. Control barrier functions (CBFs) are used to design a family of control solutions that guarantee the forward invariance of a set, which implies satisfaction of the safety specifications. The CBFs are synthesized through a combination of sum-of-squares program and physics-based modeling and optimization. A real-time quadratic program is posed to combine the CBFs with the performance-based controllers, which can be either expressed as control Lyapunov function conditions or as black-box legacy controllers. In both cases, the resulting feedback control guarantees the safety of the composed driver assistance modules in a formally correct manner. Importantly, the quadratic program admits a closed-form solution that can be easily implemented. The effectiveness of the control approach is demonstrated by simulations in the industry-standard vehicle simulator Carsim. Note to Practitioners —Safety is of paramount importance for the control of automated vehicles. This paper is motivated by the problem of designing controllers that are provably correct for the simultaneous operation of two driver assistance modules, lane keeping and adaptive cruise control. This is a challenging problem partially, because the lateral and longitudinal dynamics of the vehicles are coupled, with few results known to exist that provide formal guarantees. In this paper, we employ an assume-guarantee formalism between these two subsystems, such that they can be considered individually; based on that, we use optimization to design safe sets that serves as “supervisors” for vehicle behavior, such that the trajectories of the closed-loop system are confined within the safe sets using predetermined bounds on wheel force and steering angle. The feedback controller is constructed by solving convex quadratic programs online, which can also be given in closed form, making the implementation much easier. One particular advantage of this control approach is that the safety set and the performance controller can be designed separately, which enables the integration of a legacy controller into a correct-by-construction solution.

Planning and Control for Collision-Free Cooperative Aerial Transportation

Abstract: This paper presents planning and control synthesis for multiple aerial manipulators to transport a common object. Each aerial manipulator that consists of a hexacopter and a two-degree-of-freedom robotic arm is controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. We propose a motion planning algorithm by exploiting rapidly exploring random tree star (RRT*) and dynamic movement primitives (DMPs). The desired path for each aerial manipulator is obtained by using RRT* with Bezier curve, which is designed to handle environmental obstacles, such as buildings or equipments. During aerial transportation, to avoid unknown obstacle, DMPs modify the trajectory based on the virtual leader–follower structure. By the combination of RRT* and DMPs, the cooperative aerial manipulators can carry a common object to keep reducing the interaction force between multiple robots while avoiding an obstacle in the unstructured environment. To validate the proposed planning and control synthesis, two experiments with multiple custom-made aerial manipulators are presented, which involve user-guided trajectory and RRT*-planned trajectory tracking in unstructured environments. Note to Practitioners —This paper presents a viable approach to autonomous aerial transportation using multiple aerial manipulators equipped with a multidegree-of-freedom robotic arm. Existing approaches for cooperative manipulation based on force decomposition or impedance-based control often require a heavy or expensive force/torque sensor. However, this paper suggests a method without using a heavy or expensive force/torque sensor based on closed-chain dynamics in joint space and rapidly exploring random tree star (RRT*) that generates the desired trajectory of aerial manipulators. Unlike conventional RRT*, in this paper, our method can also avoid an unknown moving obstacle during aerial transportation by exploiting RRT* and dynamic movement primitives. The proposed planning and control synthesis is tested to demonstrate performance in a lab environment with two custom-made aerial manipulators and a common object.

Optimizing Makespan and Ergonomics in Integrating Collaborative Robots Into Manufacturing Processes

Abstract: As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human–robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process. Note to Practitioners —Collaborative robots promise many advantages on the shop and factory floor, including low-cost automation and flexibility in small-batch production. Using this technology requires engineers to redesign tasks that are currently performed by human workers to effectively involve human and robot workers. However, existing quantitative methods for scheduling and allocating tasks to multiple workers do not consider factors, such as differences in skill between human and robot workers or the differential ergonomic impact of tasks on workers. We propose a method to analyze how the inclusion of a collaborative robot in an existing process might affect the makespan of the task and the physical strain the task places on the human worker. The method enables the engineer to prioritize and weigh makespan and worker ergonomics in creating schedules and inspect the resulting task schedules. Using this method, engineers can determine how the addition of a collaborative robot might improve makespan and/or reduce job risk and potential for occupational hazard for human workers, particularly in tasks that involve high physical strain. We apply our method to six real-world tasks from various industries to demonstrate its use and discuss its practical limitations. In our future work, we plan to develop a software tool that will assist engineers in the use of our method.

