12,814 research outputs found
A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation
Symmetric bi-manual manipulation is essential for various on-orbit operations
due to its potent load capacity. As a result, there exists an emerging research
interest in the problem of achieving high operation accuracy while enhancing
adaptability and compliance. However, previous works relied on an inefficient
algorithm framework that separates motion planning from compliant control.
Additionally, the compliant controller lacks robustness due to manually
adjusted parameters. This paper proposes a novel Learning-based Adaptive
Compliance algorithm (LAC) that improves the efficiency and robustness of
symmetric bi-manual manipulation. Specifically, first, the algorithm framework
combines desired trajectory generation with impedance-parameter adjustment to
improve efficiency and robustness. Second, we introduce a centralized
Actor-Critic framework with LSTM networks, enhancing the synchronization of
bi-manual manipulation. LSTM networks pre-process the force states obtained by
the agents, further ameliorating the performance of compliance operations. When
evaluated in the dual-arm cooperative handling and peg-in-hole assembly
experiments, our method outperforms baseline algorithms in terms of optimality
and robustness.Comment: 12 pages, 10 figure
Cooperative Object Manipulation with Force Tracking on the da Vinci Research Kit
The da Vinci Surgical System is one of the most established robot-assisted surgery device commended for its dexterity and ergonomics in minimally invasive surgery. Conversely, it inherits disadvantages which are lack of autonomy and haptic feedback. In order to address these issues, this work proposes an industry-inspired solution to the field of force control in medical robotics. This approach contributes to shared autonomy by developing a controller for cooperative object manipulation with force tracking utilizing available manipulators and force feedback. To achieve simultaneous position and force tracking of the object, master and slave manipulators were assigned then controlled with Cartesian position control and impedance control respectively. Because impedance control requires a model-based feedforward compensation, we identified the lumped base parameters of mass, inertias, and frictions of a three degree-of-freedom double four-bar linkage mechanism with least squares and weighted least squares regression methods. Additionally, semidefinite programming was used to constrain the parameters to a feasible physical solution in standard parameter space. Robust stick-slip static friction compensation was applied where linear Viscous and Coulomb friction was inadequate in modeling the prismatic third joint. The Robot Operating System based controller was tested in RViz to check the cooperative kinematics of up to three manipulators. Additionally, simulation with the dynamic engine Gazebo verified the cooperative controller applying a constant tension force on a massless spring-damper virtual object. With adequate model feedback linearization, the cooperative impedance controller tested on the da Vinci Research Kit yielded stable tension force tracking while simultaneously moving in Cartesian space. The maximum force tracking error was +/- 0.5 N for both a compliant and stiff manipulated object
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