In this work we study the DMP spatial scaling in the Cartesian space. The DMP framework is claimed to have the ability to generalize learnt trajectories to new initial and goal positions, maintaining the desired kinem...
ISBN:
(数字)9781728160757
ISBN:
(纸本)9781728160764
In this work we study the DMP spatial scaling in the Cartesian space. The DMP framework is claimed to have the ability to generalize learnt trajectories to new initial and goal positions, maintaining the desired kinematic pattern. However we show that the existing formulations present problems in trajectory spatial scaling when used in the Cartesian space for a wide variety of tasks and examine their cause. We then propose a novel formulation alleviating these problems. Trajectory generalization analysis, is performed by deriving the trajectory tracking dynamics. The proposed formulation is compared with the existing ones through simulations and experiments on a KUKA LWR 4+ robot.
In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed. The control scheme is based only on the classified point cloud obtained by the in-hand camera attached ...
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ISBN:
(纸本)9798350309805
In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed. The control scheme is based only on the classified point cloud obtained by the in-hand camera attached to the robot's end effector. It is shown that the proposed controller reaches in the vicinity of the object progressively unveiling the neighborhood of each visible point of the object of interest. It can therefore potentially achieve the complete unveiling of the object. The proposed control scheme is evaluated through simulations and experiments with a UR5e robot with an in-hand RealSense camera on a mock-up vine setup for unveiling the stem of a grape cluster.
DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patter...
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In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collabo...
In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the reliance on perception systems which exhibit delays and errors. Our scheme is able to successfully avoid obstacles, while remaining compliant in the entirety of the executed task. Therefore, in case of unexpected collisions due to perception system errors, the robot remains safe for humans and its environment. Our approach is validated through experiments with simulated and real obstacles utilizing a 7-dof KUKA LBR iiwa robotic manipulator.
DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patter...
DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patterns that take into account not only the initial and target position but also features relating to the overall structure and layout of the scene. To make DMP applicable to a wider range of tasks and further automate their use, we design a framework combining deep residual networks with DMP, that can encapsulate different motion patterns of a planar task, provided through human demonstrations on the RGB image plane. We can then automatically infer from new raw RGB visual input the appropriate DMP parameters, i.e. the weights that determine the motion pattern and the initial/target positions. We compare our method against another SoA method for inferring DMP from images and carry out experimental validations in two different planar tasks.
In this work a passivity based control scheme to manipulate large objects supported on planar surfaces by bimanual robots is introduced. A control input is designed to accomplish the rotation and displacement of the o...
In this work a passivity based control scheme to manipulate large objects supported on planar surfaces by bimanual robots is introduced. A control input is designed to accomplish the rotation and displacement of the object to a desired pose without assuming any knowledge of the object dynamics and friction dynamics between the object and the supporting surface. Passivity arguments are used to rigorously prove its stability. The proposed method is evaluated in simulations with friction forces simulated using the elastoplastic model. Experimental results performed on a bimanual robot with two 7-dof KUKA LBR iiwa 7 arms manipulating a variety of large objects verify the theoretical results.
In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed. The control scheme is based only on the classified point cloud obtained by the in-hand camera attached ...
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In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a ...
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This paper deals with the topic of qLPV state-space model based control design in which LMIs are used to optimize the multi-objective control performance. In this paper we investigate how the convex hull of the polyto...
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ISBN:
(纸本)9789549260014
This paper deals with the topic of qLPV state-space model based control design in which LMIs are used to optimize the multi-objective control performance. In this paper we investigate how the convex hull of the polytopic model influences the state value estimation of the LMI based control design. We examine these influences through the control design of the two dimensional aeroelastic system's example. First we define various TP type polytopic model representations of a wing section whose vertices define different convex hulls. In the second step we investigate how these models lead to different performances of the state value estimation.
Over the last years significant effort has been made to improve the performance of speech recognition. The Fisher Kernel has been suggested as good ways to combine and underlying generative model in the feature space ...
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