TY - GEN
T1 - Distributed planning of optimal reconfiguration with collision avoidance and final configuration constraints
AU - Guo, Juan
AU - Chu, Jing
AU - Yan, Jie
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/8
Y1 - 2015/2/8
N2 - This paper presents a distributed framework to develop planning algorithms for optimal reconfiguration of multiagent systems in the presence of collision avoidance and final configuration constraints. First, reconfiguration problems are formulated as optimal control problems which are addressed using direct methods. Thus, reconfiguration problems become nonlinear programming problems subject to coupling variables, i.e., final configuration constraints, and coupling constraints, i.e., collision avoidance constraints. In our framework subgradient methods are adopted to include reconfiguration cases with non-differentiable objectives. Then, to develop distributed algorithms, final configuration constraints are tackled by primal decomposition, while collision avoidance constraints by dual decomposition. Since standard decomposition methods prevent the distributed implementation due to the existence of master problems, primal decomposition is combined with the distributed consensus algorithm and dual decomposition is integrated with the incremental subgradient method. In the end, this framework is employed to develop distributed planning algorithms for optimal reconfiguration of satellite clusters.
AB - This paper presents a distributed framework to develop planning algorithms for optimal reconfiguration of multiagent systems in the presence of collision avoidance and final configuration constraints. First, reconfiguration problems are formulated as optimal control problems which are addressed using direct methods. Thus, reconfiguration problems become nonlinear programming problems subject to coupling variables, i.e., final configuration constraints, and coupling constraints, i.e., collision avoidance constraints. In our framework subgradient methods are adopted to include reconfiguration cases with non-differentiable objectives. Then, to develop distributed algorithms, final configuration constraints are tackled by primal decomposition, while collision avoidance constraints by dual decomposition. Since standard decomposition methods prevent the distributed implementation due to the existence of master problems, primal decomposition is combined with the distributed consensus algorithm and dual decomposition is integrated with the incremental subgradient method. In the end, this framework is employed to develop distributed planning algorithms for optimal reconfiguration of satellite clusters.
UR - http://www.scopus.com/inward/record.url?scp=84962033786&partnerID=8YFLogxK
U2 - 10.1109/CDC.2015.7402493
DO - 10.1109/CDC.2015.7402493
M3 - 会议稿件
AN - SCOPUS:84962033786
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1951
EP - 1957
BT - 54rd IEEE Conference on Decision and Control,CDC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th IEEE Conference on Decision and Control, CDC 2015
Y2 - 15 December 2015 through 18 December 2015
ER -