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For a wearable robotic arm to autonomously assist a human, it has to be able to stabilize its end-effector in light of the human’s independent activities. This paper presents a method for stabilizing the end-effector in planar assembly and pick-and-place tasks. Ideally, given an accurate positioning of the end effector and the wearable robot attachment point, human disturbances could be compensated by using a simple feedback control strategy. Realistically, system delays in both sensing and actuation suggest a predictive approach. In this work, we characterize the actuators of a wearable robotic arm and estimate these delays using linear models. We then consider the motion of the human arm as an autoregressive process to predict the deviation in the robot’s base position at a time horizon equivalent to the estimated delay. Generating set points for the end-effector using this predictive model, we report reduced position errors of 19.4% (x) and 20.1% (y) compared to a feedback control strategy without prediction.
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Predictive Control of the KINOVA Gen3 Robotic Manipulator Using a Nonlinear Model
Abstract In contemporary manufacturing processes, reliable but efficient pick-and-place robots are frequently used. The automation and optimization of the pick and place procedures utilizing various path-planning approaches thereby support the expansion of application areas. Yet, the design of a controller faces significant difficulties due to the nonlinearities inherent in robotic manipulators and the unpredictable nature of the ambient factors. In place of the classic model predictive control (MPC), this paper presents the application of the Nonlinear Model Predictive Controller (NLMPC) as an acceptable control mechanism for real-time optimization and robust stability of the KINOVA Gen3 robotic arm. The developed NLMPC-based method ensures that the robotic arm does not run into obstacles in the workplace or with itself while reaching, gripping, selecting, and placing the necessary items. To acquire the control input trajectory, the optimization in NLMPC is solved repeatedly. When input constraints are available, the modeled system tracks reference trajectories to achieve the aim of recognizing and organizing distinct objects. After the NLMPC is successfully developed, a simulation environment is built and finally brought to life by combining all the processes into one using a MATLAB Stateflow chart.
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- Award ID(s):
- 2133630
- PAR ID:
- 10581265
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8763-9
- Format(s):
- Medium: X
- Location:
- New Orleans, Louisiana, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/IMECE2023-112777
