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Potential and Sampling Based RRT Star for Real-Time Dynamic Motion Planning Accounting for Momentum in Cost Function

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11307))

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Abstract

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Path planning is an extremely important step in every robotics related activity today. In this paper, we present an approach to a real-time path planner which makes use of concepts from the random sampling of the Rapidly-exploring random tree and potential fields. It revises the cost function to incorporate the dynamics of the obstacles in the environment. Not only the path generated is significantly different but also it is much more optimal and rigid to breakdowns and features faster replanning. This variant of the Real-Time RRT* incorporates artificial potential field with a revised cost function.

S. Agarwal, A.K. Gaurav, M.K. Nirala and S. Sinha—Equal contribution.

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Acknowledgement

We thank Manjunath Bhatt ([email protected]), Rahul Kumar ([email protected]) and Shubham Maddhashiya ([email protected]) for assisting us in this project and supporting us as and when required.

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Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Saurabh Agarwal, Ashish Kumar Gaurav, Mehul Kumar Nirala & Sayan Sinha

Authors
  1. Saurabh Agarwal
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  2. Ashish Kumar Gaurav
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  3. Mehul Kumar Nirala
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  4. Sayan Sinha
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Corresponding author

Correspondence to Sayan Sinha .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Agarwal, S., Gaurav, A.K., Nirala, M.K., Sinha, S. (2018). Potential and Sampling Based RRT Star for Real-Time Dynamic Motion Planning Accounting for Momentum in Cost Function. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-04239-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04238-7

  • Online ISBN: 978-3-030-04239-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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