8,819 research outputs found
Effects of Impedance Reduction of a Robot for Wrist Rehabilitation on Human Motor Strategies in Healthy Subjects during Pointing Tasks
Studies on human motor control demonstrated the existence of simplifying strategies (namely
`Donders' law') adopted to deal with kinematically redundant motor tasks. In recent research we
showed that Donders' law also holds for human wrist during pointing tasks, and that it is heavily
perturbed when interacting with a highly back-drivable state-of-the-art rehabilitation robot. We
hypothesized that this depends on the excessive mechanical impedance of the Pronation/Supination
(PS) joint of the robot and in this work we analyzed the effects of its reduction. To this end we
deployed a basic force control scheme, which minimizes human-robot interaction force. This resulted
in a 70% reduction of the inertia in PS joint and in decrease of 81% and 78% of the interaction
torques during 1-DOF and 3-DOFs tasks. To assess the effects on human motor strategies, pointing
tasks were performed by three subjects with a lightweight handheld device, interacting with the
robot using its standard PD control (setting impedance to zero) and with the force-controlled robot.
We quantified Donders' law as 2-dimensional surfaces in the 3-dimensional configuration space of
rotations. Results revealed that the subject-specific features of Donders' surfaces reappeared after
the reduction of robot impedance obtained via the force control
A spatial impedance controller for robotic manipulation
Mechanical impedance is the dynamic generalization of stiffness, and determines interactive behavior by definition. Although the argument for explicitly controlling impedance is strong, impedance control has had only a modest impact on robotic manipulator control practice. This is due in part to the fact that it is difficult to select suitable impedances given tasks. A spatial impedance controller is presented that simplifies impedance selection. Impedance is characterized using ¿spatially affine¿ families of compliance and damping, which are characterized by nonspatial and spatial parameters. Nonspatial parameters are selected independently of configuration of the object with which the robot must interact. Spatial parameters depend on object configurations, but transform in an intuitive, well-defined way. Control laws corresponding to these compliance and damping families are derived assuming a commonly used robot model. While the compliance control law was implemented in simulation and on a real robot, this paper emphasizes the underlying theor
Trajectory Deformations from Physical Human-Robot Interaction
Robots are finding new applications where physical interaction with a human
is necessary: manufacturing, healthcare, and social tasks. Accordingly, the
field of physical human-robot interaction (pHRI) has leveraged impedance
control approaches, which support compliant interactions between human and
robot. However, a limitation of traditional impedance control is that---despite
provisions for the human to modify the robot's current trajectory---the human
cannot affect the robot's future desired trajectory through pHRI. In this
paper, we present an algorithm for physically interactive trajectory
deformations which, when combined with impedance control, allows the human to
modulate both the actual and desired trajectories of the robot. Unlike related
works, our method explicitly deforms the future desired trajectory based on
forces applied during pHRI, but does not require constant human guidance. We
present our approach and verify that this method is compatible with traditional
impedance control. Next, we use constrained optimization to derive the
deformation shape. Finally, we describe an algorithm for real time
implementation, and perform simulations to test the arbitration parameters.
Experimental results demonstrate reduction in the human's effort and
improvement in the movement quality when compared to pHRI with impedance
control alone
A macro-micro robot for precise force applications
This paper describes an 8 degree-of-freedom macro-micro robot capable of performing tasks which require accurate force control. Applications such as polishing, finishing, grinding, deburring, and cleaning are a few examples of tasks which need this capability. Currently these tasks are either performed manually or with dedicated machinery because of the lack of a flexible and cost effective tool, such as a programmable force-controlled robot. The basic design and control of the macro-micro robot is described in this paper. A modular high-performance multiprocessor control system was designed to provide sufficient compute power for executing advanced control methods. An 8 degree of freedom macro-micro mechanism was constructed to enable accurate tip forces. Control algorithms based on the impedance control method were derived, coded, and load balanced for maximum execution speed on the multiprocessor system
Deep Model Predictive Variable Impedance Control
The capability to adapt compliance by varying muscle stiffness is crucial for
dexterous manipulation skills in humans. Incorporating compliance in robot
motor control is crucial to performing real-world force interaction tasks with
human-level dexterity. This work presents a Deep Model Predictive Variable
Impedance Controller for compliant robotic manipulation which combines Variable
Impedance Control with Model Predictive Control (MPC). A generalized Cartesian
impedance model of a robot manipulator is learned using an exploration strategy
maximizing the information gain. This model is used within an MPC framework to
adapt the impedance parameters of a low-level variable impedance controller to
achieve the desired compliance behavior for different manipulation tasks
without any retraining or finetuning. The deep Model Predictive Variable
Impedance Control approach is evaluated using a Franka Emika Panda robotic
manipulator operating on different manipulation tasks in simulations and real
experiments. The proposed approach was compared with model-free and model-based
reinforcement approaches in variable impedance control for transferability
between tasks and performance.Comment: Preprint submitted to the journal of robotics and autonomous system
Enhancing Robot-Environment Physical Interaction via Optimal Impedance Profiles
Physical interaction of robots with their environment is a challenging problem because of the exchanged forces. Hybrid position/force control schemes often exhibit problems during the contact phase, whereas impedance control appears to be more simple and reliable, especially when impedance is shaped to be energetically passive. Even if recent technologies enable shaping the impedance of a robot, how best to plan impedance parameters for task execution remains an open question. In this paper we present an optimization-based approach to plan not only the robot motion but also its desired end-effector mechanical impedance. We show how our methodology is able to take into account the transition from free motion to a contact condition, typical of physical interaction tasks. Results are presented for planar and three-dimensional open-chain manipulator arms. The compositionality of mechanical impedance is exploited to deal with kinematic redundancy and multi-arm manipulation
SMC based bilateral control
Design of a motion control system should take into account (a) unconstrained motion performed without interaction with environment or other system, and
(b) constrained motion with system in contact with environment or another system or has certain functional interaction with another system. Control in both cases can be formulated in terms of maintaining desired system configuration what makes essentially the same structure for common tasks: trajectory tracking, interaction force control, compliance control etc. It will be shown that the same design approach can be used for systems that maintain some functional relation – like bilateral or multilateral systems, relation among mobile robots or control of haptic systems.
Human-robot co-carrying using visual and force sensing
In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments
Object-Aware Impedance Control for Human-Robot Collaborative Task with Online Object Parameter Estimation
Physical human-robot interactions (pHRIs) can improve robot autonomy and
reduce physical demands on humans. In this paper, we consider a collaborative
task with a considerably long object and no prior knowledge of the object's
parameters. An integrated control framework with an online object parameter
estimator and a Cartesian object-aware impedance controller is proposed to
realize complicated scenarios. During the transportation task, the object
parameters are estimated online while a robot and human lift an object. The
perturbation motion is incorporated into the null space of the desired
trajectory to enhance the estimator accuracy. An object-aware impedance
controller is designed using the real-time estimation results to effectively
transmit the intended human motion to the robot through the object.
Experimental demonstrations of collaborative tasks, including object
transportation and assembly tasks, are implemented to show the effectiveness of
our proposed method.Comment: 11 pages, 5 figures, for associated video, see
https://youtu.be/bGH6GAFlRgA?si=wXj_SRzEE8BYoV2
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