Evolving Collision Avoidance on Autonomous Robots

by: Lukas König, Hartmut Schmeck

Biologically-Inspired Collaborative Computing (2008), pp. 85-94.

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Abstract

Utilizing the collective behavior of a population of interacting individuals, based on rather simple local algorithms, is a promising approach for achieving complex goals. We use an onboard online evolutionary model, based on finite Moore automata, to develop collective behavior in an artificial swarm of micro-robots. Experiments have been made in simulation to achieve Collision Avoidance. The model is shown to be capable to generate the desired behavior and we present experiments for adjusting the parameters of the evolutionary optimization.

@incollection{citeulike:2988285, abstract = {Utilizing the collective behavior of a population of interacting individuals, based on rather simple local algorithms, is a promising approach for achieving complex goals. We use an onboard online evolutionary model, based on finite Moore automata, to develop collective behavior in an artificial swarm of micro-robots. Experiments have been made in simulation to achieve Collision Avoidance. The model is shown to be capable to generate the desired behavior and we present experiments for adjusting the parameters of the evolutionary optimization.}, author = {K\"{o}nig, Lukas and Schmeck, Hartmut }, citeulike-article-id = {2988285}, doi = {http://dx.doi.org/10.1007/978-0-387-09655-1\_8}, journal = {Biologically-Inspired Collaborative Computing}, keywords = {evolutionary-computation, metaheuristic, path, robot, swarm-intelligence}, pages = {85--94}, posted-at = {2008-07-11 14:34:29}, priority = {2}, title = {Evolving Collision Avoidance on Autonomous Robots}, url = {http://dx.doi.org/10.1007/978-0-387-09655-1\_8}, year = {2008} }

RIS record

TY - CHAP ID - citeulike:2988285 L3 - citeulike-article-id:2988285 TI - Evolving Collision Avoidance on Autonomous Robots JF - Biologically-Inspired Collaborative Computing SP - 85 EP - 94 N2 - Utilizing the collective behavior of a population of interacting individuals, based on rather simple local algorithms, is a promising approach for achieving complex goals. We use an onboard online evolutionary model, based on finite Moore automata, to develop collective behavior in an artificial swarm of micro-robots. Experiments have been made in simulation to achieve Collision Avoidance. The model is shown to be capable to generate the desired behavior and we present experiments for adjusting the parameters of the evolutionary optimization. KW - evolutionary-computation KW - metaheuristic KW - path KW - robot KW - swarm-intelligence AU - König, Lukas AU - Schmeck, Hartmut PY - 2008/// UR - http://dx.doi.org/10.1007/978-0-387-09655-1_8 ER -

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