Sampling-Based Tour Generation of Arbitrarily Oriented Dubins Sensor Platforms

TL;DR

This paper introduces a novel sampling-based method for generating optimal paths for heterogeneous UAV fleets with different sensing and motion constraints, formulated as a generalized TSP variant and solved efficiently.

Contribution

It formulates the heterogeneous multi-UAV path planning as a GHMDATSP and transforms it into an ATSP, enabling efficient heuristic solutions with local optimization for high-quality paths.

Findings

The method effectively minimizes total fleet cost while visiting all task regions.

The transformation to ATSP allows use of efficient heuristic algorithms.

Numerical experiments validate the approach's applicability and effectiveness.

Abstract

This paper describes a formulation and develops a novel procedure for a fleet of unmanned aerial vehicles (UAVs) from the perspective of remotely executable tasks. In a complex mission environment, the characteristics of vehicles can be different in terms of sensing capability, range, direction, or the motion constraints. The purpose of this paper is to find a set of paths that minimizes the sum of costs while every task region is visited exactly once under certain reasonable assumptions. The heterogeneous multi-UAV path planning problem is formulated as a generalized, heterogeneous, multiple depot traveling salesmen problem (GHMDATSP), which is a variant of the traveling salesman problem. The proposed transformation procedure changes an instance of the GHMDATSP into a format of an Asymmetric, Traveling Salesman Problem (ATSP) to obtain tours for which the total cost of a fleet of vehicles is minimized. The instance of the ATSP is solved using the Lin-Kernighan-Helsgaun heuristic, and the result is inversely transformed to the GHMDATSP-formatted instance to obtain a set of tours. An additional local optimization based path refinement process helps obtain a high-quality solution. Numerical experiments investigate and confirm for the validity and applicability of the proposed procedure.