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The Ant and the Trap: Evolution of Ant-Inspired Obstacle Avoidance in a Multi-Agent Robotic System (Master's Thesis Defense)

April 3, 2015

Master's Thesis Defense

Karl A Stolleis

Where: Farris Engineering Center Room 107 (Room Changed) 
When: April 3, 2015, 1:00 pm

Dr. Melanie Moses (chair) - Computer Science
Dr. Lydia Tapia - Computer Science
Dr. Rafael Fierro - Electrical and Computer Engineering

Title: The Ant and the Trap: Evolution of Ant-Inspired Obstacle Avoidance in a Multi-Agent Robotic System

Abstract:
Interest in swarm robotics has been increasing with each passing year. Research into use of biological systems as a model for swarm robotics has also increased steadily to date. In the creation of the iAnt robot we combined these two ideas and added the ability to evolved behaviors with a genetic algorithm. Although swarm robotics is still a loosely defined field one of the included hallmarks is multiple robots cooperating to complete a given task. The use of multiple robots means increased cost for research, scaling often linearly with the number of robots. We set out to create a system with the previously described capabilities while lowering the entry cost by building simple, cheap robots able to operate outside of a dedicated lab environment. Obstacle avoidance has long been a necessary component of robot systems. Avoiding collisions is also a difficult problem and has been studied for many years. As part of moving the iAnt further and further into the real-world we eventually had to incorporate a method of obstacle avoidance. Our hypothesis is that use of biological methods including evolution, stochastic movments and stygmergic trails into the iAnt Central Place Foraging Algorithm (CPFA) could result in robot behaviors suited to navigating obstacle-filled environments. The result is a modification of the CPFA to include pheromone trails, CPFA-Trails or CPFAT. This thesis first demonstrates the low-cost, simple and robust design of the physical iAnt robot. Secondly we will demonstrate the adaptability of the the system to evolve and succeed in an obstacle-laden environment.