Ph.D. Defense Announcement

          Distributed Evolution for Swarm Robotics
                  by Suranga Hettiarachchi

               Advisor: Dr. William M. Spears
                Computer Science Department
          University of Wyoming, Laramie, WY 82071

              Date: Wednesday, September 5, 2007
                       Time: 3:00PM
                       Room: EN1062

Abstract:

Traditional approaches to designing multi-agents systems are offline, in
simulation, and assume the presence of a global observer.  Artificial 
Physics (AP) or "Physicomimetics" can be used to self-organize swarms of
mobile robots into formations that move towards a goal.  Using an offline
approach, we extend the AP framework to moving formations through obstacle
fields.  We provide important metrics of performance that allows us to (a)
compare the utility of different generalized force laws in the artificial
physics framework, (b) examine trade-offs between different metrics, and (c)
provide a detailed method of comparison for future researchers in this area.

In the online, real world, a global observer may be absent, performance
feedback may be delayed or perturbed by noise, agents may only interact with
their local neighbors, and only a subset of agents may experience any form
of performance feedback. Under these constraints, designing multi-agent
systems is difficult.  We present a novel approach called "Distributed 
Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios" or 
DAEDALUS to address these issues, by mimicking more closely the actual 
dynamics of populations of agents moving and interacting in a (task) 
environment.

The thesis then merges DAEDALUS and AP by using obstacle avoidance as a
case study to illustrate the feasibility of DAEDALUS when the environment
changes.  We present empirical and practical results that address (a)
offline vs. online learning (b) obstructed perception, (c) homogeneous
vs. heterogeneous agent cooperation, and (d) implementation of obstacle
avoidance with real robots.

Weather and hardware permitting, a live outdoor demo of AP with obstacle
avoidance on real robots follows the defense.