e-space
Manchester Metropolitan University's Research Repository

    A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles

    Huang, C, Lan, Y, Liu, Y, Zhou, W, Pei, H, Yang, L, Cheng, Y, Hao, Y and Peng, Y ORCID logoORCID: https://orcid.org/0000-0002-5508-1819 (2018) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Complexity, 2018. p. 8420294. ISSN 1076-2787

    [img] Published Version
    Available under License Creative Commons Attribution.

    Download (5MB)

    Abstract

    © 2018 Chenxi Huang et al. Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    186Downloads
    6 month trend
    121Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record