A Passivity-based Nonlinear Admittance Control with Application to Powered Upper-limb Control under Unknown Environmental Interactions

This paper presents an admittance controller based on the passivity theory for a powered upper-limb exoskeleton robot which is governed by the nonlinear equation of motion. Passivity allows us to include a human operator and environmental interaction in the control loop. The robot interacts with the human operator via F/T sensor and interacts with the environment mainly via end-effectors. Although the environmental interaction cannot be detected by any sensors (hence unknown), passivity allows us to have natural interaction. An analysis shows that the behavior of the actual system mimics that of a nominal model as the control gain goes to infinity, which implies that the proposed approach is an admittance controller. However, because the control gain cannot grow infinitely in practice, the performance limitation according to the achievable control gain is also analyzed. The result of this analysis indicates that the performance in the sense of infinite norm increases linearly with the control gain. In the experiments, the proposed properties were verified using 1 degree-of-freedom testbench, and an actual powered upper-limb exoskeleton was used to lift and maneuver the unknown payload.Comment: Accepted in IEEE/ASME Transactions on Mechatronics (T-MECH

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

Adaptive human force scaling via admittance control for physical human-robot interaction

The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.WOS:000731146900006Scopus - Affiliation ID: 60105072Q2ArticleUluslararası işbirliği ile yapılan - EVETOctober2021YÖK - 2021-22Eki

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control Retargetting Human Commands to Feasible Robot Control References

This article presents a robust and reliable human–robot collaboration (HRC) framework for bimanual manipulation. We propose an optimal motion adaptation method to retarget arbitrary human commands to feasible robot pose references while maintaining payload stability. The framework comprises three modules: 1) a task-space sequential equilibrium and inverse kinematics optimization ( task-space SEIKO ) for retargeting human commands and enforcing feasibility constraints, 2) an admittance controller to facilitate compliant human–robot physical interactions, and 3) a low-level controller improving stability during physical interactions. Experimental results show that the proposed framework successfully adapted infeasible and dangerous human commands into continuous motions within safe boundaries and achieved stable grasping and maneuvering of large and heavy objects on a real dual-arm robot via teleoperation and physical interaction. Furthermore, the framework demonstrated the capability in the assembly task of building blocks and the insertion task of industrial power connectors

Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted