Visual Pursuit Control based on Gaussian Processes for Switched Motion Trajectories
This paper received the "Best Student Paper Award"
Abstract:
This paper considers a scenario of pursuing a moving target that may switch behaviors due to external factors in a dynamic environment by motion estimation using visual sensors. First, we present an improved Visual Motion Observer with switched Gaussian Process models for an extended class of target motion profiles. We then propose a pursuit control law with an online method to estimate the switching behavior of the target by the GP model uncertainty. Next, we prove ultimate boundedness of the control and estimation errors for the switch in target behavior with high probability. Finally, a Digital Twin simulation demonstrates the effectiveness of the proposed switching estimation and control law to prove applicability to real world scenarios.
Cite as:
M. Omainska, J. Yamauchi and M. Fujita, "Visual Pursuit Control based on Gaussian Processes with Switched Motion Trajectories", Proc. of the 9th IFAC Symposium on Mechatronic Systems (MS-MoViC), pp. 208-213, 2022.
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