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Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field

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Abstract

In this paper, we introduce the concept of Parallel Evolutionary Artificial Potential Field (PEAPF) as a new method for path planning in mobile robot navigation. The main contribution of this proposal is that it makes possible controllability in complex real-world sceneries with dynamic obstacles if a reachable configuration set exists. The PEAPF outperforms the Evolutionary Artificial Potential Field (EAPF) proposal, which can also obtain optimal solutions but its processing times might be prohibitive in complex real-world situations. Contrary to the original Artificial Potential Field (APF) method, which cannot guarantee controllability in dynamic environments, this innovative proposal integrates the original APF, evolutionary computation and parallel computation for taking advantages of novel processors architectures, to obtain a flexible path planning navigation method that takes all the advantages of using the APF and the EAPF, strongly reducing their disadvantages. We show comparative experiments of the PEAPF against the APF and the EAPF original methods. The results demonstrate that this proposal overcomes both methods of implementation; making the PEAPF suitable to be used in real-time applications.

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Authors and Affiliations

  1. Instituto Politécnico Nacional-CITEDI, Av. del Parque No. 1310, Mesa de Otay, C.P. 22510, Tijuana, Baja California, México

    Oscar Montiel, Roberto Sepúlveda & Ulises Orozco-Rosas

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  1. Oscar Montiel
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  2. Roberto Sepúlveda
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  3. Ulises Orozco-Rosas
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Correspondence to Oscar Montiel.

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Montiel, O., Sepúlveda, R. & Orozco-Rosas, U. Optimal Path Planning Generation for Mobile Robots using Parallel Evolutionary Artificial Potential Field. J Intell Robot Syst 79, 237–257 (2015). https://doi.org/10.1007/s10846-014-0124-8

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  • DOI: https://doi.org/10.1007/s10846-014-0124-8

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