Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 3; p. 734
• Main Authors: Chen, Hao-Xiang, Nan, Ying, Yang, Yi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12-02-2019; MDPI
• Subjects: Classification; Computer simulation; Cooperation; Design optimization; Genetic algorithms; Heuristic; Linear programming; Multiple objective analysis; Operations research; Optimization algorithms; Pareto dominance determination; reconnaissance task assignment; Robots; Scaling factors; Search algorithms; sensor; Sensors; Sorting algorithms; symbiotic organisms search; Traveling salesman problem; UAV; Unmanned aerial vehicles; Vehicles; United States > US; reconnaissance task assignment; sensor; UAV; Pareto dominance determination; symbiotic organisms search
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19030734

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs' task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.