Showing papers on "Mobile robot published in 2020"
TL;DR: A mobile robot autonomously operates analytical instruments in a wet chemistry laboratory, performing a photocatalyst optimization task much faster than a human would be able to.
Abstract: Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1–5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6–14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water15. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm16–18. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous19,20 free-roaming robot21–24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis. A mobile robot autonomously operates analytical instruments in a wet chemistry laboratory, performing a photocatalyst optimization task much faster than a human would be able to.
540 citations
TL;DR: A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented and give good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.
Abstract: Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.
187 citations
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TL;DR: A modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category and outperforms a wide range of baselines including end-to-end learning-based methods as well as modular map- based methods.
Abstract: This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. End-to-end learning-based navigation methods struggle at this task as they are ineffective at exploration and long-term planning. We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category. Empirical results in visually realistic simulation environments show that the proposed model outperforms a wide range of baselines including end-to-end learning-based methods as well as modular map-based methods and led to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation analysis indicates that the proposed model learns semantic priors of the relative arrangement of objects in a scene, and uses them to explore efficiently. Domain-agnostic module design allow us to transfer our model to a mobile robot platform and achieve similar performance for object goal navigation in the real-world.
166 citations
TL;DR: A novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods and enables the control policy to learn from human demonstration data and thus improve the learning speed and performance.
Abstract: Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.
164 citations
24 Sep 2020
TL;DR: This work introduces a globally guided reinforcement learning approach (G2RL), which incorporates a novel reward structure that generalizes to arbitrary environments and applies G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner.
Abstract: Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information. Different from existing learning-based methods, we introduce a globally guided reinforcement learning approach ( G2RL ), which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state-of-the-art benchmarks.
113 citations
TL;DR: Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios.
Abstract: Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called modified aging ant colony optimization (AACO). The AACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.
109 citations
TL;DR: The swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy and showed that the architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.
Abstract: Recently, the human-like behavior on anthropomorphic robot manipulators are increasingly accomplished by the kinematic model estabilshing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced techniques in data science facilitate the imitation learning process in anthropomorphic robotics. However, the enormous data set causes the labeling and prediction burden. In this paper, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network (IN-DCNN) is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then to evolve its hierarchical representation of features. The incremental learning process is capable of fine-tuning the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). The anthropomorphic kinematic structure based redundant robots can be held by this approach. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.
106 citations
TL;DR: This paper designs a UAV-UGV team that integrates two custom-built mobile robots that is capable of autonomous navigation, and the performance of the system demonstrates the feasibility of developing and deploying a robust and automated data collection system for construction applications in the near future.
95 citations
TL;DR: The results show that the TLFMRF can identify emotions in a stable manner, and application results indicate that mobile robot can real-time track six basic emotions, including angry, fear, happy, neutral, sad, and surprise.
93 citations
TL;DR: Simulations have been carried out that indicated that this method generates a feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods.
91 citations
12 Dec 2020
TL;DR: A hybrid shared control approach based on EMG and artificial potential field is exploited to avoid obstacles according to the repulsive force and attractive force and to enhance the human perception of the remote environment based on force feedback of the mobile platform.
Abstract: Mobile robots can complete a task in cooperation with a human partner. In this letter, a hybrid shared control method for a mobile robot with omnidirectional wheels is proposed. A human partner utilizes a six degrees of freedom haptic device and electromyography (EMG) signals sensor to control the mobile robot. A hybrid shared control approach based on EMG and artificial potential field is exploited to avoid obstacles according to the repulsive force and attractive force and to enhance the human perception of the remote environment based on force feedback of the mobile platform. This shared control method enables the human partner to tele-control the mobile robot's motion and achieve obstacles avoidance synchronously. Compared with conventional shared control methods, this proposed one provides a force feedback based on muscle activation and drives the human partners to update their control intention with predictability. Experimental results demonstrate the enhanced performance of the mobile robots in comparison with the methods in the literature.
TL;DR: Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems.
TL;DR: The results show that the proposed hybrid path planning method, used for global path planning, tracking and obstacles avoidance, can meet the application needs of mobile robots in complex dynamic environments.
Abstract: When mobile robot used in large-scale dynamic environments, it face more challenging problems in real-time path planning and collision-free path tracking. This paper presents a new hybrid path planning method that combines A* algorithm with adaptive window approach to conduct global path planning, real-time tracking and obstacles avoidance for mobile robot in large-scale dynamic environments. Firstly, a safe A* algorithm is designed to simplify the calculation of risk cost function and distance cost. Secondly, key path points are extracted from the planned path which generated by the safe A* to reduce the number of the grid nodes for smooth path tracking. Finally, the real-time motion planning based on adaptive window approach is adopted to achieve the simultaneous path tracking and obstacle avoidance (SPTaOA) together the switching of the key path points. The simulation and practical experiments are conducted to verify the feasibility and performance of the proposed method. The results show that the proposed hybrid path planning method, used for global path planning, tracking and obstacles avoidance, can meet the application needs of mobile robots in complex dynamic environments.
