Abstract
This article addresses a fundamental path planning problem which aims to route a collection of heterogeneous vehicles such that each target location is visited by some vehicle and the sum of the travel costs of the vehicles is minimal. Vehicles are heterogeneous as the cost of traveling between any two locations depends on the type of the vehicle. Algorithms are developed for this path planning problem with bounds on the quality of the solutions produced by the algorithms. Computational results show that high quality solutions can be obtained for the path planning problem involving four vehicles and 40 targets using the proposed approach.
Highlights
Recent advances in small sensing devices and unmanned vehicles (UVs) have provided an efficient way of collecting useful information in civil and military applications such as crop monitoring [1, 2], forest temperature monitoring [3] and border surveillance [4]
This article addresses the following resource allocation problem called the multiple heterogeneous UV problem (MHUVP) that arises in monitoring applications: Given a team of small heterogeneous UVs located at distinct depots, a set of target locations and the cost of traveling between any two locations for each UV, the problem aims to find a sequence of targets for each UV such that each target is visited by a UV, the UVs return to their respective depots after visiting the targets, and the sum of the travel cost of all the UVs is minimized
A collection of UVs is heterogeneous if the cost of traveling between any two target locations depend on the type of the UV
Summary
Recent advances in small sensing devices and unmanned vehicles (UVs) have provided an efficient way of collecting useful information in civil and military applications such as crop monitoring [1, 2], forest temperature monitoring [3] and border surveillance [4]. The main contribution of this article is in developing a m log2(n + 1) -approximation algorithm for the MHUVP where n denotes the number of targets. This is the first approximation result for a path planning problem involv‐ ing heterogeneous UVs with asymmetric travel costs. This approximation algorithm first partitions the targets among UVs by solving a Linear Programming relaxation and uses the Frieze et al algorithm [18] to find a path for each of the UVs. We perform a simple modification of this.
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