A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots

Living cells exhibit both growth and regeneration of body tissues. Epigenetic Tracking (ET), models this growth and regenerative qualities of living cells and has been used to generate complex 2D and 3D shapes. In this paper, we present an ET based algorithm that aids a swarm of identically-programmed robots to form arbitrary shapes and regenerate them when cut. The algorithm works in a distributed manner using only local interactions and computations without any central control and aids the robots to form the shape in a triangular lattice structure. In case of damage or splitting of the shape, it helps each set of the remaining robots to regenerate and position themselves to build scaled down versions of the original shape. The paper presents the shapes formed and regenerated by the algorithm using the Kilombo simulator.Comment: 8 pages, 9 figures, GECCO-18 conferenc

Robust and Self-Repairing Formation Control for Swarms of Mobile Agents

We describe a decentralized algorithm for coordinating a swarm of identically-programmed mobile agents to spatially self-aggregate into arbitrary shapes using only local interactions. Our approach, called SHAPEBUGS, generates a consensus coordinate system by agents continually performing local trilaterations, and achieves shape formation by simultaneously allowing agents to disperse within the defined 2D shape using a gas expansion model. This approach has several novel features (1) agents can easily aggregate into arbitrary user-specified shapes, using a formation process that is independent of the number of agents (2) the system automatically adapts to influx and death of agents, as well as accidental displacement. We show that the consensus coordinate system is robust and provides reasonable accuracy in the face of significant sensor and movement error

R.: Robust and self-repairing formation control for swarms of mobile agents

We describe a decentralized algorithm for coordinating a swarm of identically-programmed mobile agents to spatially self-aggregate into arbitrary shapes using only local interactions. Our approach, called SHAPEBUGS, generates a consensus coordinate system by agents continually performing local trilaterations, and achieves shape formation by simultaneously allowing agents to disperse within the defined 2D shape using a Contained Gas Model. This approach has several novel features (1) agents can easily aggregate into arbitrary user-specified shapes, using a formation process that is independent of the number of agents (2) the system automatically adapts to influx and death of agents, as well as accidental displacement. We show that the consensus coordinate system is robust and provides reasonable accuracy in the face of significant sensor and movement error