A Path Planning Algorithm for a Hybrid UAV Traveling in Noise Restricted Zones

TL;DR

This paper introduces an efficient path planning algorithm for hybrid UAVs operating in noise-restricted zones, optimizing energy use and route planning with significant computational improvements over existing methods.

Contribution

The paper develops a mixed-integer convex programming approach for path planning in HUAVs with noise zones, including a TSP extension, demonstrating substantial efficiency gains and accuracy.

Findings

Up to 100-fold reduction in computation time for lower bound calculation.

Average gap of 0.24% between lower bound and exact cost.

Route planning within 1.02% cost difference using simplified SOC assumptions.

Abstract

This paper presents an integrated approach for efficient path planning and energy management in hybrid unmanned aerial vehicles (HUAVs) equipped with dual fuel-electric propulsion systems. These HUAVs operate in environments that include noise-restricted zones, referred to as quiet zones, where only electric mode is permitted. We address the problem by parameterizing the position of a point along the side of the quiet zone using its endpoints and a scalar parameter, transforming the problem into a variant of finding the shortest path over a graph of convex sets. We formulate this problem as a mixed-integer convex program (MICP), which can be efficiently solved using commercial solvers. Additionally, a tight lower bound can be obtained by relaxing the path-selection variable. Through extensive computations across 200 instances over four maps, we show a substantial improvement in computational efficiency over a state-of-the-art method, achieving up to a 100-fold and 10-fold decrease in computation time for calculating the lower bound and the exact solution, respectively. Moreover, the average gap between the exact cost and the lower bound was approximately 0.24%, and the exact cost was 1.05% lower than the feasible solution from the state-of-the-art approach on average, highlighting the effectiveness of our method. We also extend our approach to plan the HUAV route to visit a set of targets and return to its starting location in environments with quiet zones, yielding a Traveling Salesman Problem (TSP). We employ two methodologies to solve the TSP: one where the SOC at each target is discretized, and another where it is assumed to be the minimum allowable level upon departure. A comparative analysis reveals the second method achieves a cost within 1.02% of the first on average while requiring significantly less computational time.