Forward Dynamics Compliance Control (FDCC): A new approach to cartesian compliance for robotic manipulators

Scherzinger, Stefan; Roennau, Arne; Dillmann, Rüdiger

doi:10.1109/iros.2017.8206325

Cited by 40 publications

(19 citation statements)

References 19 publications

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“…1 as using a virtually conditioned twin of the real mechanism for which we wish to solve IK. The basic idea behind this approach has been presented in our previous work [13]. In this paper, we present more detailed aspects behind this concept and provide an empirical proof of this earlier proposition.…”

Section: B Homogenization Methodsmentioning

confidence: 63%

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Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking

Scherzinger

Roennau

Dillmann

2019

2019 19th International Conference on Advanced Robotics (ICAR)

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Tracking Cartesian trajectories with end-effectors is a fundamental task in robot control. For motion that is not known a priori, the solvers must find fast solutions to the inverse kinematics (IK) problem for discretely sampled target poses. On joint control level, however, the robot's actuators operate in a continuous domain, requiring smooth transitions between individual states. In this work we present a boost to the well-known Jacobian transpose method to address this goal, using the mass matrix of a virtually conditioned twin of the manipulator. Results on the UR10 show superior convergence and quality of our dynamics-based solver against the plain Jacobian method. Our algorithm is straightforward to implement as a controller, using present robotics libraries.

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Section: B Homogenization Methodsmentioning

confidence: 63%

“…A primary contribution of this paper is to propose forward dynamics solvers in the context of J T -based IK solving. This general idea bases on previous findings [13]. Here, we present an enhanced algorithm with improved convergence with physically plausible intermediate solutions for motion control.…”

Section: Problem Statement and Related Workmentioning

confidence: 75%

Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking

Scherzinger

Roennau

Dillmann

2019

2019 19th International Conference on Advanced Robotics (ICAR)

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“…Compliant control with a robotic hand can be performed with soft [22] or flexible hardware [2] or with software using sensor feedback [1]. Different approaches use software compliant control using a torque sensor with a robust modelling [23], or using online learning for adaptive control [24], or using cerebellar principles [25], or combining reflexes with predictive control [26]. Although, there are other approaches using SNN for motion control using force feedback, to the best of our knowledge, there is no implementation of an SNN for soft grasping with a 5-finger anthropomorphic hand performing compliant control without force sensors using the standart joint interface.…”

Section: Introductionmentioning

confidence: 99%

Soft-Grasping With an Anthropomorphic Robotic Hand Using Spiking Neurons

Tieck

Secker

Kaiser

et al. 2021

IEEE Robot. Autom. Lett.

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Evolution gave humans advanced grasping capabilities combining an adaptive hand with efficient control. Grasping motions can quickly be adapted if the object moves or deforms. Soft-grasping with an anthropomorphic hand is a great capability for robots interacting with objects shaped for humans. Nevertheless, most robotic applications use vacuum, 2-finger or custom made grippers. We present a biologically inspired spiking neural network (SNN) for soft-grasping to control a robotic hand. Two control loops are combined, one from motor primitives and one from a compliant controller activated by a reflex. The finger primitives represent synergies between joints and hand primitives represent different affordances. Contact is detected with a mechanism based on inter-neuron circuits in the spinal cord to trigger reflexes. A Schunk SVH 5-finger hand was used to grasp objects with different shapes, stiffness and sizes. The SNN adapted the grasping motions without knowing the exact properties of the objects. The compliant controller with online learning proved to be sensitive, allowing even the grasping of balloons. In contrast to deep learning approaches, our SNN requires one example of each grasping motion to train the primitives. Computation of the inverse kinematics or complex contact point planning is not required. This approach simplifies the control and can be used on different robots providing similar adaptive features as a human hand. A physical imitation of a biological system implemented completely with SNN and a robotic hand can provide new insights into grasping mechanisms.

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“…The Tri-Co (coexisting–cooperative–cognitive) robot is a new trend in the development of robotics. Active compliance control technology plays an extremely important role in the application of Tri-Co robots (Firouzeh and Paik, 2017; Scherzinger et al , 2017; Buchli et al , 2009). To solve the problem of physical human–robot interaction, the impedance control or admittance control is widely used to improve the dynamic performance of robot.…”

Section: Introductionsmentioning

confidence: 99%

Design of admittance controller with sliding mode based on disturbance observer for elbow joint actuated by pneumatic muscles

Wang

Huang

Shen

et al. 2020

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Purpose Active compliance control is the key technology for Tri-Co robots (coexisting–cooperative–cognitive robots) to interact with the environment and people. This study aims to make the robot arm shake hands compliantly with people; the paper proposed two closed-loop-compliant control schemes for the dynamic identification of cascade elbow joint. Design/methodology/approach The active compliance control strategy consists of inner and outer loops. The inner loop is the position control using sliding mode control with disturbance observer (SMCDO), in which a new saturation function is designed to replace the traditional signal function of sliding mode control (SMC) law so as to mitigate chatter. The outer loop is the admittance control to regulate the dynamic behaviours of the elbow joint, i.e. its impedance. The simulation is carried out to verify the performance of the proposed control scheme. Findings The results show that the chatter of traditional SMC can be effectively eliminated by using SMCDO with this saturation function. In addition, for the handshake task, the value of threshold force and elbow joint compliance is defined. Then, the threshold force tests, impact tests and elbow-joint compliance tests are carried out. The results show that, in the impedance model, the elbow joint compliance only depends on the stiffness parameters, not on the position control loop. Practical implications The effectiveness of the admittance control based on SMCDO can improve the adaptability of industrial manipulator in different working environments to some degree. Originality/value The admittance control with SMCDO completed trajectory tracking has higher accuracy than that based on SMC.

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Forward Dynamics Compliance Control (FDCC): A new approach to cartesian compliance for robotic manipulators

Cited by 40 publications

References 19 publications

Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking

Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking

Soft-Grasping With an Anthropomorphic Robotic Hand Using Spiking Neurons

Design of admittance controller with sliding mode based on disturbance observer for elbow joint actuated by pneumatic muscles

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