A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots
This work addresses shape formation and regeneration for swarm robotics, offering a decentralized approach that could enhance robustness in applications like environmental monitoring or disaster response, though it appears incremental as it builds on existing Epigenetic Tracking models.
The paper tackles the problem of enabling a swarm of robots to form and regenerate arbitrary shapes in a distributed manner, achieving this through an Epigenetic Tracking-based algorithm that allows robots to self-organize into triangular lattice structures and regenerate scaled-down versions after damage, as demonstrated in simulations.
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.