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TL;DR: It is shown that RGB-D segmentation is superior to processing RGB images solely and that it can still be performed in real time if the network architecture is carefully designed and that the approach is suitable for other areas of application as well.
Abstract: Analyzing scenes thoroughly is crucial for mobile robots acting in different environments. Semantic segmentation can enhance various subsequent tasks, such as (semantically assisted) person perception, (semantic) free space detection, (semantic) mapping, and (semantic) navigation. In this paper, we propose an efficient and robust RGB-D segmentation approach that can be optimized to a high degree using NVIDIA TensorRT and, thus, is well suited as a common initial processing step in a complex system for scene analysis on mobile robots. We show that RGB-D segmentation is superior to processing RGB images solely and that it can still be performed in real time if the network architecture is carefully designed. We evaluate our proposed Efficient Scene Analysis Network (ESANet) on the common indoor datasets NYUv2 and SUNRGB-D and show that we reach state-of-the-art performance while enabling faster inference. Furthermore, our evaluation on the outdoor dataset Cityscapes shows that our approach is suitable for other areas of application as well. Finally, instead of presenting benchmark results only, we also show qualitative results in one of our indoor application scenarios.
TL;DR: A novel protocol is designed such that the estimated leader state is tracked in a fixed time and the obtained upper bound of settling time is explicitly linked with a single parameter in the protocol, which facilitates the adjustment of the bound under different performance requirements.
Abstract: This article deals with the problem of leader-following consensus for multiple wheeled mobile robots. Under a directed graph, a distributed observer is proposed for each follower to estimate the leader state in a fixed time. Based on the observer and a constructed nonlinear manifold, a novel protocol is designed such that the estimated leader state is tracked in a fixed time. Moreover, a switching protocol together with a linear manifold is proposed to ensure that fixed-time leader-following consensus is realized for any initial conditions without causing singularity issues. In contrast to alternative fixed-time consensus protocols in some existing results, the protocol proposed in this article is designed by constructing the nonlinear or linear manifold, which builds a new framework for fixed-time leader-following consensus. Furthermore, the obtained upper bound of settling time is explicitly linked with a single parameter in the protocol, which facilitates the adjustment of the bound under different performance requirements. Finally, the proposed protocol is applied to formation control of wheeled mobile robots.
TL;DR: An indoor robot VLP localization system based on Robot Operating System (ROS) is presented for the first time, aiming at promoting the application of VLP in mature robotic system.
Abstract: Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demand for service-based indoor positioning. Meanwhile, high accuracy localization is very important for mobile robots in various scenes including industrial, domestic and public transportation workspace. In this paper, an indoor robot VLP localization system based on Robot Operating System (ROS) is presented for the first time, aiming at promoting the application of VLP in mature robotic system. On the basis of our previous researches, we innovatively designed a VLP localization package which contains the basic operation control of the robots, the features extraction and recognition of the LED-ID, cm-level positioning, and robust dynamic tracking algorithms. This package exploited the proposed lightweight algorithm, distributed framework design, the loose coupling characteristics of the ROS, and the message communication methods among different nodes. What's more, an efficient LED-ID detection scheme is proposed to ensure the lightweight and accuracy of the positioning. A prototype system has been implemented on a Turtlebot3 Robot 1 1 Experiment Demonstration is available at: https://kwanwaipang.github.io/Image/ROS.mp4 . . Experimental results show that the proposed system can provide robot indoor positioning accuracy within 1 cm and an average computational time of only 0.08 s.
TL;DR: A simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP) is proposed and it is shown that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning.
Abstract: Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The key problem is in the design of robust learning algorithms aimed at building a "living computer" on the basis of SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike timing dependent plasticity (STDP). The SNN implements associative learning by exploring spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays the fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures. They also admit the use of memristive devices for hardware implementation.
TL;DR: An end-to-end navigation planner that translates sparse laser ranging results into movement actions and achieves map-less navigation in complex environments through a reward signal that is enhanced by intrinsic motivation, the agent explores more efficiently, and the learned strategy is more reliable.
Abstract: In this article, we develop a navigation strategy based on deep reinforcement learning (DRL) for mobile robots. Because of the large difference between simulation and reality, most of the trained DRL models cannot be directly migrated into real robots. Moreover, how to explore in a sparsely rewarded environment is also a long-standing problem of DRL. This article proposes an end-to-end navigation planner that translates sparse laser ranging results into movement actions. Using this highly abstract data as input, agents trained by simulation can be extended to the real scene for practical application. For map-less navigation across obstacles and traps, it is difficult to reach the target via random exploration. Curiosity is used to encourage agents to explore the state of an environment that has not been visited and as an additional reward for exploring behavior. The agent relies on the self-supervised model to predict the next state, based on the current state and the executed action. The prediction error is used as a measure of curiosity. The experimental results demonstrate that without any manual design features and previous demonstrations, the proposed method accomplishes map-less navigation in complex environments. Through a reward signal that is enhanced by intrinsic motivation, the agent explores more efficiently, and the learned strategy is more reliable.
