Optimal Control-Based UAV Path Planning with Dynamically-Constrained TSP with Neighborhoods
This work addresses path planning for UAVs with remote sensing or communication tasks, offering incremental improvements in computational efficiency and solution quality for domain-specific applications.
The paper tackles UAV path planning with dynamic constraints by formulating it as a dynamically-constrained traveling salesman problem with neighborhoods and proposes a sampling-based roadmap algorithm with optimal control-based path generation to find close-to-optimal solutions efficiently. Comparative simulations show the algorithm reduces computation time and improves solution quality compared to previous methods.
This paper addresses path planning of an unmanned aerial vehicle (UAV) with remote sensing capabilities (or wireless communication capabilities). The goal of the path planning is to find a minimum-flight-time closed tour of the UAV visiting all executable areas of given remote sensing and communication tasks; in order to incorporate the nonlinear vehicle dynamics, this problem is regarded as a dynamically-constrained traveling salesman problem with neighborhoods. To obtain a close-to-optimal solution for the path planning in a tractable manner, a sampling-based roadmap algorithm that embeds an optimal control-based path generation process is proposed. The algorithm improves the computational efficiency by reducing numerical computations required for optimizing inefficient local paths, and by extracting additional information from a roadmap of a fixed number of samples. Comparative numerical simulations validate the efficiency of the presented algorithm in reducing computation time and improving the solution quality compared to previous roadmap-based planning methods.