PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Study of Mechanisms for Improving Robotic Group Performance
Avi Rosenfeld, Gal Kaminka, Sarit Kraus and Onn Shehory
Artificial Intelligence Volume 172, Number 6--7, pp. 633-655, 2008.


Many collaborative multi-robot application domains have limited areas of operation that cause spatial conflicts between robotic teammates. These spatial conflicts can cause the team’s productivity to drop with the addition of robots. This phenomenon is impacted by the coordination methods used by the team-members, as different coordination methods yield radically different productivity results. However, selecting the best coordination method to be used by teammates is a formidable task. This paper presents techniques for creating adaptive coordination methods to address this challenge. We first present a combined coordination cost measure, CCC, to quantify the cost of group interactions. Our measure is useful for facilitating comparison between coordination methods, even when multiple cost factors are considered. We consistently find that as CCC values grow, group productivity falls. Using the CCC, we create adaptive coordination techniques that are able to dynamically adjust the efforts spent on coordination to match the number of perceived coordination conflicts in a group. We present two adaptation heuristics that are completely distributed and require no communication between robots. Using these heuristics, robots independently estimate their combined coordination cost (CCC), adjust their coordination methods to minimize it, and increase group productivity. We use simulated robots to perform thousands of experiment trials to demonstrate the efficacy of our approach. We show that using adaptive coordination methods create a statistically significant improvement in productivity over static methods, regardless of the group size.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3844
Deposited By:Sarit Kraus
Deposited On:25 February 2008