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Model Predictive Interaction Control for Robotic Manipulation Tasks
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AI
DOI：10.1109/TRO.2022.3196607.png)
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Abstract
En 中文
This article presents the concept of model predictive interaction control (MPIC) as a generic, flexible, and comprehensive approach for robotic manipulation tasks. MPIC is based on the repetitive solution of an optimal control problem that includes a robot model for motion prediction as well as an interaction model for force prediction. In order to handle both elastic and rigid contact situations, a cascaded approach with low-level PD control is adopted, which allows to combine the linear-elastic environment model and the limited controller stiffness. Due to its flexibility, MPIC can be favorably used for realizing the elementary manipulation primitives (MP) within a hierarchical task planning framework, where each MP corresponds to a particular parameterization of the cost function and the constraints. The control methodology and the manipulation approach are evaluated in simulations and experiments using a 7-degree-of-freedom industrial robot.
Keywords:
Robots
Force
Task analysis
Predictive models
Behavioral sciences
Dynamics
Impedance
Model predictive control (MPC)
robot control
robotic manipulation
Journal
IF:
10.5
Papers:
3.3K
Citations:
2.8W