Abstract
This 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.
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Clark, C.M., Morton, R., Bekey, G.A. (2009). Altruistic Relationships for Optimizing Task Fulfillment in Robot Communities. In: Asama, H., Kurokawa, H., Ota, J., Sekiyama, K. (eds) Distributed Autonomous Robotic Systems 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00644-9_23
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DOI: https://doi.org/10.1007/978-3-642-00644-9_23
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