Optimal Control-Based UAV Path Planning with Dynamically-Constrained TSP with Neighborhoods

TL;DR

This paper presents a novel sampling-based roadmap algorithm that integrates optimal control for UAV path planning, effectively addressing the nonlinear dynamics in a dynamically-constrained TSP with neighborhoods to minimize flight time.

Contribution

It introduces a new algorithm combining sampling-based roadmaps with optimal control to efficiently solve the nonlinear, dynamically-constrained TSP with neighborhoods for UAVs.

Findings

Reduces computation time compared to previous methods.

Improves solution quality through better path optimization.

Validated via numerical simulations.

Abstract

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.