Multi-robot optimized sampling-base cooperative collision avoidance method in Lidar naviation

Abstract

In multi-robot systems with dynamic and complex environments, robots are required to avoid not only the static objects but also other moving robots. To solve this problem, this paper presents an implementation of cooperative collision avoidance architecture based on optimized sampling-based collision avoidance paradigm. In our work, localization error is considered and bounded in adaptive Monte-Carlo localization process. Plus, we employ velocity obstacle paradigm in predicting collisions. Subsequently, by using Sampling-based planner and optimization theory, we get an optimizing velocity selection policy. Furthermore, we also introduce our distributed multi-robot system model in this paper. By applying the cooperative collision avoidance method in Gazebo self-driving car simulation environment and ROS mobile robots, it is illustrated that our approach is applicable and well-performed.