Autonomous Robots

, Volume 38, Issue 2, pp 161–177 | Cite as

Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization

  • Dong Jun Kwak
  • Byunghun Choi
  • Dongsoo Cho
  • H. Jin Kim
  • Choon-woo Lee
Article

Abstract

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.

Keywords

Trajectory optimization Virtual motion camouflage  Particle swarm optimization Terminal time and angle control Multi-robot systems 

Supplementary material

Supplementary material 1 (wmv 6458 KB)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Dong Jun Kwak
    • 1
  • Byunghun Choi
    • 2
  • Dongsoo Cho
    • 1
  • H. Jin Kim
    • 1
  • Choon-woo Lee
    • 3
  1. 1.Department of Mechanical and Aerospace EngineeringSeoul National UniversitySeoulKorea
  2. 2.Agency for Defense DevelopmentDaejeonKorea
  3. 3.Samsung Thales, Corp.SeongnamKorea

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