TL;DR: Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks, and complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities.
Abstract: Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as having collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful on-board computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities.
TL;DR: This survey paper comprehensively reviews the state-of-the-art development of collaborative robotic manipulation from the perspective of modelling, control and optimization.
19 Aug 2020
TL;DR: The design and physical realization of RoBeetle is the result of combining the notion of controllable NiTi-Pt–based catalytic artificial micromuscle with that of integrated millimeter-scale mechanical control mechanism (MCM).
Abstract: The creation of autonomous subgram microrobots capable of complex behaviors remains a grand challenge in robotics largely due to the lack of microactuators with high work densities and capable of using power sources with specific energies comparable to that of animal fat (38 megajoules per kilogram). Presently, the vast majority of microrobots are driven by electrically powered actuators; consequently, because of the low specific energies of batteries at small scales (below 1.8 megajoules per kilogram), almost all the subgram mobile robots capable of sustained operation remain tethered to external power sources through cables or electromagnetic fields. Here, we present RoBeetle, an 88-milligram insect-sized autonomous crawling robot powered by the catalytic combustion of methanol, a fuel with high specific energy (20 megajoules per kilogram). The design and physical realization of RoBeetle is the result of combining the notion of controllable NiTi-Pt-based catalytic artificial micromuscle with that of integrated millimeter-scale mechanical control mechanism (MCM). Through tethered experiments on several robotic prototypes and system characterization of the thermomechanical properties of their driving artificial muscles, we obtained the design parameters for the MCM that enabled RoBeetle to achieve autonomous crawling. To evaluate the functionality and performance of the robot, we conducted a series of locomotion tests: crawling under two different atmospheric conditions and on surfaces with different levels of roughness, climbing of inclines with different slopes, transportation of payloads, and outdoor locomotion.
TL;DR: Experimental results show that the incremental training mode can notably improve the development efficiency and the PRM+TD3 path planner can effectively improve the generalization of the model.
Abstract: This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight 2D environment. Then, we design the algorithm based on DRL, including observation states, reward function, network structure as well as parameters optimization, in a 2D environment to circumvent the time-consuming works for a 3D environment. We transfer the designed algorithm to a simple 3D environment for retraining to obtain the converged network parameters, including the weights and biases of deep neural network (DNN), etc. Using these parameters as initial values, we continue to train the model in a complex 3D environment. To improve the generalization of the model in different scenes, we propose to combine the DRL algorithm Twin Delayed Deep Deterministic policy gradients (TD3) with the traditional global path planning algorithm Probabilistic Roadmap (PRM) as a novel path planner (PRM+TD3). Experimental results show that the incremental training mode can notably improve the development efficiency. Moreover, the PRM+TD3 path planner can effectively improve the generalization of the model.
TL;DR: The proposed intelligent control system based on a deep reinforcement learning approach for self-adaptive multiple PID controllers for mobile robots demonstrated that it can be of aid by providing with behavior that can compensate or even adapt to changes in the uncertain environments providing a model free unsupervised solution.
Abstract: Intelligent control systems are being developed for the control of plants with complex dynamics. However, the simplicity of the PID (proportional-integrative-derivative) controller makes it still widely used in industrial applications and robotics. This paper proposes an intelligent control system based on a deep reinforcement learning approach for self-adaptive multiple PID controllers for mobile robots. The proposed hybrid control strategy uses an actor-critic structure and it only receives low-level dynamic information as input and simultaneously estimates the multiple parameters or gains of the PID controllers. The proposed approach was tested in several simulated environments and in a real time robotic platform showing the feasibility of the approach for the low-level control of mobile robots. From the simulation and experimental results, our proposed approach demonstrated that it can be of aid by providing with behavior that can compensate or even adapt to changes in the uncertain environments providing a model free unsupervised solution. Also, a comparative study against other adaptive methods for multiple PIDs tuning is presented, showing a successful performance of the approach.
24 Oct 2020
TL;DR: In this paper, a combined imitation learning and deep reinforcement learning approach is proposed for motion planning in crowded and cluttered environments, which can learn motion patterns that are tailored to real-world environments.