Occupancy Detection via Environmental Sensing

Abstract: Sensing by proxy (SbP) is proposed in this paper as a sensing paradigm for occupancy detection, where the inference is based on “proxy” measurements such as temperature and CO2 concentrations. The effects of occupants on indoor environments are captured by constitutive models comprising a coupled partial differential equation–ordinary differential equation system that exploits the spatial and physical features. Sensor fusion of multiple environmental parameters is enabled in the proposed framework. We report on experiments conducted under simulated conditions and real-life circumstances, when the variation of occupancy follows a schedule as the ground truth. The inference of the number of occupants in the room based on CO2 concentration at the air return and air supply vents by our approach achieves an overall mean squared error of 0.6044 (fractional person), while the best alternative by Bayes net is 1.2061 (fractional person). Results from the projected ventilation analysis show that SbP can potentially save 55% of total ventilation compared with the traditional fixed schedule ventilation strategy, while at the same time maintain a reasonably comfort profile for the occupants. Note to Practitioners —Building indoor occupancy is essential to facilitate heating, ventilation, and air conditioning (HVAC) control, lighting adjustment, and geofencing to achieve occupancy comfort and energy efficiency. The significance of this paper is the proposal of a paradigm of sensing that results in a parsimonious and accurate occupancy inference model, which holds considerable potential for energy saving and improvement of HVAC operations. Parameters of the model are data-driven, which exhibit long-term stability and robustness across all the occupants’ experiments. The proposed framework can also be applied to other tasks, such as indoor pollutants source identification, while requiring minimal infrastructure expenses. The data set and algorithm code are available to assist the comparison study.

Motor-Current-Based Estimation of Cartesian Contact Forces and Torques for Robotic Manipulators and Its Application to Force Control

Abstract: We present a Kalman filter-based approach for estimating external forces and torques relying on a dynamic model of a serial-chain robotic manipulator where only motor signals (currents, joint angles, and joint speeds) are measurable. The method does not require any additional sensing compared to standard robot control systems. The approach exploits redundancy in 7DOF arms, but also applies to traditional 6DOF manipulators. Automatic filter calibration routines are presented minimizing the number of parameters that must be tuned in order to successfully apply the proposed scheme and to optimize estimation quality. The approach is verified by measurement data gathered from an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm. Furthermore, measurement results are presented employing force and torque estimates in a compliance control scheme, verifying that the estimation quality achieved is improved compared to existing approaches and is sufficient to employ the estimates in force-controlled applications. Note to Practitioners —More and more robotic applications involve contact with at least partially unknown environments. As a consequence, they require control approaches that go beyond the traditional position control. In particular, information about contact forces and torques has to be taken into account. However, integrating additional sensing equipment to obtain the required force/torque information is often technically challenging and expensive. Cartesian contact force and torque estimation allows obtaining force/torque information solely from available sensors. The estimation technique can be regarded as a virtual sensor, and hence this brief deals with a key technology enabling force controlled robotic applications such as assembly, grinding, and deburring without the need for expensive additional sensing.

Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

Abstract: We propose a learning-based approach for automatic detection of fabric defects Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT) The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics In addition to being compact and fast to compute, these signatures enable accurate localization of defects Our defect detection system is based on three main steps In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics Finally, defects are detected during image inspection by testing local patches using the learned BC Our approach can deal with multiple types of textile fabrics, from simple to more complex ones Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods Note to Practitioners —Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing This paper deals with this problem through a learning-based approach By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images Experiments have shown that our method yields very good performance in terms of defect detection and localization In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation

Distributed Estimation and Control for Leader-Following Formations of Nonholonomic Mobile Robots

Abstract: The problem of the leader-following formation control of nonholonomic mobile robots is addressed in this paper. A distributed formation control strategy using explicitly the coordination errors among robots is proposed without assuming that each follower robot knows the full state of the leader. First, a distributed estimation law is proposed for each follower robot to estimate the states, including the position, orientation, and linear velocity of the leader. The distributed formation control law is then designed based on the estimated states of the leader, and the neighborhood formation tracking error. Under some mild assumptions on the interaction graph among the leader and the follower robots, and the velocity of the leader, asymptotic convergence of formation tracking errors to zero can be achieved. Finally, some numerical simulations and experiments on a group of nonholonomic mobile robots are presented to demonstrate the effectiveness of the proposed strategy. Note to Practitioners —The motivation of this paper is to investigate a practical control strategy for the leader-following formation of multiple autonomous mobile robots subjected to nonholonomic constraints. In most of the existing leader-following formation control schemes for nonholonomic mobile robots, having access to the full state of the leader is a requirement. However, due to limitations in communication bandwidth and range, it is reasonable to assume that the information of the leader is available only to a subset of followers. Hence, this paper suggests a new distributed leader-following formation control strategy based on the distributed estimation of the leader’s states. Moreover, the coordination error between a pair of interacting robots is explicitly used in the control design to weaken the dependence on the estimated state of the leader, and enhance the decentralized nature of the proposed control scheme. The stability and convergence of the system are analyzed mathematically and the experiment using unicycles provides promising results. In ongoing research, we are addressing the issues of collision avoidance and communication delays to provide more realistic setup for the industrial applications of multivehicle systems.

Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human–Robot Interaction

Abstract: Recent emergence of safe, lightweight, and flexible robots has opened a new realm for human–robot collaboration in manufacturing. Utilizing such robots with the new human–robot interaction (HRI) functionality to interact closely and effectively with a human co-worker, we propose a novel framework for integrating HRI factors (both physical and social interactions) into the robot motion controller for human–robot collaborative assembly tasks in a manufacturing hybrid cell. To meet human physical demands in such assembly tasks, an optimal control problem is formulated for physical HRI (pHRI)-based robot motion control to keep pace with human motion progress. We further augment social HRI (sHRI) into the framework by considering a computational model of the human worker’s trust in his/her robot partner as well as robot facial expressions. The human worker’s trust in robot is computed and used as a metric for path selection as well as a constraint in the optimal control problem. Robot facial expression is displayed for providing additional visual feedbacks to the human worker. We evaluate the proposed framework by designing a robotic experimental testbed and conducting a comprehensive study with a human-in-the-loop. Results of this paper show that compared to the manual adjustments of robot velocity, an autonomous controller based on pHRI, pHRI and sHRI with trust, or pHRI and sHRI with trust, and emotion result in 34%, 39%, and 44% decrease in human workload and 21%, 32%, and 60% increase in robot’s usability, respectively. Compared to the manual framework, human trust in robot increases by 38% and 42%, respectively, in the latter two autonomous frameworks. Moreover, the overall efficiency in terms of assembly time remains the same. Note to Practitioners —Conventionally, industrial robots are used to perform repetitive tasks in human-free cages with minimal HRI for safety concerns. Thanks to the new safety and flexibility functions embedded in human-friendly manufacturing robots, humans and robots can now collaborate closely with each other accomplishing the tasks that were previously done by human workers solely. However, existing criteria for designing robot controllers need to be modified by considering the human workers’ demands since the performance of a human worker would vary due to factors such as individual strength, working pattern, and interaction with the robot. To address this problem, we propose a novel framework that integrates HRI factors in controlling the motion of a robot for the collaborative assembly tasks. Within this framework, the speed of robot can be controlled such that its motion progress synchronizes with that of the human during the task to improve pHRI. Moreover, for better sHRI, human trust in robot is calculated and used to select robot path and modify its speed control. Furthermore, we dynamically change the robot facial expression to provide visual feedbacks for performance and safety concerns. The results of the experimental study show that integrating both pHRI and sHRI in the robot controller leads to a significant drop of human perceived workload and considerable increase of robot usability and human trust in robot while the overall efficiency in terms of assembly time remains intact. For practical utilization in assembly plants, sensory devices for tracking the human motion are required. The robot is also required to have built-in safety functions that reduce the impact of possible collisions between the human and the robot. We believe that this paper addresses how the implementation of human-friendly robots in the manufacturing environments can improve HRI and reduce workload.

State-of-the-Art Intelligent Flight Control Systems in Unmanned Aerial Vehicles

Abstract: We discuss state-of-the-art intelligent robotic aircraft with the special focus on evolutionary autopilots for small unmanned aerial vehicles (UAVs). Under the umbrella of adaptive autopilots, we highlight the pros and cons of the most widely implemented intelligent algorithms against the navigational and maneuvering capabilities of small UAVs. We present several cutting-edge applications of bioinspired flight control systems that have the capability of self-learning. We also highlight several research opportunities and challenges associated with each technique. Note to Practitioners —Soft computing methods have been widely implemented in numerous engineering applications. Recent advancements in computational technology have enabled the implementations of intelligent autopilots in real time. This paper aims to discuss many aspects of the developments and implementations of soft computing techniques in aerial robotics with the main focus on its flight control systems.

