K-Redundant Trees for Safe and Efficient Multi-robot Recovery in Complex Environments

This paper presents a self-stabilizing distributed algorithm to recover a large number of robots safely and efficiently in a goal location. Previously, we designed a distributed algorithm, called DMLST, to recover robots [1]. Our approach constructed a maximum-leaf spanning tree for physical routing, such that interior robots remained stationary and leaf robots move. In this paper, we extend our approach to k-DMLST recovery that provides k-connectivity in the network, meaning that each robot is connected to the goal location through k trees. This redundancy provides stronger network connectivity by reducing the probability of losing the parent during recovery. We also propose an efficient navigation algorithm for the motion of robots which guarantees forward progress during the recovery. k-DMLST recovery has been tested and compared with other methods in simulation, and implemented on a physical multi-robot system. A basic recovery algorithm fails in all experiments, and DMLST recovery is not successful in few trials. However, kDMLST recovery efficiently succeeds in all trials.