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Performance analysis of path planning techniques for autonomous robots

A deep path planning analysis in 2D environments

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Abstract

Autonomous robots can use path planning techniques to determine the optimal trajectory during the mission. These techniques can be classified as classical, meta heuristic, or machine learning-based. The choice of each technique for a mission depends on its specific requirements, such as finding the shortest path, completing the mission in the minimum time, or/and exploring the environment, among others. Therefore, the path planning algorithms analysis is essential to assist in selecting the appropriate technique. In the literature, the path planning algorithms are typically compared within the same category, and a general analysis is conducted to decide which technique to employ for a particular mission. However, this paper aims to delve deeper into the behavior and performance of these three path planning techniques. The analysis is based on simulations in various environments to understand how each technique behaves and performs, specifically focusing on computation costs, time, and path length efficiency.

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The code for this paper is open source and can be found in https://github.com/lidiaxp/plannie.

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Acknowledgements

The authors acknowledge Brazilian research agency CAPES and CNPQ for the financial support of this research.

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  1. Computer Department, Federal University of São Carlos, São Carlos, Brazil

    Lidia G. S. Rocha, Pedro H. C. Kim & Kelen C. Teixeira Vivaldini

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Rocha, L.G.S., Kim, P.H.C. & Teixeira Vivaldini, K.C. Performance analysis of path planning techniques for autonomous robots. Int J Intell Robot Appl 7, 778–794 (2023). https://doi.org/10.1007/s41315-023-00298-8

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