Real-Time Evolution of iAnt Robot Foraging Strategies

Abstract

Central-place foraging is a canonical task in swarm robotics. For this task, robots are programmed to search an area for resources and aggregate these resources at a central location. Foraging can be instantiated in a number of real-world applications, such as hazardous waste clean-up, search and rescue, and in-situ resource utilization. We extend our prior work by using coevolutionary methods to evolve iAnt robot swarm foraging strategies in real robots and in real time. Each robot maintains a private agent-based simulation, which models the physical environment according to the robot’s own approximation of the realworld resource distribution. The robot uses a private genetic algorithm (GA) to evolve parameters for a central-place foraging algorithm (CPFA) which maximize the foraging success of the simulated agents; it then employs these evolved parameters as a foraging strategy in the real world. In addition to evolving individual foraging strategies, the entire swarm executes a distributed GA to evolve a population of resource distribution approximations that is shared across all robots. In this way, coevolution evolves both the foraging strategy and the resource distribution approximation for each robot. Over time, this allows the swarm to adapt its behavior to previously unknown environments.