Research on Multi-Vehicle and Multi-Task Route Planning for Autonomous Delivery Robots in Parks
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In the context of multi-vehicle and multi-task logistics distribution within a park, traditional algorithms are often hindered by high computational complexity and slow convergence rates. Particle Swarm Optimization (PSO) has gained popularity in path planning for autonomous delivery vehicles due to its straightforward algorithmic principles, broad applicability, and comprehensive search capabilities. However, the conventional PSO is susceptible to premature convergence, leading to local optima. To address this, this study incorporates the Tent map into the PSO to enhance the algorithm's global search ability and prevent premature convergence. Benchmark function tests demonstrate that the improved Particle Swarm Optimization algorithm (TPSO), as proposed in this study, exhibits faster convergence and greater accuracy.In the instance verification section, X Park was selected as an example to construct a multi-vehicle and multi-task model for the logistics distribution within the park. The TPSO algorithm proposed in this paper was used to solve the model, and finally, the superiority of the TPSO algorithm was verified through comparative simulation.
Abstract
In the context of multi-vehicle and multi-task logistics distribution within a park, traditional algorithms are often hindered by high computational complexity and slow convergence rates. Particle Swarm Optimization (PSO) has gained popularity in path planning for autonomous delivery vehicles due to its straightforward algorithmic principles, broad applicability, and comprehensive search capabilities. However, the conventional PSO is susceptible to premature convergence, leading to local optima. To address this, this study incorporates the Tent map into the PSO to enhance the algorithm's global search ability and prevent premature convergence. Benchmark function tests demonstrate that the improved Particle Swarm Optimization algorithm (TPSO), as proposed in this study, exhibits faster convergence and greater accuracy.In the instance verification section, X Park was selected as an example to construct a multi-vehicle and multi-task model for the logistics distribution within the park. The TPSO algorithm proposed in this paper was used to solve the model, and finally, the superiority of the TPSO algorithm was verified through comparative simulation.
참고문헌 (16)
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