Toward Swarm Robots Tracking: A Constrained Gaussian Condensation Filter Method
| Autor: | Cheng Xu, Jiawang Wan, Shihong Duan, Hang Wu |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030788100 ICSI (2) |
| DOI: | 10.1007/978-3-030-78811-7_13 |
| Popis: | Real-time high-precision navigation has a wide range of applications in scenarios. In practice, the measurement models are often non-linear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from the problem of accumulative errors, which cannot provide long-time high-precision services for localization. To solve the problem of arbitrary noise distribution, this paper proposes a Gaussian condensation filter to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse re-sampling-based Gaussian condensation (EER-GCF) filter, which establishes error-ellipses with different confidence probabilities and implements a re-sampling algorithm based on the sampling points’ geometrical positions. Furthermore, a cooperative Gaussian condensation filter based on error-ellipse re-sampling (CEER-GCF) is proposed to enhance information fusion in the swarm robots network. This study accomplishes swarm robots tracking based on spatial-temporal constraints to enhance tracking accuracy. Experiment results show that the accuracy of EER-GCF reaches 0.80 m, while CEER-GCF achieves a localization accuracy of 0.27 m. |
| Databáze: | OpenAIRE |
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