Lattice Structure Design and Optimization With Additive Manufacturing Constraints

Abstract: Lattice structures with different desired physical properties are promising for a broad spectrum of applications. The availability of additive manufacturing (AM) technology has relaxed the fabricating limitation of lattice structures. However, manufacturing constraints still exist for AM-fabricated lattice structures, which have a significant influence on the printing quality and mechanical properties of lattice struts. In this paper, a design and optimization strategy is proposed for lattice structures with the consideration of manufacturability to ensure desired printing quality. The concept of manufacturable element is used to link the design and manufacturing process. A meta-model is constructed by experiments and the artificial neural network to obtain the manufacturing constraints. Sizes of struts are optimized by a bidirectional evolutionary structural optimization-based algorithm with these manufacturing constraints. An arm of quadcopter is redesigned and optimized to validate the proposed method. Its result shows that optimized heterogeneous lattice structures can improve the stiffness of the model compared to the homogeneous lattice structure and the original design. Both the Von-Mises stress and the maximum displacement are reduced without increasing the weight of designed part. And by considering the manufacturability constraints, the optimized design has been successfully fabricated by the selected additive manufacturing process. Note to Practitioners —Lattice structures might fail to be fabricated by the additive manufacturing technique if the designed model exceeds the processability of the machine. Our approach has the capability of considering the manufacturing constraints in the design and optimization process. We conducted experiments to investigate the manufacturability and proposed a method that can give the domain of the design variables for a selected manufacturing process. And we also designed an algorithm that can optimize the lattice structure inside the domain of design variables. It ensures that the lattice model can be successfully fabricated by the selected process and the performance is dramatically increased compared to the original design. Engineers can use our approach to optimize the lattice structure automatically without knowing the knowledge of optimization and manufacturability.

Fast Linear Quaternion Attitude Estimator Using Vector Observations

Abstract: As a key problem for multisensor attitude determination, Wahba’s problem has been studied for almost 50 years. Different from existing methods, this paper presents a novel linear approach to solve this problem. We name the proposed method the fast linear attitude estimator (FLAE) because it is faster than known representative algorithms. The original Wahba’s problem is extracted to several 1-D equations based on quaternions. They are then investigated with pseudoinverse matrices establishing a linear solution to $n$ -D equations, which are equivalent to the conventional Wahba’s problem. To obtain the attitude quaternion in a robust manner, an eigenvalue-based solution is proposed. Symbolic solutions to the corresponding characteristic polynomial are derived, showing higher computation speed. Simulations are designed and conducted using test cases evaluated by several classical methods, e.g., Shuster’s quaternion estimator, Markley’s singular value decomposition method, Mortari’s second estimator of the optimal quaternion, and some recent representative methods, e.g., Yang’s analytical method and Riemannian manifold method. The results show that FLAE generates attitude estimates as accurate as that of several existing methods, but consumes much less computation time (about 50% of the known fastest algorithm). Also, to verify the feasibility in embedded application, an experiment on the accelerometer–magnetometer combination is carried out where the algorithms are compared via C++ programming language. An extreme case is finally studied, revealing a minor improvement that adds robustness to FLAE, inspired by Cheng et al. Note to Practitioners —Attitude determination using vector observations can be applied in many areas. The most frequently involved are the accelerometer–magnetometer combination and star tracker array. Based on the proposed efficient fast linear attitude estimator algorithm, the time consumption of the sensor fusion can be significantly reduced, saving the execution time for fault detection, fail safe, and so on.

Weakly Paired Multimodal Fusion for Object Recognition

Abstract: The ever-growing development of sensor technology has led to the use of multimodal sensors to develop robotics and automation systems. It is therefore highly expected to develop methodologies capable of integrating information from multimodal sensors with the goal of improving the performance of surveillance, diagnosis, prediction, and so on. However, real multimodal data often suffer from significant weak-pairing characteristics, i.e., the full pairing between data samples may not be known, while pairing of a group of samples from one modality to a group of samples in another modality is known. In this paper, we establish a novel projective dictionary learning framework for weakly paired multimodal data fusion. By introducing a latent pairing matrix, we realize the simultaneous dictionary learning and the pairing matrix estimation, and therefore improve the fusion effect. In addition, the kernelized version and the optimization algorithms are also addressed. Extensive experimental validations on some existing data sets are performed to show the advantages of the proposed method. Note to Practitioners —In many industrial environments, we usually use multiple heterogeneous sensors, which provide multimodal information. Such multimodal data usually lead to two technical challenges. First, different sensors may provide different patterns of data. Second, the full-pairing information between modalities may not be known. In this paper, we develop a unified model to tackle such problems. This model is based on a projective dictionary learning method, which efficiently produces the representation vector for the original data by an explicit form. In addition, the latent pairing relation between samples can be learned automatically and be used to improve the classification performance. Such a method can be flexibly used for multimodal fusion with full-pairing, partial-pairing and weak-pairing cases.

