Robot motion planning: a distributed representation approach
International Journal of Robotics Research
Computer rendering of stochastic models
Communications of the ACM
Robot Motion Planning
Linear Optimal Control: H(2) and H (Infinity) Methods
Linear Optimal Control: H(2) and H (Infinity) Methods
Analysis of Generalized Pattern Searches
SIAM Journal on Optimization
Route Planning for Unmanned Air Vehicles with Multiple Missions Using an Evolutionary Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolutionary trajectory planner for multiple UAVs in realistic scenarios
IEEE Transactions on Robotics
Evolutionary Route Planner for Unmanned Air Vehicles
IEEE Transactions on Robotics
Evolutionary algorithm based offline/online path planner for UAV navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper addresses the problem of path planning for multiple UAVs. The paths are planned to maximize collected amount of information from Desired Regions (DR) while avoiding Forbidden Regions (FR) violation and reaching the destination. The approach extends prior study for multiple UAVs by considering 3D environment constraints. The path planning problem is studied as an optimization problem. The problem has been solved by a Genetic Algorithm (GA) with the proposal of novel evolutionary operators. The initial populations have been generated from a seed-path for each UAV. The seed-paths have been obtained both by utilizing the Pattern Search method and solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of "which DR should be visited by which UAV". It should be emphasized that all of the paths in population in any generation of the GA have been constructed using the dynamical mathematical model of an UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm has been tested with different scenarios, and the results are presented in Section 6. Although there are previous studies in this field, this paper focuses on maximizing the collected information instead of minimizing the total mission time. Even though, a direct comparison of our results with those in the literature is not possible, it has been observed that the proposed methodology generates satisfactory and intuitively expected solutions.