Abstract
Swarm localization, cooperative robot localization in swarm robotics, has a significant role in a swarm robot system and requires much deliberation for its estimation scheme. As such, designing stochastic hidden Markov model, in a way a variety of conditionally dependent, observed random variables such as measurements are effectively chosen and properly integrated into the probability distribution of a belief, is very important. In this paper, we propose swarm EKF localization, a hybrid of two inference algorithms, extended Kalman filter (EKF) and belief propagation (BP), with a capability of choosing how many dependencies of random variables are exploited in inference using the concept of neighborhood. Also, this paper presents a numerical experiment result of swarm EKF localizations. In conclusion, we could confirm that 2nd order neighborhood EKF has an overall better estimation performance compared to conventional 1st order neighborhood EKFs.
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© 2014 Springer International Publishing Switzerland
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Fukui, S., Naruse, K. (2014). Swarm EKF Localization for a Multiple Robot System with Range-Only Measurements. In: Kim, YT., Kobayashi, I., Kim, E. (eds) Soft Computing in Advanced Robotics. Advances in Intelligent Systems and Computing, vol 269. Springer, Cham. https://doi.org/10.1007/978-3-319-05573-2_9
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DOI: https://doi.org/10.1007/978-3-319-05573-2_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05572-5
Online ISBN: 978-3-319-05573-2
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