Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

Abstract This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.

[1]  Jean-Daniel Boissonnat,et al.  Accessibility region for a car that only moves forwards along optimal paths , 1993 .

[2]  Tal Shima,et al.  Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms , 2006, Comput. Oper. Res..

[3]  L. Dubins.  On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents , 1957 .

[4]  A. Ollero,et al.  Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms , 2004, DARS.

[5]  Goran Martinović,et al.  Solving the task assignment problem with ant colony optimisation incorporating ideas from the clonal selection algorithm , 2015, Int. J. Bio Inspired Comput..

[6]  Jonathan P. How,et al.  COORDINATION AND CONTROL OF MULTIPLE UAVs , 2002 .

[7]  Nikhil Nigam,et al.  The Multiple Unmanned Air Vehicle Persistent Surveillance Problem: A Review , 2014 .

[8]  Xing Zhang,et al.  A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets , 2014 .

[9]  S. Rasmussen,et al.  Tree search algorithm for assigning cooperating UAVs to multiple tasks , 2008 .

[10]  Le Thi Hoai An,et al.  Globally solving a nonlinear UAV task assignment problem by stochastic and deterministic optimization approaches , 2010, Optimization Letters.

[11]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[12]  Marjorie Darrah,et al.  Multiple UAV Dynamic Task Allocation using Mixed Integer Linear Programming in a SEAD Mission , 2005 .

[13]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

[14]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[15]  Lior Pachter,et al.  Constrained Optimization for UAV Task Assignment , 2004 .

[16]  Tal Shima,et al.  Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms , 2011, Comput. Oper. Res..

[17]  Tal Shima,et al.  UAV Cooperative Decision and Control: Challenges and Practical Approaches , 2008 .

[18]  Ningfei Wang,et al.  Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes , 2013 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Emilio Frazzoli,et al.  Traveling Salesperson Problems for the Dubins Vehicle , 2008, IEEE Transactions on Automatic Control.

[21]  M. Shanmugavel,et al.  Cooperative Path Planning of Unmanned Aerial Vehicles , 2010 .

[22]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .