Electing an Approximate Center in a Huge Modular Robot with the k-BFS SumSweep Algorithm

Abstract

Among the diversity of the existing modular robotic systems, we consider in this paper the subset of distributed modular robotic ensembles composed of resourceconstrained identical modules that are organized in a lattice structure and which can only communicate with neighboring modules. These modular robotic ensembles that we name LMRs form asynchronous distributed embedded systems. In many algorithms dedicated to distributed system coordination, a specific role has to be played by a leader, i.e., a single node in the system. This leader can be elected using various criteria. A possible strategy is to elect a center node, i.e., a node that has the minimum distance to all the other nodes. Indeed, this node is ideally located to communicate with all the others and this leads to better performance in many algorithms. The contribution of this paper is to propose the k-BFS SumSweep algorithm designed to elect an approximate-center node. We evaluated our algorithm both on hardware modular robots and in a simulator for large ensemble of robots. Results show our algorithm is a good trade-off if accuracy is the main concern. Indeed, it is the most precise approximation algorithm for systems with less than 7,000 modules. In these systems, our algorithm exhibits a relative accuracy between 92% to 100%. Moreover the k-BFS SumSweep algorithm is reasonably efficient in terms of time and communication, and has a limited memory footprint.

A novel distributed meta-module motion design for modular robotic systems based on grid partition method

“…14 Among the self-reconfiguration strategies, the centralized strategies 15,16 calculate the configuration distance come from the comparison between the target configuration and the current configuration, and then drive the locomotion of certain modules to minimize the configuration distance. In the distributed strategies, such as the gradient-based method, 17 k-BFS sum-sweep algorithm, 18 artificial neural networks with pruning technology 19 and the unique one-dimensional (ID) assignment method based on the energy and time complexity optimization, 20 it is generally assumed that only local communication is valid between connecting modules. That means the components of control strategies for self-configuration such as detection for potential target positions, decision from path planning, and execution for the module's locomotion, need to be designed carefully to meet the module's ability both in sensing, communication, and movability.…”