Draft:Social potential fields
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Social Potential Fields, developed in 1999 by John H. Reif and Hongyan Wang,[1] is one of the earliest models for Swarm Intelligence, developed for use for autonomous control of robot swarm systems that may consist of from hundreds to perhaps tens of thousands or more autonomous robots. It is the first paper to apply a potential field model to distributed autonomous multi-robot control. A Social Potential Field defines simple artificial force laws between pairs of robots or robot groups. These force laws are inverse-power force laws, incorporating both attraction and repulsion, similar but more general compared to the force laws found in molecular dynamics. As one of simplest examples, they define a force law where attraction dominates over long distances and repulsion dominates or short distances. The force laws can be distinct between various robots. An individual robot's motion is controlled by the resultant artificial force imposed by other robots and other components of the system. The Social Potential Fields approach is distributed since the force calculations and motion control can be done in an asynchronous and distributed manner. Using specially tailored force laws, they demonstrate complex behaviors and what might be viewed as "social relations' among robots. Therefore, the model was termed "Social Potential Fields". They demonstrated by computer simulations that the method can yield interesting and useful behaviors among robots, including clustering, guarding, escorting, patrolling, etc. The 1999 paper envisioned many industrial and military applications such as assembling, transporting, hazardous inspection. patrolling, and military control of swarm systems. Their simulations showed the social potential fields method is robust in that the method can tolerate errors in sensors and actuators. The Social Potential Fields paper also extended the social potential fields model to use spring laws as force laws.
References
- ^ Reif, John; Wang, Hongyan (1999). "Social potential fields: A distributed behavioral control for autonomous robots" (PDF). Robotics and Autonomous Systems. 27 (3): 171–194. doi:10.1016/S0921-8890(99)00004-4.
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