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Sampling-based Non-Holonomic Path Generation for Self-driving Cars

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Abstract

Semi-autonomous self driving car technologies are commercially available today, but making them fully autonomous under guaranteed safety is still an open challenge. There are holonomic robot path planning algorithms for generating guaranteed collision-free paths even in very complex but completely known environments. However, it is not feasible to apply these algorithms to non-holonomic cars. In this paper we propose a novel approach to solve this problem in real-time, by generating a series of incremental collision-free path segments from the start to end configuration using an incremental sampling-based planner, such as Rapidly-exploring Random Trees (RRT). The proposed approach employs only local sensor data (point cloud data) for path planning of nonholonomic self driving cars in quasi static unknown environments. The proposed planner generates real-time paths that guarantee safety. The algorithms are extensively testedand validated in popular benchmark simulation environments.

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Acknowledgements

This publication is based upon research work supported by the Khalifa University of Science and Technology under Award No. RC1-2018-KUCARS

Funding

This publication is based upon research work supported by the Khalifa University of Science and Technology under Award No. RC1-2018-KUCARS.

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Authors and Affiliations

  1. Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, UAE

    Sotirios Spanogiannopoulos, Yahya Zweiri & Lakmal Seneviratne

  2. Faculty of Science, Engineering and computing, Kingston University, London, UK

    Yahya Zweiri

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  1. Sotirios Spanogiannopoulos
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Contributions

Author: Sotirios Spanogiannopoulos Facilitating real-time planner for non-holonomic path generation process will need further advancements [68]:

1. Computational simplicity in non-holonomic path generation.

2. acilitation of incremental collision-free paths.

3. Ability to modify the existing non-holonomic path generated based on updated sensor data from the environment.

Co-authors: Yahya Zweiri and Lakmal Seneviratne Help on reviewing paper from different angles several times before the final submission. Special thanks to Lakmal Seneviratne for his suggestions on paper’s proofreading.

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Correspondence to Sotirios Spanogiannopoulos.

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Spanogiannopoulos, S., Zweiri, Y. & Seneviratne, L. Sampling-based Non-Holonomic Path Generation for Self-driving Cars. J Intell Robot Syst 104, 14 (2022). https://doi.org/10.1007/s10846-021-01440-z

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