Abstract: Mobile robots operating in public environments require the ability to navigate among humans and other obstacles in a socially compliant and safe manner. This work presents a combined imitation learning and deep reinforcement learning approach for motion planning in such crowded and cluttered environments. By separately processing information related to static and dynamic objects, we enable our network to learn motion patterns that are tailored to real-world environments. Our model is also designed such that it can handle usual cases in which robots can be equipped with sensor suites that only offer limited field of view. Our model outperforms current state-of-the-art approaches, which is shown in simulated environments containing human-like agents and static obstacles. Additionally, we demonstrate the real-time performance and applicability of our model by successfully navigating a robotic platform through real-world environments.
TL;DR: The extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances, and the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed.
Abstract: This paper studies the extended state observer (ESO)-based distributed model predictive control (DMPC) approach to deal with multiple mobile robot formation with unknown disturbances. The distributed control problem with path parameters synchronization and disturbance rejection is formulated for formation system according to the tracking error dynamic model, where the reference paths are parameterized. A local distributed controller is designed by using DMPC strategy for each mobile robot in the absence of disturbance by including parameter synchronization constraints in the quadratic performance index as coupling terms. The DMPC optimization problem is solved by using Nash-optimization iteration strategy with the maximum number of iteration constraint. To improve the ability of anti-jamming, a feedforward compensation controller is designed by using ESO method, where the ESO is designed by pole assignment. The convergence of the proposed iterative algorithm is given. Furthermore, the input-to-state stability property of the proposed composite controller, combining a feedforward compensation controller and local distributed controller, is analyzed for the closed-loop system. Finally, the validity of the proposed algorithm is verified by two simulation examples.
10 Feb 2020
TL;DR: In this paper, a graph convolutional network based on human gaze data is trained to predict human attention to different agents in the crowd as they perform a navigation task based on a top-down view of the environment.
Abstract: Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd as they perform a navigation task based on a top down view of the environment. We incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-of-art methods, increasing task completion rate by 18.4% and decreasing navigation time by 16.4%.
TL;DR: This article presents an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task, based on a model predictive control framework.
Abstract: In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.
TL;DR: An elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic trajectory.
Abstract: In a human–robot coexisting environment, it is pivotal for a mobile service robot to arrive at the goal position safely and efficiently. In this article, an elastic band-based rapidly exploring random tree (EB-RRT) algorithm is proposed to achieve real-time optimal motion planning for the mobile robot in the dynamic environment, which can maintain a homotopy optimal trajectory based on current heuristic trajectory. Inspired by the EB method, we propose a hierarchical framework consisting of two planners. In the global planner, a time-based RRT algorithm is used to generate a feasible heuristic trajectory for a specific task in the dynamic environment. However, this heuristic trajectory is nonoptimal. In the dynamic replanner, the time-based nodes on the heuristic trajectory are updated due to the internal contraction force and the repulsive force from the obstacles. In this way, the heuristic trajectory is optimized continuously, and the final trajectory can be proved to be optimal in the homotopy class of the heuristic trajectory. Simulation experiments reveal that compared with two state-of-the-art algorithms, our proposed method can achieve better performance in dynamic environments. Note to Practitioners —The motivation of this work stems from the need to achieve real-time optimal motion planning for the mobile robot in the human–robot coexisting environment. Sampling-based algorithms are widely used in this area due to their good scalability and high efficiency. However, the generated trajectory is usually far from optimal. To obtain an optimized trajectory for the mobile robot in the dynamic environment with moving pedestrians, we propose the EB-RRT algorithm on the basis of the time-based RRT tree and the EB method. Depending on the time-based RRT tree, we quickly get a heuristic trajectory and guarantee the probabilistic completeness of our algorithm. Then, we optimize the heuristic trajectory similar to the EB method, which achieves the homotopy optimality of the final trajectory. We also take into account the nonholonomic constraints, and our proposed algorithm can be applied to most mobile robots to further improve their motion planning ability and the trajectory quality.
TL;DR: In this paper, the authors present the Oxford Inertial Odometry Data Set (OxIOD), a first-of-its-kind public data set for deep learning-based inertial navigation research with fine-grained ground truth on all sequences.
Abstract: Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet of Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this article, we present and release the Oxford Inertial Odometry Data Set (OxIOD), a first-of-its-kind public data set for deep-learning-based inertial navigation research with fine-grained ground truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our data set and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.
Proceedings Article•
01 Jul 2020TL;DR: In this paper, a modular system called "Goal-Oriented Semantic Exploration" is proposed, which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category.
Abstract: This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. End-to-end learning-based navigation methods struggle at this task as they are ineffective at exploration and long-term planning. We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category. Empirical results in visually realistic simulation environments show that the proposed model outperforms a wide range of baselines including end-to-end learning-based methods as well as modular map-based methods and led to the winning entry of the CVPR-2020 Habitat ObjectNav Challenge. Ablation analysis indicates that the proposed model learns semantic priors of the relative arrangement of objects in a scene, and uses them to explore efficiently. Domain-agnostic module design allow us to transfer our model to a mobile robot platform and achieve similar performance for object goal navigation in the real-world.