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Cache consensus: rapid object sorting by a robotic swarm

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Abstract

We present a new method which allows a swarm of robots to sort arbitrarily arranged objects into homogeneous clusters. In the ideal case, a distributed robotic sorting method should establish a single homogeneous cluster for each object type. This can be achieved with existing methods, but the rate of convergence is considered too slow for real-world application. Previous research on distributed robotic sorting is typified by randomised movement with a pick-up/deposit behaviour that is a probabilistic function of local object density. We investigate whether the ability of each robot to localise and return to remembered places can improve distributed sorting performance. In our method, each robot maintains a cache point for each object type. Upon collecting an object, it returns to add this object to the cluster surrounding the cache point. Similar to previous biologically inspired work on distributed sorting, no explicit communication between robots is implemented. However, the robots can still come to a consensus on the best cache for each object type by observing clusters and comparing their sizes with remembered cache sizes. We refer to this method as cache consensus. Our results indicate that incorporating this localisation capability enables a significant improvement in the rate of convergence. We present experimental results using a realistic simulation of our targeted robotic platform. A subset of these experiments is also validated on physical robots.

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Notes

  1. The term ‘pose’ implies the specification of both position and orientation with respect to some reference frame.

  2. http://www.surveyor.com

  3. http://jbox2d.org.

  4. See http://matplotlib.org/. The griddata function is used for interpolation.

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Acknowledgments

Thanks to Paul Gillard for helpful discussions and support in diagnosing myriad hardware problems. WB gratefully acknowledges funding from NSERC under Discovery Grant RGPIN 283304-2012.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Engineering & Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Andrew Vardy

  2. Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Gregory Vorobyev & Wolfgang Banzhaf

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  1. Andrew Vardy
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  2. Gregory Vorobyev
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  3. Wolfgang Banzhaf
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Correspondence to Andrew Vardy.

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Vardy, A., Vorobyev, G. & Banzhaf, W. Cache consensus: rapid object sorting by a robotic swarm. Swarm Intell 8, 61–87 (2014). https://doi.org/10.1007/s11721-014-0091-5

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