Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm

Abstract Motion planning is a key factor in enhancing the autonomy level of unmanned flying vehicles. A new dynamic hybrid algorithm is developed to solve the motion planning problem in real-time using a heuristic optimization approach. The proposed algorithm effectively combines desired features such as rapid convergence to an optimal path with reduced computational effort. In addition to the terrain obstacles, the proposed algorithm is able to avoid random threats that may arise sporadically in the terrain. Using the maneuver automaton concept, nonlinear dynamic model and performance constraints are also considered in the process of motion planning to further ensure feasible trajectories. Evaluation of the proposed algorithm against several simulated scenarios has effectively demonstrated its potential for generating optimal contour-matching trajectories that succeed in avoiding stochastic obstacles.

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