Biologically-Inspired Multi-Robot Systems: Lessons from Nature on Storing Information, Reducing Complexity, and Building and Using Large Populations of Robots
Monday, November 19, 2012
PSF 173 [map]
Biological inspiration is at the very core of robotics. Long before anyone had ever heard of a “robot,” biological systems have captured the imagination of engineers and scientists, simultaneously providing inspiration for seemingly limitless possibilities and concrete examples of practical operation. For computer scientists, the goal is to understand the algorithmic nature of biological systems – to distill the fundamental constraints on sensing, communication, computation, and actuation that any computational system, natural or otherwise, must respect.
In this talk we take three themes from biology and apply them to multi-robot systems: storing information in physical configurations, using limited application-specific sensors, and leveraging population size to solve algorithmic problems.
- We present a distributed recovery algorithm to extract a multi-robot system from complex environments. The goal is to maintain network connectivity while allowing efficient recovery. Our approach uses a maximal-leaf spanning tree as a communication and navigation backbone, and routes robots along this tree to the goal.
- Angular coordinate systems can provide robots with useful network geometry from limited, low-cost hardware. We introduce “scale-free coordinates” as a coordinate system of intermediate power and design complexity. We show that it can estimate low-quality network geometry, but can still be used to build a useful motion controller with interesting limitations.
- We introduce the “r-one” robot, a low-cost design suitable for research, education, and outreach. We provide tales of joy and disaster from using 90 of these platforms for our research, education, and outreach.
James McLurkin is an Assistant Professor at Rice University in the Department of Computer Science. Current interests include using distributed computational geometry for multi-robot configuration estimation and control, and defining complexity metrics that quantify the relationships between algorithm execution time, inter-robot communication bandwidth, and robot speed. Previous positions include lead research scientist at iRobot corporation, where McLurkin was the manager of the DARPA-funded Swarm project. Results included the design and construction of 112 robots and distributed configuration control algorithms, including robust software to search indoor environments. He
holds a S.B. in Electrical Engineering with a Minor in Mechanical Engineering from M.I.T., a M.S. in Electrical Engineering from University of California, Berkeley, and a S.M. and Ph.D. in Computer Science from M.I.T.