TY - GEN

T1 - Relative state modeling based distributed receding horizon formation control of multiple robot systems

AU - Zheng, Wang

AU - Yuqing, He

AU - Jianda, Han

PY - 2011

Y1 - 2011

N2 - Receding horizon control has been shown as a good method in multiple robot formation control problem. However, there are still two disadvantages in almost all receding horizon formation control (RHFC) algorithms. One of them is the huge computational burden due to the complicated nonlinear dynamical optimization, and the other is that most RHFC algorithms use the absolute states directly while relative states between two robots are more accurate and easier to be measured in many applications. Thus, in this paper, a new relative state modeling based distributed RHFC algorithm is designed to solve the two problems referred to above. Firstly, a simple strategy to modeling the dynamical process of the relative states is given; Subsequently, the distributed RHFC algorithm is introduced and the convergence is ensured by some extra constraints; Finally, formation control simulation with respect to three ground robots is conducted and the results show the improvement of the new given algorithm in the real time capability and the insensitiveness to the measurement noise.

AB - Receding horizon control has been shown as a good method in multiple robot formation control problem. However, there are still two disadvantages in almost all receding horizon formation control (RHFC) algorithms. One of them is the huge computational burden due to the complicated nonlinear dynamical optimization, and the other is that most RHFC algorithms use the absolute states directly while relative states between two robots are more accurate and easier to be measured in many applications. Thus, in this paper, a new relative state modeling based distributed RHFC algorithm is designed to solve the two problems referred to above. Firstly, a simple strategy to modeling the dynamical process of the relative states is given; Subsequently, the distributed RHFC algorithm is introduced and the convergence is ensured by some extra constraints; Finally, formation control simulation with respect to three ground robots is conducted and the results show the improvement of the new given algorithm in the real time capability and the insensitiveness to the measurement noise.

KW - distributed receding horizon control

KW - formation control

KW - multiple robot system

KW - relative state model

UR - http://www.scopus.com/inward/record.url?scp=79958230692&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21524-7_14

DO - 10.1007/978-3-642-21524-7_14

M3 - Conference contribution

AN - SCOPUS:79958230692

SN - 9783642215230

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 108

EP - 117

BT - Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings

T2 - 2nd International Conference on Swarm Intelligence, ICSI 2011

Y2 - 12 June 2011 through 15 June 2011

ER -