Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles
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DOI：
10.1016/j.eswa.2015.02.033
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发表时间：
2015-07-15
影响因子：
8.5
通讯作者：
Sepulveda, Roberto
中科院分区：
文献类型：
--
作者：
Montiel, Oscar;Orozco-Rosas, Ulises;Sepulveda, Roberto
在本文中，使用一种新的路径规划方法计算移动机器人（MR）在有静态和动态障碍物的环境中的最佳路径。所提出的称为细菌势场（BPF）的方法确保了可行、最优和安全的路径。该新颖的提议利用人工势场（APF）方法和细菌进化算法（BEA）来获得增强的灵活路径规划器方法，该方法充分利用了APF方法的所有优点，大大减少了其缺点。实现了BPF方法与经典APF方法、伪细菌势场（PBPF）方法、遗传势场（GPF）方法的顺序和并行实施的对比实验，这些方法都基于进化计算来优化APF参数。设计了一个使用 MR 现实模型的仿真平台来测试路径规划算法。总的来说，BPF 的性能优于 APF、GPF 和 PBPF 方法，因为它减少了寻找最佳路径的计算时间至少 1.59 倍。这些结果对BPF路径规划方法在动态复杂环境中满足局部和全局可控性的能力产生积极影响，避免与干扰MR导航的物体发生碰撞。 (C) 2015 Elsevier Ltd. 保留所有权利。
In this paper, optimal paths in environments with static and dynamic obstacles for a mobile robot (MR) are computed using a new method for path planning. The proposed method called Bacterial Potential Field (BPF) ensures a feasible, optimal and safe path. This novel proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method taking all the advantages of using the APF method, strongly reducing its disadvantages. Comparative experiments for sequential and parallel implementations of the BPF method against the classic APF method, as well as with the Pseudo-Bacterial Potential Field (PBPF) method, and with the Genetic Potential Field (GPF) method, all of them based on evolutionary computation to optimize the APF parameters, were achieved. A simulation platform that uses an MR realistic model was designed to test the path planning algorithms. In general terms, it was demonstrated that the BPF outperforms the APF, GPF, and the PBPF methods by reducing the computational time to find the optimal path at least by a factor of 1.59. These results have a positive impact in the ability of the BPF path planning method to satisfy local and global controllability in dynamic complex environments, avoiding collisions with objects that will interfere the navigation of the MR. (C) 2015 Elsevier Ltd. All rights reserved.