Scheduling Cluster Tools in Semiconductor Manufacturing: Recent Advances and Challenges

Abstract: Cluster tools are automated robotic manufacturing systems containing multiple computer-controlled process modules. They have been increasingly used for wafer fabrication. This paper reviews the modeling and scheduling methods for cluster tools with both nonrevisiting and revisiting processes. For nonrevisiting processes, we focus on the modeling and scheduling problems of cluster tools with different constraints. Then, their solution methods are reviewed and compared. For revisiting processes, this paper first discusses the scheduling problem of some general manufacturing systems with revisiting. Then, the modeling and scheduling methodologies used to solve the scheduling problems of cluster tools with revisiting processes are reviewed. Future research directions and conclusions are finally discussed. Note to Practitioners —Semiconductor manufacturing systems are among the most advanced and complicated manufacturing systems. Their key equipment is highly automated robot-based cluster tools. With wafer residency time constraints, wafer revisiting, activity time variation, chamber cleaning requirements, and failure-prone process modules (PMs), it is very challenging to schedule and control them. This paper surveys their modeling and scheduling methods. Scheduling them requires one to schedule their robot tasks and processing activities simultaneously. Owing to wafer residency time constraints and the lack of buffers among PMs, it is difficult to conduct their optimal scheduling. This paper presents a thorough review of the state-of-the-art research results about modeling and optimal scheduling of clusters tools and indicates the future research directions.

Online Trajectory Planning and Force Control for Automation of Surgical Tasks

Abstract: Automation of surgical tasks is expected to improve the quality of surgery. In this paper, we address two issues that must be resolved for automation of robotic surgery: online trajectory planning and force control under dynamic conditions. By leveraging demonstrations under various conditions, we model the conditional distribution of the trajectories given the task condition. This scheme enables generalization of the trajectories of spatial motion and contact force to new conditions in real time. In addition, we propose a force tracking controller that robustly and stably tracks the planned profile of the contact force by learning the spatial motion and contact force simultaneously. The proposed scheme was tested with bimanual tasks emulating surgical tasks that require online trajectory planning and force tracking control, such as tying knots and cutting soft tissues. Experimental results show that the proposed scheme enables planning of the task trajectory under dynamic conditions in real time. In addition, the performance of the force control schemes was verified in the experiments. Note to Practitioners —This paper addresses the problem of motion planning and control for automation of surgical tasks. In surgical tasks, it is necessary to manipulate objects under conditions where positions or shapes of objects often change during the task. Thus, trajectories for surgical tasks need to be planned and updated according to the change in the conditions in real time. In this paper, we propose a framework for learning both spatial motion and force profile from human experts. The proposed system can plan and update task trajectories in real time and robustly control the contact force under dynamic conditions. On the other hand, generalization of trajectories is limited to the conditions, which are close to the conditions where the demonstrations were performed. In the future work, we will investigate reinforcement learning approaches in order to enable autonomous improvement of the performance.

Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics

Abstract: The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene. Note to Practitioners —This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue.

Design and Testing of a Flexure-Based Constant-Force Stage for Biological Cell Micromanipulation

Abstract: This paper presents the design of a novel flexure-based precision positioning stage with constant output force for biological cell micromanipulation. One uniqueness of the proposed design is that it produces a constant force without using a force controller. Only a motion control is needed to produce a constant output force, which significantly simplifies the system design process. The stage is driven by a piezoelectric actuator through a displacement amplifier. Analytical models of the displacement amplifier and the zero-stiffness structure are established and verified by conducting finite-element analysis simulations. The structure parameters are optimally designed to guarantee the requirement on output force, motion range, and physical size. A prototype stage is fabricated by 3-D printing process and a series of experiments is carried out. Experimental results show that the developed positioning stage delivers a near constant output force with slight fluctuation in the reachable constant-force motion range of $138~\mu \text{m}$ . The applications of the developed constant-force stage in biological cell manipulation have been demonstrated through experimental investigations. Note to Practitioners —A constant-force stage can produce a constant output force without using a force control. It is attractive for biological micromanipulation. This paper presents the design and testing of a novel constant-force flexure stage. The constant force indicates a zero stiffness for the mechanism. The stage mechanism is devised using modified leaf flexure (MLF) to achieve positive-stiffness structure. Bistable beams are used to design negative-stiffness structure by making use of their postbuckling characteristics. Two bistable beams and two MLFs are combined together to construct a zero-stiffness structure. A conventional stage is also fabricated for comparison study. The performance of the proposed constant-force stage has been verified by simulation and experimental studies. Results indicate that the developed stage system has great superiority over conventional one in terms of reducing driving force, increasing motion range, and reducing force fluctuation. Experimental demonstration of bio-micromanipulation has been presented to reveal its potential applications.

