Abstract
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 (manjunathbhat9920@iitkgp.ac.in), Rahul Kumar (vernwalrahul@iitkgp.ac.in) and Shubham Maddhashiya (shubhamsipah@iitkgp.ac.in) for assisting us in this project and supporting us as and when required.
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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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