Abstract
Due to the NP-hard complexity, the path planning problem may perhaps best be resolved by stochastically searching for an acceptable solution rather than using a complete search to find the guaranteed best solution. Most other evolutionary path planners tend to produce jagged paths consisting of a set of nodes connected by line segments. This paper presents a novel path planning approach based on AppART and Particle Swarm Optimization (PSO). AppART is a neural model multidimensional function approximator, while PSO is a promising evolutionary algorithm. This path planning approach combines neural and evolutionary computing in order to evolve smooth motion paths quickly. In our simulation experiments, some complicated path-planning environments were tested, the result show that the hybrid approach is an effective path planner which outperforms many existing methods.
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Tang, J., Zhu, J., Sun, Z. (2005). A Novel Path Planning Approach Based on AppART and Particle Swarm Optimization. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_40
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DOI: https://doi.org/10.1007/11427469_40
Publisher Name: Springer, Berlin, Heidelberg
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