A Persistent Decentralized Massively Scalable Mobile-Agent Based Evolutionary Algorithm for Optimizing Robotic Gaits

Multi-robot and modular robotic systems present a novel robotics paradigm: They can accomplish a wide variety of tasks using different physical configurations and control laws; they can be cheaper than traditional robotics because identical modules can be mass produced; they form fault-tolerant systems because malfunctioning modules can easily be swapped out; and their available computing power continues to increase exponentially. These factors can make autonomous modular robots ideal for usage in remote areas where a single set of robots might have to perform a variety of impromptu tasks. For instance, these robotic systems might be used for search-and-rescue, or deep-sea and space exploration missions. Creating control laws for a cluster of modular robots with an arbitrary morphology presents an interesting challenge. Ideally, the robot morphologies and control laws for arbitrary morphologies should be generated dynamically by the robotic systems themselves for cases where human input is minimal or nonexistent. Among these controls is the locomotion of these modular robots, or robotic gaits.;A Persistent Distributed Agent-Based Genetic Algorithm (PDABGA) has been designed and tested towards the goal of dynamically generating robotic gaits for a cluster of modular robots in the course of this research. PDABGA is based on an agency framework called Mobile-C, an IEEE Foundation for Intelligent Physical Agents standards compliant mobile agent framework. PDABGA leverages innate features of the mobile agent framework, such as persistence, computational distribution, and robustness. To generate optimal gaits for arbitrary robot morphologies, PDABGA augments agents with the core principles of the Genetic Algorithm: sexual reproduction among a population of candidate solutions. The system has been validated to show that it can leverage multiple cooperating computing nodes to generate high quality results.