Introduction to algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Handbook of combinatorics (vol. 1)
Handbook of combinatorics (vol. 1)
Robot Motion Planning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
Computers and Operations Research
Introduction to Operations Research and Revised CD-ROM 8
Introduction to Operations Research and Revised CD-ROM 8
Planning Algorithms
UAV Cooperative Decision and Control: Challenges and Practical Approaches
UAV Cooperative Decision and Control: Challenges and Practical Approaches
On optimal cooperative conflict resolution for air traffic management systems
IEEE Transactions on Intelligent Transportation Systems
Info-gap approach to multiagent search under severe uncertainty
IEEE Transactions on Robotics
Heuristics for determining a patrol path of an unmanned combat vehicle
Computers and Industrial Engineering
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
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The problem of integrating task assignment and planning paths for a group of cooperating uninhabited aerial vehicles, servicing multiple targets, is addressed. In the problem of interest the uninhabited aerial vehicles need to perform multiple consecutive tasks cooperatively on each ground target. A Dubins car model is used for motion planning, taking into account each vehicle's specific constraint of minimum turn radius. By using a finite set to define the visitation angle of a vehicle over a target we pose the integrated problem of task assignment and path optimization in the form of a graph. This new approach results in suboptimal trajectory assignments. Refining the visitation angle discretization allows for an improved solution. Due to the computational complexity of the resulting combinatorial optimization problem, we propose genetic algorithms for the stochastic search of the space of solutions. We distinguish between two cases of vehicle group composition: homogeneous, where all vehicles are identical; and heterogeneous, where the vehicles may have different operational capabilities and kinematic constraints. The performance of the genetic algorithms is demonstrated through sample runs and a Monte Carlo simulation study. Results show that the algorithms quickly provide good feasible solutions, and find the optimal solution for small sized problems.