Bidirectional artificial potential field-based ant colony optimization for robot path planning

IF 4.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems Pub Date : 2024-10-19 DOI:10.1016/j.robot.2024.104834

Bo Fu , Yuming Chen , Yi Quan , Xilin Zhou , Chaoshun Li

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引用次数: 0

Abstract

Ant colony optimization (ACO) is a common approach for addressing mobile robot path planning problems. However, it still encounters some challenges including slow convergence speed, susceptibility to local optima, and a tendency to falling into traps. We propose a bidirectional artificial potential field-based ant colony optimization (BAPFACO) algorithm to solve these issues. First, the bidirectional artificial potential field is introduced to initialize the grid environment model and restrict direction selection to jump out of the trap. Second, an adaptive heuristic function is presented to strengthen directionality of the algorithm and reduce the turning times. Third, a pseudo-random state transition rule based on potential difference between starting and ending nodes is developed to accelerate convergence speed. Finally, an improved pheromone update strategy incorporating pheromone diffusion mechanism and elite ants update strategy is proposed to help getting out of local optima. To demonstrate the advantages of BAPFACO, the validation of the performance in six different complexity environments and comparative experiments with other conventional search algorithms and ACO variants are conducted. The results of experiment show that compared to various ACO variants, BAPFACO have advantages in terms of reducing the turning times, shortening path length, improving convergence speed and avoiding ant loss. In complex environments, compared to IHMACO, the average path length enhancement percentage (PLE) of BAPFACO is 20.98%, the average iterations enhancement percentage (IE) of BAPFACO is 20.00% and the average turning times enhancement percentage (TE) of BAPFACO is 49.43%. These results firmly demonstrate the efficiency and practicality of the BAPFACO algorithm for mobile robot in path planning.

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基于人工势场的双向蚁群优化机器人路径规划

蚁群优化（ACO）是解决移动机器人路径规划问题的常用方法。然而，它仍然面临一些挑战，包括收敛速度慢、容易出现局部最优以及容易陷入陷阱。我们提出了一种基于双向人工势场的蚁群优化算法（BAPFACO）来解决这些问题。首先，引入双向人工势场来初始化网格环境模型，并限制方向选择以跳出陷阱。其次，提出了一种自适应启发式函数，以加强算法的方向性并减少转弯时间。第三，基于起点和终点节点之间的电位差开发了一种伪随机状态转换规则，以加快收敛速度。最后，提出了一种改进的信息素更新策略，该策略结合了信息素扩散机制和精英蚂蚁更新策略，有助于摆脱局部最优状态。为了证明 BAPFACO 的优势，我们在六种不同的复杂性环境中进行了性能验证，并与其他传统搜索算法和 ACO 变体进行了对比实验。实验结果表明，与各种 ACO 变体相比，BAPFACO 在减少转弯时间、缩短路径长度、提高收敛速度和避免蚂蚁损失方面具有优势。在复杂环境下，与 IHMACO 相比，BAPFACO 的平均路径长度提高率（PLE）为 20.98%，平均迭代次数提高率（IE）为 20.00%，平均转弯次数提高率（TE）为 49.43%。这些结果充分证明了 BAPFACO 算法在移动机器人路径规划中的高效性和实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Autonomous Systems 工程技术-机器人学

CiteScore

9.00

自引率

7.00%

发文量

164

审稿时长

4.5 months

期刊介绍： Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.