ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

International audienceIn this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches

Towards a Probabilistic Roadmap for Multi-robot Coordination

International audienceIn this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches

A Survey and Analysis of Multi-Robot Coordination

International audienceIn the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper

Sampling-based Multi-robot Motion Planning

International audienceThis paper describes a sampling-based approach to multi-robot motion planning. The proposed approach is centralized, which aims to reduce interference between mobile robots such as collision, congestion and deadlock, by increasing the number of waypoints. The implementation based on occupancy grid map is decomposed into three steps: the first step is to identify primary waypoints by using the Voronoi diagram, the second step is to generate additional waypoints by sampling the Voronoi diagram, and the last step is to assign the waypoints to robots by using the Hungarian method. The approach has been implemented and tested in simulation and the experimental results show a good system performance for multi-robot motion planning

Path Planning and Evolutionary Optimization of Wheeled Robots

Probabilistic roadmap methods (PRM) have been a well-known solution for solving motion planning problems where we have a fixed set of start and goal configurations in a workspace. We define a configuration space with static obstacles. We implement PRM to find a feasible path between start and goal for car-like robots. We further extend the concept of path planning by incorporating evolutionary optimization algorithms to tune the PRM parameters. The theory is demonstrated with simulations and experiments. Our results show that there is a significant improvement in the performance metrics of PRM after optimizing the PRM parameters using biogeography-based optimization, which is an evolutionary optimization algorithm. The performance metrics (namely path length, number of hops, number of loops and fail-rate) show 34.91 , 23.18 , 52.21 and 21.21 improvement after using optimized PRM parameters. We also experimentally demonstrate the application of path planning using PRM to mobile car-like robot

Path Planning and Evolutionary Optimization of Wheeled Robots

Probabilistic roadmap methods (PRM) have been a well-known solution for solving motion planning problems where we have a fixed set of start and goal configurations in a workspace. We define a configuration space with static obstacles. We implement PRM to find a feasible path between start and goal for car-like robots. We further extend the concept of path planning by incorporating evolutionary optimization algorithms to tune the PRM parameters. The theory is demonstrated with simulations and experiments. Our results show that there is a significant improvement in the performance metrics of PRM after optimizing the PRM parameters using biogeography-based optimization, which is an evolutionary optimization algorithm. The performance metrics (namely path length, number of hops, number of loops and fail-rate) show 34.91 , 23.18 , 52.21 and 21.21 improvement after using optimized PRM parameters. We also experimentally demonstrate the application of path planning using PRM to mobile car-like robot