TY - GEN
T1 - A distributed optimal strategy for rendezvous of multi-robots with random node failures
AU - Park, Hyongju
AU - Hutchinson, Seth
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/31
Y1 - 2014/10/31
N2 - In this paper, we consider the problem of designing distributed control algorithms to solve the rendezvous problem for multi-robot systems with limited sensing, for situation in which random nodes may fail during execution. We first formulate a distributed solution based upon averaging algorithms that have been reported in the consensus literature. In this case, at each stage of execution a 1-step sequential optimal control (i.e., naïve greedy algorithm) is used. We show that by choosing an appropriate constraint set, finite-time point convergence is guaranteed. We then propose a distributed stochastic optimal control algorithm that minimizes a mean-variance cost function for each stage, given that the probability distribution for possible node failures is known a priori. We show via simulation results that our algorithm provides competitive rendezvous task performance in comparison to that of the classical circumcenter algorithm for cases in which there are no node failures. Then we show, via examples with multiple node failures, that our proposed algorithm provides better rendezvous task performance than contemporary algorithms in cases for which failures occur. Additionally, we generate and compare a spectrum of results by varying the probabilities of node failures, or varying the weight value for the variance term in the cost functional. The results suggest that by choosing the design parameters appropriately, one may adjust the degree of soft constraints of the controller as well.
AB - In this paper, we consider the problem of designing distributed control algorithms to solve the rendezvous problem for multi-robot systems with limited sensing, for situation in which random nodes may fail during execution. We first formulate a distributed solution based upon averaging algorithms that have been reported in the consensus literature. In this case, at each stage of execution a 1-step sequential optimal control (i.e., naïve greedy algorithm) is used. We show that by choosing an appropriate constraint set, finite-time point convergence is guaranteed. We then propose a distributed stochastic optimal control algorithm that minimizes a mean-variance cost function for each stage, given that the probability distribution for possible node failures is known a priori. We show via simulation results that our algorithm provides competitive rendezvous task performance in comparison to that of the classical circumcenter algorithm for cases in which there are no node failures. Then we show, via examples with multiple node failures, that our proposed algorithm provides better rendezvous task performance than contemporary algorithms in cases for which failures occur. Additionally, we generate and compare a spectrum of results by varying the probabilities of node failures, or varying the weight value for the variance term in the cost functional. The results suggest that by choosing the design parameters appropriately, one may adjust the degree of soft constraints of the controller as well.
UR - http://www.scopus.com/inward/record.url?scp=84911472957&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911472957&partnerID=8YFLogxK
U2 - 10.1109/IROS.2014.6942703
DO - 10.1109/IROS.2014.6942703
M3 - Conference contribution
AN - SCOPUS:84911472957
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 1155
EP - 1160
BT - IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Y2 - 14 September 2014 through 18 September 2014
ER -