Altruistic Relationships for Optimizing Task Fulfillment in Robot CommunitiesDistributed Autonomous Robot Systems 8
AbstractThis paper introduces the concept of a multi-robot community in which multiple robots must fulfill their individual tasks while operating in a shared environment. Unlike typical multi-robot systems in which global cost functions are minimized while accomplishing a set of global tasks, the robots in this work have individual tasks to accomplish and individual cost functions to optimize (e.g. path length or number of objects to gather). A strategy is presented in which a robot may choose to aid in the completion of another robot’s task. This type of “altruistic” action leads to evolving altruistic relationships between robots, and can ultimately result in a decrease in the individual cost functions of each robot. However, altruism with respect to another robot must be controlled such that it allows a relationship where both robots are altruistic, but protects an altruistic robot against a selfish robot that does not help others. A quantitative description of this altruism is presented, along with a law for controlling an individuals altruism. With a linear model of the altruism dynamics, altruistic relationships are proven to grow when robots are altruistic, but protect an altruistic robot from a selfish robot. Results of task planning simulations are presented that highlight the decrease in individual robot cost functions, as well as evolutionary trends of altruism between robots.
Citation InformationChristopher M. Clark, Ryan Morton and George A. Bekey. "Altruistic Relationships for Optimizing Task Fulfillment in Robot Communities" Distributed Autonomous Robot Systems 8 (2008) p. 261 - 270
Available at: http://works.bepress.com/cmclark/25/