Abstract
This paper explores the parameter estimation problem for a differential-drive agricultural vehicle with unknown parameters. The differential-drive configuration is commonly used in robotic applications as well as in large agricultural machinery such as harvesters. We use simulation scenarios to compare the performance of two dual filters for parameter and state estimation: the Dual Liu and West filter (D-L&WF) and a novel Dual Merging particle filter (D-MPF). Our initial results indicate a slightly better performance of the D-MPF and we discuss the limitations and advantages of each filter. Dual-particle filtering techniques offer a great opportunity for applications to agricultural machinery, and to the best of the author’s knowledge, these techniques have not been previously investigated in this domain of application.
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