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Collaborative robots should ideally use low torque actuators for passive safety reasons. However, some applications require these collaborative robots to reach deep into confined spaces while assisting a human operator in physically demanding tasks. In this paper, we consider the use of in-situ collaborative robots (ISCRs) that balance the conflicting demands of passive safety dictating low torque actuation and the need to reach into deep confined spaces. We consider the judicious use of bracing as a possible solution to these conflicting demands and present a modeling framework that takes into account the constrained kinematics and the effect of bracing on the endeffector compliance. We then define a redundancy resolution framework that minimizes the directional compliance of the end-effector while maximizing end-effector dexterity. Kinematic simulation results show that the redundancy resolution strategy successfully decreases compliance and improves kinematic conditioning while satisfying the constraints imposed by the bracing task. Applications of this modeling framework can support future research on the choice of bracing locations and support the formation of an admittance control framework for collaborative control of ISCRs under bracing constraints. Such robots can benefit workers in the future by reducing the physiological burdens that contribute to musculoskeletal injury.
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Reinforcement Learning Control of a Reconfigurable Planar Cable Driven Parallel Manipulator
Cable driven parallel robots (CDPRs) are often challenging to model and to dynamically control due to the inherent flexibility and elasticity of the cables. The additional inclusion of online geometric reconfigurability to a CDPR results in a complex underdetermined system with highly non-linear dynamics. The necessary (numerical) redundancy resolution requires multiple layers of optimization rendering its application computationally prohibitive for real-time control. Here, deep reinforcement learning approaches can offer a model-free framework to overcome these challenges and can provide a real-time capable dynamic control. This study discusses three settings for a model-free DRL implementation in dynamic trajectory tracking: (i) for a standard non-redundant CDPR with a fixed workspace; (ii) in an end-to-end setting with redundancy resolution on a reconfigurable CDPR; and (iii) in a decoupled approach resolving kinematic and actuation redundancies individually.
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- PAR ID:
- 10447082
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Robotics and Automation (ICRA2023)
- Page Range / eLocation ID:
- 9644-9650
- Format(s):
- Medium: X
- 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/10.1109/ICRA48891.2023.10160498
