Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China.
Faculty of Information Engineering, Xinjiang Institute of Technology, Akesu 843100, China.
Math Biosci Eng. 2023 Jan;20(2):3528-3565. doi: 10.3934/mbe.2023165. Epub 2022 Dec 6.
With the background of limited energy storage of robots and considering the high coupling problem of multi-agent path finding (MAPF), we propose a priority-free ant colony optimization (PFACO) to plan conflict-free and energy-efficient paths, reducing multi-robots motion cost in the rough ground environment. First, a dual-resolution grid map considering obstacles and ground friction factors is designed to model the unstructured rough terrain. Second, an energy-constrained ant colony optimization (ECACO) is proposed to achieve energy-optimal path planning for a single robot, in which we improve the heuristic function based on the combined effects of path length, path smoothness, ground friction coefficient and energy consumption, and consider multiple energy consumption metrics during robot motion to improved pheromone update strategy. Finally, considering multiple collision conflict cases among multiple robots, we incorporate a prioritized conflict-free strategy (PCS) and a route conflict-free strategy (RCS) based on ECACO to achieve MAPF with low-energy and conflict-free in a rough environment. Simulation and experimental results show that ECACO can achieve better energy saving for single robot motion under all three common neighborhood search strategies. PFACO achieves both the conflict-free path and energy-saving planning for robots in complex scenarios, and the study has some reference value for solving practical problems.
针对机器人储能能力有限且多智能体路径规划（MAPF）高度耦合的问题，提出一种无优先级的蚁群优化（PFACO）算法来规划无冲突且节能的路径，降低多机器人在粗糙地面环境中的运动成本。首先，设计了一种考虑障碍物和地面摩擦因素的双分辨率栅格地图来对非结构化的粗糙地形进行建模。其次，提出了一种能量约束蚁群优化（ECACO）算法来实现单个机器人的能量最优路径规划，其中基于路径长度、路径平滑度、地面摩擦系数和能量消耗的综合影响改进启发式函数，并在机器人运动过程中考虑多种能量消耗指标，改进了信息素更新策略。最后，针对多机器人之间的多种碰撞冲突情况，融合了基于 ECACO 的无优先级冲突策略（PCS）和路径无冲突策略（RCS）来实现复杂环境下的低能量无冲突 MAPF。仿真和实验结果表明，在三种常见邻域搜索策略下，ECACO 可以为单个机器人运动实现更好的节能效果。PFACO 可以为机器人在复杂场景下实现无冲突的路径和节能规划，该研究对解决实际问题具有一定的参考价值。
