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Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment

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Abstract

Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.

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Authors and Affiliations

  1. Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Abdul Qadir Faridi, Sanjeev Sharma, Anupam Shukla, Ritu Tiwari & Joydip Dhar

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  1. Abdul Qadir Faridi
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  2. Sanjeev Sharma
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  3. Anupam Shukla
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  4. Ritu Tiwari
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  5. Joydip Dhar
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Correspondence to Sanjeev Sharma.

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Faridi, A.Q., Sharma, S., Shukla, A. et al. Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment. Intel Serv Robotics 11, 171–186 (2018). https://doi.org/10.1007/s11370-017-0244-7

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  • DOI: https://doi.org/10.1007/s11370-017-0244-7

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