Leader–Follower Consensus of Multivehicle Wirelessly Networked Uncertain Systems Subject to Nonlinear Dynamics and Actuator Fault

Abstract: This paper addresses the leader–follower consensus problem of multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault and proposes a class of distributed discontinuous communication protocols based only on the relative states among neighboring vehicles. By introducing a novel fault model for multivehicle wirelessly networked uncertain systems, fault tolerant consensus can be achieved with different fault modes of the actuators. It is proved in the sense of Lyapunov that, if the conditions of dwell time and the intermittent communication rate are satisfied, the leader–follower consensus can be achieved for closed-loop multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault under the topology that frequently but not always contains a spanning tree rooted at the leader. Furthermore, the results are extended to the collision avoidance and formulation control problems. Four examples are presented to demonstrate the effectiveness of the proposed approaches. Note to Practitioners —This paper presents a general mechanism of leader–follower consensus for the discontinuous communication of multivehicle wirelessly networked uncertain systems with nonlinear dynamics and actuator fault. The proposed approach guarantees that the consensus can be achieved for the vehicles with nonlinear dynamics and unhealthy actuators. Specifically, the actuator behaviors include three types: miss, outage, and loss of effectiveness. More importantly, different from the existing works, the problems of leader–follower consensus, collision avoidance, and formulation control of multiple vehicles are solved by the proposed protocols, which further demonstrate the ability to extend the service life of the systems with frequent actuators faults. In addition, the proposed intermittent communication strategy also addresses the limited power consumption and the information interaction capability of each sensor.

Individualized Gait Pattern Generation for Sharing Lower Limb Exoskeleton Robot

Abstract: The development of sharing technology makes it possible for expensive lower limb exoskeleton robots to be extensively employed. However, due to the uniqueness of gait pattern, it is challenging for lower limb exoskeleton robot to adapt to different wearers’ gait patterns. Studies have shown that the gait pattern is affected by many physical factors. This paper proposes an individualized gait pattern generation (IGPG) method for sharing lower limb exoskeleton (SLEX) robot. First, the gait sequences are parameterized to extract gait features. Then, the Gaussian process regression with automatic relevance determination is used to establish the mapping relationships between the body parameters and the gait features, and the weights of each body parameters on gait pattern are also given. The gait features of an unknown subject can be predicted based on the training set. Finally, the individualized gait pattern is reconstructed by autoencoder neural network and scaling process based on predicted gait features. The experimental results show that the gait pattern predicted by IGPG is very similar to the subject’s actual trajectory and has been successfully applied on the SLEX robot. With the help of sharing technology, the training set will be increased, and the prediction accuracy of individualized gait pattern will also be improved. Note to Practitioners —The main purpose of this paper is to solve the gait pattern mismatch problem when different people wear an lower limb exoskeleton robot. The gait patterns are different for each individual, and the main gait-related factors include body parameters and walking speed (WS). Therefore, the suitable gait pattern for the wearer is predicted according to their body parameters and target WS in this paper. The detailed prediction process and a full analysis of experimental results are also given. Finally, the generated gait patterns are successfully verified on the lower limb exoskeleton robot.

Indoor Relocalization in Challenging Environments With Dual-Stream Convolutional Neural Networks

