Constrained Heterogeneous Vehicle Path Planning for Large-area Coverage

TL;DR

This paper presents a comprehensive planning framework for large-area coverage using multiple UAVs and a ground vehicle, optimizing paths to handle battery constraints and minimize total flight distance.

Contribution

It introduces a novel multi-stage optimization approach combining heuristic and exact methods for efficient large-area UAV coverage with ground vehicle support.

Findings

The framework effectively reduces total UAV flight distance.

The combined heuristic and optimization approach achieves near real-time solutions.

The method handles complex constraints like battery life and coverage requirements.

Abstract

There is a strong demand for covering a large area autonomously by multiple UAVs (Unmanned Aerial Vehicles) supported by a ground vehicle. Limited by UAVs' battery life and communication distance, complete coverage of large areas typically involves multiple take-offs and landings to recharge batteries, and the transportation of UAVs between operation areas by a ground vehicle. In this paper, we introduce a novel large-area-coverage planning framework which collectively optimizes the paths for aerial and ground vehicles. Our method first partitions a large area into sub-areas, each of which a given fleet of UAVs can cover without recharging batteries. UAV operation routes, or trails, are then generated for each sub-area. Next, the assignment of trials to different UAVs and the order in which UAVs visit their assigned trails are simultaneously optimized to minimize the total UAV flight distance. Finally, a ground vehicle transportation path which visits all sub-areas is found by solving an asymmetric traveling salesman problem (ATSP). Although finding the globally optimal trail assignment and transition paths can be formulated as a Mixed Integer Quadratic Program (MIQP), the MIQP is intractable even for small problems. We show that the solution time can be reduced to close-to-real-time levels by first finding a feasible solution using a Random Key Genetic Algorithm (RKGA), which is then locally optimized by solving a much smaller MIQP.