Using a pocket-filling strategy for distributed reconfiguration of a system of hexagonal metamorphic robots in an obstacle-cluttered environment

Abstract

We address the problem of reconfiguration planning for a metamorphic robotic system composed of a large number of hexagonal mobile robots. Our objective is to develop an algorithm to plan the concurrent movement of individual robots over a lattice composed of identical robots, from an initial configuration I to a goal configuration G, when G contains one or more obstacles. The contribution of this paper is a deterministic motion planning algorithm to envelop multiple obstacles in an admissible set of goal configurations while eliminating the risk of module collision or deadlock. We developed a discrete event simulator to test our algorithms, and every admissible G tested was filled successfully. We include a full proof of correctness and analysis of our algorithm.

Curve shortening-inspired self-reconfiguration of heterogenous hexagonal-shaped modules toward a straight chain

“…send Achiev i 1,2 (k − ) = 1 and z i (k − ) ∈ G c i to each adjacent module; 8: else send Achiev i 1,2 (k − ) = 1 to each adjacent module; 9: end if 10: else send Achiev i 1,2 (k − ) = 0 and P i 1,2 (k − ) to each adjacent module; 11: end if 12: Phase II: detecting phase (lines [12][13][14][15][16][17][18][19] 13: if Achiev i 1,2 (k − ) = 0, z i (k − ) ∈ B i (k − ) and z 1,2 ∈Int 1,2 (k − ) then 14:…”

“…Related works with respect to modules' motion planning can be found in refs. [ [13][14][15][16][17][18][19] ]. To date, several entities of modular systems have been developed.…”

“…send 'true' to each adjacent module; 7: end if 8: Phase II: detecting phase (lines[8][9][10][11][12][13][14][15] …”

A distributed reconfiguration strategy for target enveloping with hexagonal metamorphic modules

“…Nguyen et al [7] investigated the reconfiguration planning problem in the sight of module density. Later, Walter et al [8] [9] developed several algorithms that call for a centralized planning before a distributed algorithm being applied. In addition, [10] studied the state complex of a metamorphic robotic system by means of optimization theorem and [11] introduced an efficient planning algorithm for metamorphic robots to achieve self-reconfiguration.…”