Abstract: This paper presents an indoor relocalization system using a dual-stream convolutional neural network (CNN) with both color images and depth images as the network inputs. Aiming at the pose regression problem, a deep neural network architecture for RGB-D images is introduced, a training method by stages for the dual-stream CNN is presented, different depth image encoding methods are discussed, and a novel encoding method is proposed. By introducing the range information into the network through a dual-stream architecture, we not only improved the relocalization accuracy by about 20% compared with the state-of-the-art deep learning method for pose regression, but also greatly enhanced the system robustness in challenging scenes such as large-scale, dynamic, fast movement, and night-time environments. To the best of our knowledge, this is the first work to solve the indoor relocalization problems based on deep CNNs with RGB-D camera. The method is first evaluated on the Microsoft 7-Scenes data set to show its advantage in accuracy compared with other CNNs. Large-scale indoor relocalization is further presented using our method. The experimental results show that 0.3 m in position and 4° in orientation accuracy could be obtained. Finally, this method is evaluated on challenging indoor data sets collected from motion capture system. The results show that the relocalization performance is hardly affected by dynamic objects, motion blur, or night-time environments. Note to Practitioners —This paper was motivated by the limitations of the existing indoor relocalization technology that is significant for mobile robot navigation. Using this technology, robots can infer where they are in a previously visited place. Previous visual localization methods can hardly be put into wide application for the reason that they have strict requirements for the environments. When faced with challenging scenes such as large-scale environments, dynamic objects, motion blur caused by fast movement, night-time environments, or other appearance changed scenes, most existing methods tend to fail. This paper introduces deep learning into the indoor relocalization problem and uses dual-stream CNN (depth stream and color stream) to realize 6-DOF pose regression in an end-to-end manner. The localization error is about 0.3 m and 4° in a large-scale indoor environments. And what is more important, the proposed system does not lose efficiency in some challenging scenes. The proposed encoding method of depth images can also be adopted in other deep neural networks with RGB-D cameras as the sensor.

Real-Time Manufacturing Machine and System Performance Monitoring Using Internet of Things

Abstract: This paper introduces a framework to assess the performance of manufacturing systems using hybrid simulation in real time. Continuous and discrete variables of different machines are monitored to analyze performance using a virtual environment running synchronous to plant floor equipment as a reference. Data are extracted from machines using industrial Internet of Things solutions. Productivity and reliability of a physical system are compared in real time with data from a hybrid simulation. The simulation uses discrete-event systems to estimate performance metrics at a system level, and continuous dynamics at a machine level to monitor input and output variables. Simulation outputs are used as a reference to detect abnormal conditions based on deviations of real outputs in different stages of the process. This monitoring method is implemented in a fully automated manufacturing system testbed with robots and CNC machines. Machines are integrated on an Ethernet/IP control network using a programmable logic controller to coordinate actions and transfer data. Results demonstrated the capacity to perform real-time monitoring and capture performance errors within confidence intervals. Note to Practitioners —Estimating expected performance of a manufacturing system processing different parts across multiple machines is a complex problem due to the lack of closed-form equations. Existing solutions focus on monitoring stochastic variables such as production or failure rate, or machine dynamics in separate environments often running asynchronous to the real system. This paper addresses the problem of monitoring and assessing the performance of complex manufacturing systems in real time. The proposed framework uses a real-time hybrid simulation of manufacturing at a machine and system level. The hybrid approach is based on a discrete and continuous model of manufacturing equipment integrated to run synchronously with the real plant floor operation. Data from both the virtual and real environments are merged to assess performance. Deviations from expected values represent an error that can trigger a warning signal to production, maintenance, and/or manufacturing personnel at the plant regarding health and productivity of plant operations.

IoT-Enabled Parking Space Sharing and Allocation Mechanisms

Abstract: This paper is among the first proposing an integrated auction and market design method for the parking space sharing and allocation problem. Drivers (agents) who fail to exchange their own parking spaces can then rent them to the platform. The platform receives private parking spaces from agents and manages some public parking spaces. We first develop the urban parking management cloud platform through Internet of Things. Based on this systemic framework, parking spaces are shared among agents via a price-compatible top trading cycles and chains (PC-TTCCs) mechanism and the platform’s parking spaces are reassigned via a one-sided Vickrey–Clarke–Groves (O-VCG) auction. Both the PC-TTCC mechanism with rule e (PC-TTCC [ $e$ ]) and O-VCG auction are effective in terms of strategy-proofness and (allocative or Pareto) efficiency. In the PC-TTCC [ $e$ ] mechanism, the platform’s payment rule used in private parking space sharing is determined based on historical O-VCG auction prices. Our experimental results further show that the proposed mechanism results in system profitability of 20%–30% and ex post budget balance for the platform. Note to Practitioners —This paper was motivated by the problem of constantly climbing parking needs in major cities. This paper suggests a new approach for intelligent parking space sharing, allocation, and pricing from the integrated market design and auction perspective based on Internet of Things/cloud technological architecture. The proposed mechanisms are effective in terms of strategy-proofness and efficiency, leading to remarkable system profitability. Reasonable agents’ cost saving, bidders’ value, and ex post budget balance for the platform can also be guaranteed in a big city with larger population. Several key managerial implications have been gained. First, a public platform should choose the integrated mechanism that realizes higher agents’ cost saving. Second, agents should be encouraged to rent their private parking slots to the platform for reaching more agents’ welfare. Third, the platform should leverage his owned public parking spaces to achieve higher system profitability and agents’ cost saving. Fourth, compared with Vickrey–Clarke–Groves auction, the simpler first-price auction may lead to higher cost saving for agents in some cases even if it cannot realize allocative efficiency and incentive compatibility. Finally, the platform’s profit will increase and the agents’ cost saving will decrease with the percentage of no show. Preliminary simulation experiments suggest that this approach is feasible but it has not yet been incorporated into a prototype system nor verified in real-world applications. Regarding future work, some other factors such as transaction costs, parking uncertainty, and release of traffic congestion can be included in the proposed mechanism. Our integrated price-compatible top trading cycles and chains [ $e$ ] and one-sided Vickrey–Clarke–Groves mechanisms can exploit the allocation and pricing problems in B2B e-commerce logistics, on-demand traffic fleet management, and ridesharing optimization.

