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Title: Algorithms for Heterogeneous, Multiple Depot, Multiple Unmanned Vehicle Path Planning Problems

Unmanned vehicles, both aerial and ground, are being used in several monitoring applications to collect data from a set of targets. This article addresses a problem where a group of heterogeneous aerial or ground vehicles with different motion constraints located at distinct depots visit a set of targets. The vehicles also may be equipped with different sensors, and therefore, a target may not be visited by any vehicle. The objective is to find an optimal path for each vehicle starting and ending at its respective depot such that each target is visited at least once by some vehicle, the vehicle–target constraints are satisfied, and the sum of the length of the paths for all the vehicles is minimized. Two variants of this problem are formulated (one for ground vehicles and another for aerial vehicles) as mixed-integer linear programs and a branchand- cut algorithm is developed to compute an optimal solution to each of the variants. Computational results show that optimal solutions for problems involving 100 targets and 5 vehicles can be obtained within 300 seconds on average, further corroborating the effectiveness of the proposed approach.
ORCiD logo [1] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Texas A & M Univ., College Station, TX (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0921-0296
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Journal of Intelligent & Robotic Systems
Additional Journal Information:
Journal Volume: 88; Journal Issue: 2-4; Journal ID: ISSN 0921-0296
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
Country of Publication:
United States
33 ADVANCED PROPULSION SYSTEMS; 97 MATHEMATICS AND COMPUTING; Unmanned vehicles; Branch-and-cut; Heterogeneous vehicles; Site-dependent vehicle routing; Vehicle routing; Dubins vehicles; Reeds-Shepp vehicles
OSTI Identifier: