Cooperative, Dynamics-based, And Abstraction-guided Multi-robot Motion Planning (original) (raw)
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Cooperative Multi-Robot Sampling-Based Motion Planning with Dynamics
Proceedings of the International Conference on Automated Planning and Scheduling
This paper develops an effective, cooperative, and probabilistically-complete multi-robot motion planner. The approach takes into account geometric and differential constraints imposed by the obstacles and the robot dynamics by using sampling to expand a motion tree in the composite state space of all the robots. Scalability and efficiency is achieved by using solutions to a simplified problem representation that does not take dynamics into account to guide the motion-tree expansion. The heuristic solutions are obtained by constructing roadmaps over low-dimensional configuration spaces and relying on cooperative multi-agent graph search to effectively find graph routes. Experimental results with second-order vehicle models operating in complex environments, where cooperation among the robots is required to find solutions, demonstrate significant improvements over related work.
IEEE Robotics and Automation Letters, 2019
This paper combines sampling-based motion planning with multi-agent search to efficiently solve challenging multirobot motion-planning problems with dynamics. This idea has shown promise in prior work which developed a centralized approach to expand a motion tree in the composite state space of all the robots along routes obtained by multi-agent search over a discrete abstraction. Still, the centralized expansion imposes a significant bottleneck due to the curse of dimensionality associated with the high-dimensional composite state space. To improve efficiency and scalability, we propose a coordinated expansion of the motion tree along routes obtained by the multiagent search. We first develop a single-robot sampling-based approach to closely follow a given route σ i. The salient aspect of the proposed coordinated expansion is to invoke the route follower one robot at a time, ensuring that robot i follows σi while avoiding not only the obstacles but also robots 1,. .. , i − 1. In the next iteration, the motion tree could be expanded from another state along other routes. This enables the approach to progress rapidly and achieve significant speedups over a centralized approach.
Multi-robot motion planning amidst dynamic obstacle
2011 International Conference on Recent Trends in Information Systems, 2011
This paper provides a modern approach to multi-robot motion planning in a given world map amidst both static and dynamic obstacles. The distributed method for multi-robot motion planning has been realized with particle swarm optimization algorithm. The experimental results show that the variation in the path deviation from optimal trajectory of the mobile robots increases with the increase in the number of dynamic obstacles. But here we have presented an approach which minimizes the probability of collision of the robots with the obstacles.
Survey of Methods Applied in Cooperative Motion Planning of Multiple Robots
Motion Planning for Dynamic Agents [Working Title]
Recent advances in robotics, autonomous systems, and artificial intelligence (AI) enable robots to perform complex tasks such as delivery, surveillance, inspection, rescue, and others. However, they are unable to complete certain tasks independently due to specific restrictions. In the last few years, researchers are keenly interested in deploying multi-robots for such tasks due to their scalability, robustness, and efficiency. Multiple robots; mobile robots, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs); are gaining much momentum and versatility in their operations, whereas cooperative motion planning is a crucial aspect of incorporating these robots into boundless applications. The purpose of this review chapter is to present an insightful look into the problem of cooperative motion planning with its solution and a comprehensive assessment of various path-planning techniques, task-based motion planning techniques, and obs...
ArXiv, 2018
Multi robot systems have the potential to be utilized in a variety of applications. In most of the previous works, the trajectory generation for multi robot systems is implemented in known environments. To overcome that we present an online trajectory optimization algorithm that utilizes communication of robots' current states to account to the other robots while using local object based maps for identifying obstacles. Based upon this data, we predict the trajectory expected to be traversed by the robots and utilize that to avoid collisions by formulating regions of free space that the robot can be without colliding with other robots and obstacles. A trajectory is optimized constraining the robot to remain within this region.The proposed method is tested in simulations on Gazebo using ROS.
Motion planning and coordination of multi-agent systems
International Journal of Computational Vision and Robotics, 2018
Development of a navigation scheme for a group of cooperative mobile robots is attempted in this paper. Potential field method is used to generate collision-free movement of a robot while it encounters other robots as obstacles. Finally, a cooperation scheme is proposed based on human behaviour to optimise motion strategies of robots treating each other as their obstacles. Computer simulations have been conducted for three different cases with four, eight and 12 robots negotiating a common dynamic environment. In each case, 100 different scenarios are considered and travelling times of all the robots are computed separately. The scenes in which the robots meeting collision is treated as a failure scenario and travelling time is not calculated. Performance of cooperation scheme has improved with the increase in a number of robots.
Comparative analysis of collision-free path-planning methods for multi-manipulator systems
Robotica, 2006
Motion planning for manipulators with many degrees of freedom is a complex task. The research in this area has been mostly restricted to static environments. This paper presents a comparative analysis of three reactive on-line path-planning methods for manipulators: the elastic-strip, strategy-based and potential field methods. Both the elastic-strip method [O. Brock and O. Khatib, “Elastic strips: A framework for integrated planning and execution,” Int. Symp. Exp. Robot. 245–254 (1999)] and the potential field method [O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res.5(1), 90–98 (1986)] have been adapted by the authors to the problem at hand related to our multi-manipulator system (MMS) (three manipulators with five degrees of freedom each). Strategy-based method is an original contribution by the authors [M. Mediavilla, J. L. González, J. C. Fraile and J. R. Perán, “Reactive approach to on-line path planning for robot manipulators in ...
A framework for motion planning of cooperative mobile robots
2003
Este artigo aborda o problema de planejamento de trajetórias e controle de grupos de robôs móveis executando tarefas cooperativas. O foco do artigo consiste em mostrar como transformar diversas tarefas robóticas cooperativas em umaúnica tarefa básica e com isso propiciar que algoritmos desenvolvidos para uma tarefa possam ser utilizados na execução de outras. Resultados experimentais com um grupo de robôs móveis equipados com cameras omnidirecionais comoúnico sensor são apresentados e discutidos.
Reactive Deformation Roadmaps: Motion Planning of Multiple Robots in Dynamic Environments
2007
We present a novel algorithm for motion planning of multiple robots amongst dynamic obstacles. Our approach is based on a new roadmap representation that uses deformable links and dynamically retracts to capture the connectivity of the free space. We use Newtonian physics and Hooke's Law to update the position of the milestones and deform the links in response to the motion of other robots and the obstacles. Based on this roadmap representation, we describe our planning algorithms that can compute collision-free paths for tens of robots in complex dynamic environments.
A Real-Time and Fully Distributed Approach to Motion Planning for Multirobot Systems
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019
Motion planning is one of the most critical problems in multirobot systems. The basic target is to generate a collisionfree 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.