Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection
This work addresses efficient robotic inspection for built infrastructure monitoring, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of path planning for UAV vision-based surface inspection by formulating it as an extended traveling salesman problem and proposing an enhanced discrete particle swarm optimization algorithm, resulting in significantly reduced computation time through GPU-based parallel computing while maintaining hardware requirements.
In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.