The Advantages of Velocity Control for Reactive Robot Motion

Zelenak, Andy; Peterson, C. E.; Thompson, J.G.; Pryor, Mitch

doi:10.1115/dscc2015-9713

Cited by 5 publications

(4 citation statements)

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“…As a side note, although the UR3 robot is controlled in position (at the hardware level), we should see more velocity-controlled robots in the future as the need for real-time applications increases [10]. The same author of this paper made an open-source ROS package, jog arm, which allows to control the speed of the joints or the end-effector in real time.…”

Section: Real-time Robot Controlmentioning

confidence: 99%

Real-Time Gestural Control of Robot Manipulator Through Deep Learning Human-Pose Inference

Martin¹,

Moutarde²

2019

Lecture Notes in Computer Science

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With the raise of collaborative robots, human-robot interaction needs to be as natural as possible. In this work, we present a framework for real-time continuous motion control of a real collaborative robot (cobot) from gestures captured by an RGB camera. Through deep learning existing techniques, we obtain human skeletal pose information both in 2D and 3D. We use it to design a controller that makes the robot mirror in real-time the movements of a human arm or hand.

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Section: Real-time Robot Controlmentioning

confidence: 99%

Real-Time Gestural Control of Robot Manipulator Through Deep Learning Human-Pose Inference

Martin¹,

Moutarde²

2019

Lecture Notes in Computer Science

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“…This conclusion matches with the observations that position based force control would either vibrate a lot at steady state or introduce large contact force and bouncing during contact transition, which would often trigger the built-in protective stop of the robot. Further details about the advantages of velocity based force control can be found in [58,59].…”

Section: )mentioning

confidence: 99%

“…This conclusion matches with the observations that position based force control would either vibrate a lot at steady state or introduce large contact force and bouncing during contact transition. Further details about the advantages of velocity based force control can be found in[58,59]. 2.2.4 Stability Analysis2.2.4.1 Pose Tracking StabilityPose tracking employs joint velocity to track a given end-effector pose.…”

mentioning

confidence: 99%

Robotic Surface Finishing of Curved Surfaces: Real-Time Identification of Surface Profile and Control

Wen

Pagilla

2018

Volume 3: Manufacturing Equipment and Systems

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An efficient strategy for robotic surface finishing of curved surfaces that includes real-time identification of the surface profile, control, and implementation is presented in this paper. Real-time identification of the surface profile in the robot base frame is accomplished by employing a proximity laser sensor mounted on the robot end-effector. This surface profile description allows us to generate trajectories for both motion and force control as it provides the surface normal at each point of the surface. Using the surface profile, a trajectory is generated that would orient the surface finishing tool to the local normal of the surface. An algorithm for simultaneous position and force control is developed for surface finishing of curved surfaces. The integrated robotic surface finishing system consists of a UR5 robot and a custom end-effector that includes a force/torque sensor, an electromechanical sander, and the proximity laser sensor. Robot Operating System (ROS) is utilized for real-time implementation, which would enable easy migration of the developed tools if other industrial robots are used. The effectiveness of the strategy is evaluated by conducting a number of experiments for flat and curved surfaces. A representative sample of results on force regulation and surface finishing are presented and discussed.

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“…In [ 23 ], the authors presented a LCM-CSM variant where the tracking of moving objects was possible using positioning commands; however, no CSM velocity-based variant has been developed to this day. Velocity-based control could prove to be more energy efficient, suitable for low control frequencies, and robust to variations on the control signal [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

Loredo

Maya

González

et al. 2022

Sensors

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It is a challenging task to track objects moving along an unknown trajectory. Conventional model-based controllers require detailed knowledge of a robot’s kinematics and the target’s trajectory. Tracking precision heavily relies on kinematics to infer the trajectory. Control implementation in parallel robots is especially difficult due to their complex kinematics. Vision-based controllers are robust to uncertainties of a robot’s kinematic model since they can correct end-point trajectories as error estimates become available. Robustness is guaranteed by taking the vision sensor’s model into account when designing the control law. All camera space manipulation (CSM) models in the literature are position-based, where the mapping between the end effector position in the Cartesian space and sensor space is established. Such models are not appropriate for tracking moving targets because the relationship between the target and the end effector is a fixed point. The present work builds upon the literature by presenting a novel CSM velocity-based control that establishes a relationship between a movable trajectory and the end effector position. Its efficacy is shown on a Delta-type parallel robot. Three types of experiments were performed: (a) static tracking (average error of 1.09 mm); (b) constant speed linear trajectory tracking—speeds of 7, 9.5, and 12 cm/s—(tracking errors of 8.89, 11.76, and 18.65 mm, respectively); (c) freehand trajectory tracking (max tracking errors of 11.79 mm during motion and max static positioning errors of 1.44 mm once the object stopped). The resulting control cycle time was 48 ms. The results obtained show a reduction in the tracking errors for this robot with respect to previously published control strategies.

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The Advantages of Velocity Control for Reactive Robot Motion

Cited by 5 publications

References 0 publications

Real-Time Gestural Control of Robot Manipulator Through Deep Learning Human-Pose Inference

Real-Time Gestural Control of Robot Manipulator Through Deep Learning Human-Pose Inference

Robotic Surface Finishing of Curved Surfaces: Real-Time Identification of Surface Profile and Control

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

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