This paper investigates a decentralized trajectory optimization method to solve a nonlinear constrained trajectory optimization problem. Especially, we consider a problem constrained on the terminal time and angle in a multi-robot application. The proposed algorithm is based on virtual motion camouflage (VMC) and particle swarm optimization (PSO). VMC changes a typical full space optimal problem to a subspace optimal problem, so it can reduce the dimension of the original problem by using path control parameters (PCPs). If PCPs are optimized, then the optimal path can be obtained. In this work, PSO is used to optimize these PCPs. In multi-robot path planning, each robot generates its own optimal path by using VMC and PSO, and sends its path information to the other robots. Then, the other robots use this path information when planning their own paths. Simulation and experimental results show that the optimal paths considering the terminal time and angle constraints are effectively generated by decentralized VMC and PSO.
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This research was financially supported by a grant to Unmanned Technology Research Center funded by Defense Acquisition Program Administration, and by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (MSIP) (no. 2009-0083495).
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Kwak, D.J., Choi, B., Cho, D. et al. Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization. Auton Robot 38, 161–177 (2015) doi:10.1007/s10514-014-9399-7
- Trajectory optimization
- Virtual motion camouflage
- Particle swarm optimization
- Terminal time and angle control
- Multi-robot systems