Software Framework for Robot-Assisted Large Structure Assembly

Chen, Shuyang; Peng, Yuan-Chi; Wason, John; Cui, Jinda; Saunders, Glenn; Nath, Shridhar; Wen, John T.

doi:10.1115/msec2018-6689

Cited by 8 publications

(7 citation statements)

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“…To facilitate the human teleoperation of the redundant manipulator, we develop the resolved velocity controller based on our work in [27] as below: miṅ q,αr,αp ||Jq − αv d || 2 + r (α r − 1) 2 + p (α p − 1) 2 (6) subject to equality constraints such as orientation control: h E (q) = 0, and inequality constraints such as joint limits: h I (q) > 0, where J is the Baxter left arm Jacobian andq is the resolved robot joint velocity for the user commanded velocity v d . And…”

Section: Resolved Velocity Controller For Human Teleoperationmentioning

confidence: 99%

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics

Chen

Wen

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Fast and precise motion control is important for industrial robots in manufacturing applications. However, some collaborative robots sacrifice precision for safety, particular for high motion speed. The performance degradation is caused by the inability of the joint servo controller to address the uncertain nonlinear dynamics of the robot arm, e.g., due to joint flexibility. We consider two approaches to improve the trajectory tracking performance through feedforward compensation. The first approach uses iterative learning control, with the gradient-based iterative update generated from the robot forward dynamics model. The second approach uses dynamic inversion to directly compensate for the robot forward dynamics. If the forward dynamics is strictly proper or is nonminimum-phase (e.g., due to time delays), its stable inverse would be non-causal. Both approaches require robot dynamical models. This paper presents results of using recurrent neural networks (RNNs) to approximate these dynamical modelsforward dynamics in the first case, inverse dynamics (possibly non-causal) in the second case. We use the bi-directional RNN to capture the noncausality. The RNNs are trained based on a collection of commanded trajectories and the actual robot responses. We use a Baxter robot to evaluate the two approaches. The Baxter robot exhibits significant joint flexibility due to the series-elastic joint actuators. Both approaches achieve sizable improvement over the uncompensated robot motion, for both random joint trajectories and Cartesian motion. The inverse dynamics method is particularly attractive as it may be used to more accurately track a user input as in teleoperation.

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Section: Resolved Velocity Controller For Human Teleoperationmentioning

confidence: 99%

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics

Chen

Wen

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…To further test the generalization capability of the trained NNs, we conduct another test of tracking 6-dimensional joint trajectories planned by MoveIt! for large structures assembly task [67] in RobotStudio. The comparison of tracking performance without and with NNs compensation is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Industrial Robot Trajectory Tracking Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Chen¹,

Wen²

2019

Preprint

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“…To further test the generalization capability of the trained NNs, we conducted another test of tracking 6-dimensional joint trajectories planned by MoveIt! for a large-structure assembly task [49] in RobotStudio. The comparison of tracking performance without and with NN compensation is shown in Figure 9.…”

Section: Multi-joint Motion Trackingmentioning

confidence: 99%

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Chen

Wen

2021

Robotics

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Fast and precise robot motion is needed in many industrial applications. Most industrial robot motion controllers allow externally commanded motion profiles, but the trajectory tracking performance is affected by the robot dynamics and joint servo controllers, to which users have no direct access and about which they have little information. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. This paper presents an approach for combining neural networks and iterative learning controls to improve the trajectory tracking performance for a multi-axis articulated industrial robot. For a given desired trajectory, the external command is iteratively refined using a high-fidelity dynamical simulator to compensate for the robot inner-loop dynamics. These desired trajectories and the corresponding refined input trajectories are then used to train multi-layer neural networks to emulate the dynamical inverse of the nonlinear inner-loop dynamics. We show that with a sufficiently rich training set, the trained neural networks generalize well to trajectories beyond the training set as tested in the simulator. In applying the trained neural networks to a physical robot, the tracking performance still improves but not as much as in the simulator. We show that transfer learning effectively bridges the gap between simulation and the physical robot. Finally, we test the trained neural networks on other robot models in simulation and demonstrate the possibility of a general purpose network. Development and evaluation of this methodology are based on the ABB IRB6640-180 industrial robot and ABB RobotStudio software packages.

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Software Framework for Robot-Assisted Large Structure Assembly

Cited by 8 publications

References 0 publications

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics

Neural-Learning Trajectory Tracking Control of Flexible-Joint Robot Manipulators with Unknown Dynamics

Industrial Robot Trajectory Tracking Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

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