Conflict-free and energy-efficient path planning for multi-robots based on priority free ant colony optimization

Abstract

<abstract><p>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.</p></abstract>

Path Planning Technique for Mobile Robots: A Review

“…For example, Xiang et al [36] employed the minimization of path length as their optimization objective. Li et al [37] factored in obstacles and ground friction, thereby translating a multi-criteria path planning problem into the context of low-energy consumption path planning.…”

“…For instance, in the context of industrial automation production lines [47], robots are required to execute tasks safely within extremely short timeframes, making time efficiency a core metric. However, in the realm of autonomous navigation [37], the optimization objective primarily revolves around ensuring that robots accomplish tasks in a shorter time span while conserving energy. To address this variance in emphasis, this paper presents an overview of several prevalent categories of path metrics that have garnered favor among researchers in Table 6.…”