A Real-Time and Fully Distributed Approach to Motion Planning for Multirobot Systems
01 Dec 2019-IEEE Transactions on Systems, Man, and Cybernetics (IEEE)-Vol. 49, Iss: 12, pp 2636-2650
TL;DR: A fully distributed approach to planning trajectories for multirobot systems operating in unstructured and changing environments by combining the model predictive control (MPC) strategy and the incremental sequential convex programming (iSCP) method.
Abstract: Motion planning is one of the most critical problems in multirobot systems. The basic target is to generate a collision-free trajectory for each robot from its initial position to the target position. In this paper, we study the trajectory planning for the multirobot systems operating in unstructured and changing environments. Each robot is equipped with some sensors of limited sensing ranges. We propose a fully distributed approach to planning trajectories for such systems. It combines the model predictive control (MPC) strategy and the incremental sequential convex programming (iSCP) method. The MPC framework is applied to detect the local running environment real-timely with the concept of receding horizon. For each robot, a nonlinear programming is built in its current prediction horizon. To construct its own optimization problem, a robot first needs to communicate with its neighbors to retrieve their current states. Then, the robot predicts the neighbors’ future positions in the current horizon and constructs the problem without waiting for the prediction information from its neighbors. At last, each robot solves its problem independently via the iSCP method such that the robot can move autonomously. The proposed method is polynomial in its computational complexity.
Citations
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TL;DR: This study investigates robust control of multi-robot systems, such that the number of robots affected by the failed ones is minimized, and proposes two distributed robust control algorithms: one for reliable robots and the other for unreliable ones.
33 citations
TL;DR: This paper proposes an approach to the design of a maximally permissive (optimal) controller to prevent vehicles from any collision based on Petri nets (PNs), and well addresses the challenging issues caused by indistinguishable and uncontrollable events.
Abstract: Automated guided vehicles (AGVs) are being extensively used for transportation and distribution of materials due to their high-efficiency. However, the vehicle-collision free problem is challenging since, when modeling these systems, there are indistinguishable and uncontrollable events due to the limited sensors and actuators. This paper proposes an approach to the design of a maximally permissive (optimal) controller to prevent vehicles from any collision based on Petri nets (PNs). For a typical class of AGV systems, a system modeling algorithm is presented using labeled PN, where indistinguishable events are represented by a set of transitions carrying the same label, and an uncontrollable event by an uncontrollable transition. By virtue of the PN model, the collision-free problem is formalized as a conjunction of linear constraints that are converted into admissible ones by an algorithm such that the computational overhead due to uncontrollable events is significantly reduced. In turn, a method is developed to compute the set of consistent markings for an observed sequence of labels that represent signals generated by sensors. Finally, given an observed sequence, a maximally permissive control action is computed to enforce a conjunction of admissible linear constraints based on the set of consistent markings. The approach well addresses the challenging issues caused by indistinguishable and uncontrollable events. A typical AGV system is utilized to illustrate and verify the theoretical results throughout the work.
31 citations
TL;DR: In this article, a variational Bayesian Gaussian mixture model (vBGMM) framework is employed to predict the future trajectory of moving obstacles, and then a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of the uncertainty within a prediction horizon.
Abstract: Continued great efforts have been dedicated toward high-quality trajectory generation based on optimization methods; however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and incorporate the presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained and, thus, a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently, a nonlinear model predictive control problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and, thus, based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.
30 citations
TL;DR: This paper proposes a robust control algorithm in the paradigm of systems of sequential systems with shared resources, which can acquire and release resources in a multitype and multiquantity way, and is validated to be a polynomially complex robust control algorithms by the distributivity analysis.
Abstract: Up to now, the supervision and control of deadlock-free resource allocation has received considerable attention, particularly regarding their deadlock problems. To date, most solutions have supposed that allocated resources never fail. However, this is quite the opposite in reality since some resources may fail unexpectedly. A robust system should be resilient to such failures. In this paper, resources are divided into reliable ones and unreliable ones. On the basis of the deadlock avoidance algorithm which is proposed for the problem of deadlocks, we propose a robust control algorithm in the paradigm of systems of sequential systems with shared resources, which can acquire and release resources in a multitype and multiquantity way. It is validated to be a polynomially complex robust control algorithm by the distributivity analysis. Finally, experimental results show that the proposed approaches are effective as well as efficient in response to resource failures.
28 citations
TL;DR: This paper investigates the properties of higher-order deadlocks and proposes a distributed approach to their avoidance in multi-robot systems where each robot has a predetermined and closed path to execute persistent motion.
23 citations
References
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01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.
6,340 citations
"A Real-Time and Fully Distributed A..." refers background in this paper
...For the centralized form, all robots determine the decision variables simultaneously [20], [21]; while for the decentralized form, the robots determine their decision variables in a sequential manner since the latter one needs some information computed by the previous robots [9], [26]....
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...Many approximate approaches to generating safe trajectories for robots have been developed in the past few decades [11], [20], [21]....
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Book•
01 Jan 1990
TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Abstract: 1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.
6,186 citations
"A Real-Time and Fully Distributed A..." refers background in this paper
...For the centralized form, all robots determine the decision variables simultaneously [20], [21]; while for the decentralized form, the robots determine their decision variables in a sequential manner since the latter one needs some information computed by the previous robots [9], [26]....
[...]
...Many approximate approaches to generating safe trajectories for robots have been developed in the past few decades [11], [20], [21]....
[...]
Book•
20 May 2005
TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
Abstract: A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.
1,811 citations
TL;DR: This paper presents a method for robot motion planning in dynamic environments that consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the rental positions and velocities of the robot and obstacles.
Abstract: This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the cur rent positions and velocities of the robot and obstacles. It is a first- order method, since it does not integrate velocities to yield positions as functions of time.The avoidance maneuvers are generated by selecting robot ve locities outside of the velocity obstacles, which represent the set of robot velocities that would result in a collision with a given obstacle that moves at a given velocity, at some future time. To ensure that the avoidance maneuver is dynamically feasible, the set of avoidance velocities is intersected with the set of admissible velocities, defined by the robot's acceleration constraints. Computing new avoidance maneuvers at regular time intervals accounts for general obstacle trajectories.The trajectory from start to goal is computed by searching a tree of feasible avoidance maneuve...
1,555 citations
Additional excerpts
...collision avoidance [2], [13], [37], and mathematical programming [1], [3], [9], [12], [26], [29]....
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