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Multi Robot Path Planning for Known and Unknown Target Using Bacteria Foraging Algorithm

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

This paper discusses a new method for multi robot path planning using bacteria foraging algorithm for known and unknown target. Here direction based movement is used to classify unknown and unknown target. The directional is representing by divide the area virtually by clustering based method. In which each cluster point represents the direction. When the target is known robot has idea for direction of movement to reach target. But when the target is unknown robot have no idea related to existence of target in which direction. After decide the direction robot will move according to the bacteria foraging algorithm that modified according to the robotics problem. The algorithm is tested for both simple and complex environments. Four parameters move, time, coverage and energy are calculated for comparison. The results show that proposed method work well for both known and unknown target path planning problem.

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Authors and Affiliations

  1. ABV- Indian Institute of Information Technology and Management, Gwalior, India

    Sanjeev Sharma, Chiranjib Sur, Anupam Shukla & Ritu Tiwari

Authors
  1. Sanjeev Sharma
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  2. Chiranjib Sur
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  3. Anupam Shukla
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  4. Ritu Tiwari
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Corresponding author

Correspondence to Sanjeev Sharma .

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Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Sharma, S., Sur, C., Shukla, A., Tiwari, R. (2015). Multi Robot Path Planning for Known and Unknown Target Using Bacteria Foraging Algorithm. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_58

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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