A Wavelet-Based Statistical Approach for Monitoring and Diagnosis of Compound Faults With Application to Rolling Bearings

Abstract: This paper proposes a wavelet-based statistical signal detection approach for monitoring and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is decomposed by an orthonormal discrete wavelet transform to obtain its energy dispersions at multiple levels. We investigate the statistical properties of the decomposed signal energy under both the normal and faulty conditions, based on which a generalized likelihood ratio test is developed. An exponentially weighted moving average control chart is then constructed to detect faults at an early stage. Simulation studies and a real case study are conducted to demonstrate the effectiveness of the proposed method. Furthermore, the comparison studies show that the proposed method outperforms the empirical mode decomposition method and Hilbert envelope spectrum analysis method. Note to Practitioners —This paper is motivated by the problem of monitoring and diagnosis of compound faults in rolling bearings at the early stage, which are seldom considered in existing methods. In this paper, we propose a new approach by using statistical signal detection method and wavelet transform to handle the fault signals. This work aims at monitoring vibration signals and diagnosing fault types. Our simulation results show the proposed approach outperforms existing methods, especially at an early stage. Our future work will aim at improving the method’s sensitivity in distinguishing faults similar to each other.

Finite-Control-Set Model Predictive Control for DFIG Wind Turbines

Abstract: This paper presents a time efficient finite-control-set model predictive control (FCS-MPC) scheme for the doubly fed induction generator system. In this scheme, the switching states of the rotor side converter are directly taken as control inputs. This way, the optimized control action can be directly applied to the converter. Compared with the existing FCS-MPC approaches, the salient feature of the proposed scheme is the reduction of the computation time. By introducing a set of augmented decision variables, the original intractable binary quadratic programming problem in FCS-MPC can be analytically transformed to a binary linear programming problem, which can be solved efficiently. By this means, the computation time of the proposed scheme is much less than that of the existing schemes. This reduction in computation time enables FCS-MPC with longer prediction horizons, thus yielding better control performance. Note to Practitioners —This paper was motivated by the problem of improving the control performance for the doubly fed induction generator (DFIG). Because of its advantages, such as high energy efficiency, low acoustic noise, variable speed operation, and reduced converter rating, DFIG has emerged as a promising solution for the wind power generation. However, existing DFIG controllers have limitations, such as difficult to tune the parameters, inefficient to handle constraints, and do not consider the discrete operation of the power converters. To overcome these limitations, this paper proposes a new DFIG control scheme based on finite-control-set model predictive control (FCS-MPC), which has the abilities of online optimal control, explicitly handling constraints, and directly generating the switching signals for the power converters. A computationally efficient algorithm is proposed to reformulate the FCS-MPC problem as a linear program, thereby significantly reducing the computational efforts and rendering the proposed scheme more practical for implementation. Simulation results validate the effectiveness of the proposed control scheme. This scheme can be implemented in the field-programmable gate array, which will be our future work.

Time-Aware Multi-Application Task Scheduling With Guaranteed Delay Constraints in Green Data Center

Abstract: A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed In addition, this paper provides the mathematical modeling of task refusal and service rates In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks Note to Practitioners —This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid Thus, they fail to meet the delay constraints of all admitted tasks In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem Simulation results show that compared with several existing algorithms, it increases both throughput and profit It can be readily incorporated into real-life industrial GDCs The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling