Optimal Path Planning for Mobile Robots Using a Multi-Objective Framework: Integrating Particle Swarm Optimization and Adaptive Bat Algorithm
Subject Areas : AI and RoboticsMohammad Asadi 1 , Zahra Mohammadi 2 , Hossein Nezamabadi-pour 3 , محدثه سليمانپورمقدم 4
1 - Department of Mechatronics Engineering, Bam Higher Education Complex, Bam, Iran
2 - Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
3 - Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
4 - Department of Mechatronics Engineering, Bam Higher Education Complex, Bam, Iran
Keywords: Path planning, Multi-objective Optimization, Metaheuristic algorithms, Bat algorithm and PSO algorithm.,
Abstract :
This study proposes a novel path planning framework based on multi-objective optimization, which simultaneously addresses three key criteria: minimizing path length, optimizing motion smoothness, and maximizing safe distance from obstacles. The multi-objective problem is first transformed into a single-objective formulation using the ε-constraint method and weighted sum approach. Given the NP-hard nature of the problem, an adaptive hybrid metaheuristic algorithm, termed PSO-ABA (Particle Swarm Optimization-Adaptive Bat Algorithm), is developed by integrating the global search mechanism of PSO with the frequency-tuning capabilities of the Bat Algorithm (BA). Subsequently, an adaptive hybrid metaheuristic algorithm, termed PSO-ABA, is developed, which leverages the integration of two complementary global search mechanisms based on Particle Swarm Optimization (PSO) and the Adaptive Bat Algorithm (ABA). In this algorithm, the task of updating the robot’s position is assigned to the ABA, while the PSO is focused on tuning the appropriate parameters. The proposed algorithm addresses several key aspects. First, it aims to develop a trajectory in real space by modifying the existing relationships within the Bat Algorithm. This modification ensures that the robot’s movement is directed as closely as possible toward the target path while minimizing abrupt turns. Second, by enhancing the fitness function, the algorithm prevents the robot’s position from being updated when it is within obstacles, effectively eliminating invalid paths. Performance evaluation in simulated environments with complex obstacle arrangements demonstrates that the proposed framework outperforms conventional routing methods in achieving optimal paths for robots.
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