COAA*- An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments

Jun Jet Tai, Swee King Phang, Felicia Yen Myan Wong

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    Abstract

    Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A*(COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor's University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.

    Original languageEnglish
    Pages (from-to)159-174
    Number of pages16
    JournalUnmanned Systems
    Volume10
    Issue number2
    Early online date3 Sep 2021
    DOIs
    Publication statusE-pub ahead of print - 3 Sep 2021

    Bibliographical note

    Electronic version of an article published as Unmanned Systems, vol. 10, no. 2, pp. 159-174. 10.1142/S2301385022500091 © copyright World Scientific Publishing
    Company https://doi.org/10.1142/S2301385022500091
    Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

    Keywords

    • multirotor
    • navigation
    • Obstacle avoidance
    • unmanned aerial vehicle

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Automotive Engineering
    • Aerospace Engineering
    • Control and Optimization

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