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

Distributed Swarm Formation Using Mobile Agents

This chapter presents decentralized control algorithms for composing formations of swarm robots. The robots are connected by communication networks. They initially do not have control program to compose formations. Control programs that implement our algorithm are introduced later from outside as mobile software agents. Our controlling algorithm is based on the pheromone communication of social insects such as ants. We have implemented the ant and the pheromone as mobile software agents. Ant agents control the robots. Each ant agent has partial information about the formation it is supposed to compose. The partial information consists of relative locations with neighbor robots that are cooperatively composing the formation. Once the ant agent detects an idle robot, it occupies that robot and generates the pheromone agent to attract other ant agents to the location for neighbor robots. Then the pheromone agent repeatedly migrates to other robots to diffuse attracting information. Once the pheromone agent reaches the robot with an ant agent, the ant agent migrates to the robot closest to the location pointed by the pheromone agent and then drives the robot to the location. We have implemented simulators based on our algorithm and conducted experiments to demonstrate the feasibility of our approach

Robustness Analysis and Failure Recovery of a Bio-Inspired Self-Organizing Multi-Robot System

Jin Y, Guo H, Meng Y. Robustness Analysis and Failure Recovery of a Bio-Inspired Self-Organizing Multi-Robot System. In: 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems. IEEE; 2009: 154-164.Biological systems can generate robust and complex behaviors through limited local interactions in the presence of large amount of uncertainties. Inspired by biological organisms, we have proposed a gene regulatory network (GRN) based algorithm for self-organizing multiple robots into different shapes. The self-organization process is optimized using a genetic algorithm. This paper focuses on the empirical analysis of robustness of the self-organizing multi-robot system to the changes in tasks, noise in the robot system and changes in the environment. We investigate the performance variation when the system is optimized for one shape and then employed for a new shape. The influence of noise in sensors for distance detection and self-localization on the final positioning error is also examined. In case of a complete self-localization failure, we introduce a recovery algorithm based on trilateration combined with a Kalman filter. Finally, we study the system's performance when the number of robots changes and when there are moving obstacles in the field. Various simulation results demonstrate that the proposed algorithm is efficient in shape formation and that the self-organizing system is robust to sensory noise, partial system failures and environmental changes

Regenerative Patterning in Swarm Robots: Mutual Benefits of Research in Robotics and Stem Cell Biology

This paper presents a novel perspective of Robotic Stem Cells (RSCs), defined as the basic non-biological elements with stem cell like properties that can self-reorganize to repair damage to their swarming organization. Self here means that the elements can autonomously decide and execute their actions without requiring any preset triggers, commands, or help from external sources. We develop this concept for two purposes. One is to develop a new theory for self-organization and self-assembly of multi-robots systems that can detect and recover from unforeseen errors or attacks. This self-healing and self-regeneration is used to minimize the compromise of overall function for the robot team. The other is to decipher the basic algorithms of regenerative behaviors in multi-cellular animal models, so that we can understand the fundamental principles used in the regeneration of biological systems. RSCs are envisioned to be basic building elements for future systems that are capable of self-organization, self-assembly, self-healing and self-regeneration. We first discuss the essential features of biological stem cells for such a purpose, and then propose the functional requirements of robotic stem cells with properties equivalent to gene controller, program selector and executor. We show that RSCs are a novel robotic model for scalable self-organization and self-healing in computer simulations and physical implementation. As our understanding of stem cells advances, we expect that future robots will be more versatile, resilient and complex, and such new robotic systems may also demand and inspire new knowledge from stem cell biology and related fields, such as artificial intelligence and tissue engineering

Feedback-Based Inhomogeneous Markov Chain Approach To Probabilistic Swarm Guidance

This paper presents a novel and generic distributed swarm guidance algorithm using inhomogeneous Markov chains that guarantees superior performance over existing homogeneous Markov chain based algorithms, when the feedback of the current swarm distribution is available. The probabilistic swarm guidance using inhomogeneous Markov chain (PSG–IMC) algorithm guarantees sharper and faster convergence to the desired formation or unknown target distribution, minimizes the number of transitions for achieving and maintaining the formation even if the swarm is damaged or agents are added/removed from the swarm, and ensures that the agents settle down after the swarm’s objective is achieved. This PSG–IMC algorithm relies on a novel technique for constructing Markov matrices for a given stationary distribution. This technique incorporates the feedback of the current swarm distribution, minimizes the coefficient of ergodicity and the resulting Markov matrix satisfies motion constraints. This approach is validated using Monte Carlo simulations of the PSG–IMC algorithm for pattern formation and goal searching application