Yang Zhen, Li Junli, Yang Liwei, Wang Qian, Li Ping, Xia Guofeng
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
Math Biosci Eng. 2023 Jan;20(1):145-178. doi: 10.3934/mbe.2023008. Epub 2022 Sep 30.
Multi-robot systems are experiencing increasing popularity in joint rescue, intelligent transportation, and other fields. However, path planning and navigation obstacle avoidance among multiple robots, as well as dynamic environments, raise significant challenges. We propose a distributed multi-mobile robot navigation and obstacle avoidance method in unknown environments. First, we propose a bidirectional alternating jump point search A* algorithm (BAJPSA*) to obtain the robot's global path in the prior environment and further improve the heuristic function to enhance efficiency. We construct a robot kinematic model based on the dynamic window approach (DWA), present an adaptive navigation strategy, and introduce a new path tracking evaluation function that improves path tracking accuracy and optimality. To strengthen the security of obstacle avoidance, we modify the decision rules and obstacle avoidance rules of the single robot and further improve the decision avoidance capability of multi-robot systems. Moreover, the mainstream prioritization method is used to coordinate the local dynamic path planning of our multi-robot systems to resolve collision conflicts, reducing the difficulty of obstacle avoidance and simplifying the algorithm. Experimental results show that this distributed multi-mobile robot motion planning method can provide better navigation and obstacle avoidance strategies in complex dynamic environments, which provides a technical reference in practical situations.
多机器人系统在联合救援、智能交通等领域越来越受欢迎。然而，多个机器人之间的路径规划和导航避障以及动态环境带来了重大挑战。我们提出了一种在未知环境中的分布式多移动机器人导航和避障方法。首先，我们提出了一种双向交替跳点搜索A算法（BAJPSA），以在先前环境中获得机器人的全局路径，并进一步改进启发式函数以提高效率。我们基于动态窗口方法（DWA）构建机器人运动学模型，提出一种自适应导航策略，并引入一种新的路径跟踪评估函数，提高路径跟踪的准确性和最优性。为了加强避障的安全性，我们修改了单个机器人的决策规则和避障规则，并进一步提高了多机器人系统的决策避障能力。此外，采用主流的优先级方法来协调我们多机器人系统的局部动态路径规划，以解决碰撞冲突，降低避障难度并简化算法。实验结果表明，这种分布式多移动机器人运动规划方法能够在复杂动态环境中提供更好的导航和避障策略，为实际情况提供了技术参考。
