Fast Trajectory Planning Via the B Spline Augmented Virtual Motion Camouflage Approach

Abstract

The recently developed bioinspired virtual motion camouflage (VMC) method can be used to rapidly solve nonlinear constrained optimal trajectory problems. However, the optimality of VMC solution is affected by the dimension reduced search space. Compared with the VMC method, the Bspline augmented VMC (BVMC) method studied in this paper improves the optimality of the solution, while the computational cost will not be significantly increased. Two simulation examples, the Snell's river problem and a robotic minimum time obstacle avoidance problem, are used to show the advantages of the algorithm.