An Energy-based Approach for the Integration of Collaborative Redundant Robots in Restricted Work Environments

Hjorth, Sebastian; Lachner, Johannes; Madsen, Ole; Chrysostomou, Dimitrios

doi:10.1109/iros45743.2020.9341561

Cited by 11 publications

(15 citation statements)

References 23 publications

(40 reference statements)

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“…This is an important distinction as the usual definition of passivity and its relevance to coupled stability does not encompass the static (in-)stability that may be induced by exerting forces [69]. Managing both of these challenges is a minimum requirement for safe and successful physical interaction [70], [71].…”

Section: Discussionmentioning

confidence: 99%

Exploiting Redundancy to Facilitate Physical Interaction

et al. 2022

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The control of kinematically redundant robots is often approached using nullspace projection, which requires precise models and can be computationally challenging. Humans have many more degrees of freedom than are required to accomplish their tasks, but given neuro-mechanical limitations, it seems unlikely that biology relies on precise models or complex computation. An alternative biologically-inspired approach leverages the compositionality of mechanical impedance. In theory, nullspace projection eliminates any conflict between two tasks. In contrast, superposition of task-space impedance and a full-rank joint-space impedance may impose a task conflict. This work compared nullspace projection with impedance superposition during unconstrained motion and forceful physical interaction. In practice, despite their theoretical differences, we did not observe a substantial influence of the nullspace projector weighting matrix. We found that nullspace projection and impedance superposition both resulted in measurable task conflict. Remarkably, when the dimensionality of the nullspace was increased, impedance superposition was comparable to nullspace projection.

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Section: Discussionmentioning

confidence: 99%

Exploiting Redundancy to Facilitate Physical Interaction

et al. 2022

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“…If small joint impedances are assigned to the nullspace, the joint motion is not predictable and can lead to joint limit violations. Therefore, it is beneficial to incorporate joint limits as hard constraints, e.g., with the method presented in [65].…”

Section: A Limitations and Further Workmentioning

confidence: 99%

Shaping Impedances to Comply With Constrained Task Dynamics

2022

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Humans are capable of managing multiple tasks simultaneously. It is widely assumed that human motor control can be emulated by impedance control. To achieve human-like behavior, however, the impedance parameters of multiple tasks may vary during task execution. We propose an algorithm that shapes task impedance as a function of the robot's time-varying inertial properties. These properties involve virtually constrained masses and virtually constrained inertias that counteract a task in order to comply with a given constraint. In this work, we not only detect task conflicts, but also show how to handle them. Our method is able to control kinematically redundant robots. We developed a dampingdesign method that does not interfere with our desired Cartesian task-space behavior. The control approach was verified in experiments on a real robot. We compared our impedance shaping method with two alternative control approaches: simple impedance superposition and nullspace projection. Our method preserved the passivity while improving the Cartesian task performance of an impedance controller. The method has computational advantages, beneficial to control robots with many degrees of freedom.

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“…Compared with traditional robotic manipulators, redundant manipulators have more degrees of freedom (DOF) in joint space than task space, which possesses better flexibility for complicated tasks. Therefore, redundant manipulators are widely used in fields such as human-robot collaboration [ 1 ], medical surgery [ 2 ], and space exploration [ 3 ]. Redundant manipulators often work in dynamic environments and often even share workspaces with people.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators

Shen

Jia

Huang

et al. 2022

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Redundant manipulators are widely used in fields such as human-robot collaboration due to their good flexibility. To ensure efficiency and safety, the manipulator is required to avoid obstacles while tracking a desired trajectory in many tasks. Conventional methods for obstacle avoidance of redundant manipulators may encounter joint singularity or exceed joint position limits while tracking the desired trajectory. By integrating deep reinforcement learning into the gradient projection method, a reactive obstacle avoidance method for redundant manipulators is proposed. We establish a general DRL framework for obstacle avoidance, and then a reinforcement learning agent is applied to learn motion in the null space of the redundant manipulator Jacobian matrix. The reward function of reinforcement learning is redesigned to handle multiple constraints automatically. Specifically, the manipulability index is introduced into the reward function, and thus the manipulator can maintain high manipulability to avoid joint singularity while executing tasks. To show the effectiveness of the proposed method, the simulation of 4 degrees of planar manipulator freedom is given. Compared with the gradient projection method, the proposed method outperforms in a success rate of obstacles avoidance, average manipulability, and time efficiency.

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An Energy-based Approach for the Integration of Collaborative Redundant Robots in Restricted Work Environments

Cited by 11 publications

References 23 publications

Exploiting Redundancy to Facilitate Physical Interaction

Exploiting Redundancy to Facilitate Physical Interaction

Shaping Impedances to Comply With Constrained Task Dynamics

Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators

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