We present in this article a pursuit controller with simultaneous data-based 3D target motion prediction and switching estimation by a 2D camera when a moving target may switch between multiple motion patterns. The switching target motion is modelled by a new Gaussian Process model for rigid body motions that can predict velocity fields based on observed target motion data and an online switching estimator. We further prove that the proposed methods for motion prediction and visual pursuit ensure stability and...
Publications
Journal
This paper considers vision-based cooperative control for robotic networks pursuing a target object based on distributed Gaussian processes. We consider a situation where networked multiple robots are learning unknown motion of the target as a Gaussian process from different datasets. In this scene, some robots may lose sight of the target due to the limited field of view. To address the issue, we introduce a notion of time varying visibility set. Then, we propose a control law based on a...
In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consider the situation where each drone has its own dataset, and learns the unknown target motion in a distributed manner. Second, we propose a control law using the distributed Gaussian processes, and show that the estimation and control errors are ultimately bounded. Furthermore, the effectiveness of the proposed method is verified first...
Conference
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...
In this paper, we propose a vision-based pursuit control law with uncertainty estimates of the target motion by Gaussian process (GP) regression. We consider a situation where a robot equipped with a visual sensor pursues a target whose velocity is unknown. First, we introduce a GP-based target motion estimation. In addition, we propose an observer- based controller that automatically tunes the feedback gains by quantifying the upper bound on the uncertainty of the target motion with a GP estimate. Second,...
In this paper, we propose a control law for camera-equipped drone networks to pursue a target rigid body with unknown motion based on distributed Gaussian process. First, we consider the situation where each drone has its own dataset, and learns the unknown target motion in a distributed manner. Second, we propose a control law using the distributed Gaussian processes, and show that the estimation and control errors are ultimately bounded. Furthermore, the effectiveness of the proposed method is verified first...
This paper considers a pursuit control based on cooperative target motion estimation by robotic networks equipped with visual sensors. First, we propose a cooperative pursuit control law with a vision-based observer using visual sensor networks, called networked visual motion observer. Then, we learn position dependent target motion by a Gaussian process and integrate it within the proposed control law. Second, we show that all rigid bodies converge to desired relative poses when at least one robot can obtain visual information...
In this paper, we propose an observer-based visual pursuit control law which integrates target motion learningvia Gaussian Process (GP). We consider two rigid bodies: a controlled rigid body with a visual sensor, and a targetrigid body whose velocity is unknown. Furthermore, a vision-based motion observer which estimates the target motionis introduced. Then, we propose an enhanced vision-based nonlinear observer and visual pursuit control which employtarget motion learning by GP, where the GP prediction is based on estimated relative rigid body...
Order picking is one of the most expensive tasks in warehouses nowadays and at the same time one of the hardest to automate. Technical progress in automation technologies however allowed for first robotic products on fully automated picking in certain applications. This paper presents a mobile order picking robot for retail store or warehouse order fulfillment on typical packaged retail store items. This task is especially challenging due to the variety of items which need to be recognized and manipulated...