Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.

中文翻译：

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使用改进的自适应粒子群优化的多机器人路径规划

路径规划在多机器人的运动规划和协同导航中具有重要意义。然而，由于其高复杂性和非确定性多项式时间困难性，有效地解决多机器人路径规划问题仍然具有很大的挑战性。为此，本文增强了协同进化机制和改进的粒子群优化（PSO）算法，提出了一种基于协同进化的粒子群优化方法来应对多机器人路径规划问题。试图很好地调整全局和局部搜索能力并解决粒子群优化的停滞问题，所提出的粒子群优化通过进化博弈论增强了广泛使用的标准粒子群优化算法，其中提出了一种新颖的自适应策略来更新粒子的三个主要控制参数。由于粒子群优化的收敛性显着影响其优化效率，本文对所提出的粒子群优化的收敛性进行了分析研究，并提供了一个参数选择规则，充分保证了该粒子群优化的收敛性。通过在单机器人和多机器人路径规划问题中的不同场景验证了所提出的规划方法的性能。数值模拟结果表明，与其竞争者相比，所提出的方法在路径优化方面非常有前途。此外，所提出方法的计算时间与其同行的计算时间相当。