Abstract
Dead reckoning error is a common problem in robotics that can be caused by multiple factors related to sensors or actuators. These errors potentially cause landmarks recorded by a robot to appear in a different location with respect to the actual position of the object. In a foraging scenario with a swarm of robots, this error will ultimately lead to the robots being unable to return successfully to the food source. In order to address this issue, we propose a computationally low-cost finite state machine strategy with which robots divide the total travelling distance into a variable number of segments, thus decreasing accumulated dead-reckoning error. The distance travelled by each robot changes according to the success and failure of exploration. Our approach is more flexible than using a previously used fixed size approach for the travel distance, thus allowing swarms greater flexibility and scaling to larger areas of operation.
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EB acknowledges financial support from CONACyT. JT is part sponsored by The Royal Society.
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Buchanan, E., Pomfret, A., Timmis, J. (2016). Dynamic Task Partitioning for Foraging Robot Swarms. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_10
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DOI: https://doi.org/10.1007/978-3-319-44427-7_10
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