3 research outputs found
An Approach to the Bio-Inspired Control of Self-reconfigurable Robots
Self-reconfigurable robots are robots built by modules which
can move in relationship to each other. This ability of changing its physical
form provides the robots a high level of adaptability and robustness.
Given an initial configuration and a goal configuration of the robot, the
problem of self-regulation consists on finding a sequence of module moves
that will reconfigure the robot from the initial configuration to the goal
configuration. In this paper, we use a bio-inspired method for studying
this problem which combines a cluster-flow locomotion based on cellular
automata together with a decentralized local representation of the
spatial geometry based on membrane computing ideas. A promising 3D
software simulation and a 2D hardware experiment are also presented.National Natural Science Foundation of China No. 6167313
Distributed leader election and computation of local identifiers for programmable matter
International audienceThe context of this paper is programmable matter, which consists of a set of computational elements, called particles, in an infinite graph. The considered infinite graphs are the square, triangular and king grids. Each particle occupies one vertex, can communicate with the adjacent particles, has the same clockwise direction and knows the local positions of neighborhood particles. Under these assumptions, we describe a new leader election algorithm affecting a variable to the particles, called the k-local identifier, in such a way that particles at close distance have each a different k-local identifier. For all the presented algorithms, the particles only need a O(1)-memory space