No abstract

In order to solve the problem in master-slave heterogeneous teleoperation systems, which is that the inverse solution of robot cannot be achieved when the mapped wrist joint exceeds the reachable space of robot wrist joint, a master-slave heterogeneous mapping method based on link pose constraint is proposed. The link pose constraint means that the position and orientation of robot hand link are the same or similar to that of exoskeleton hand link. In the proposed method, the link pose constraint is used in an exoskeleton-based heterogeneous teleoperation system to realize consistent mapping between the exoskeleton and robot hand link inside wrist joint reachable space, and to realize effective inverse solution and consistent mapping of the end effector (EE) outside the reachable space. A simulation was performed by using the proposed method. The simulation results show that the proposed method can effectively realize the mapping between the exoskeleton and robot in the reachable space of robot EE. The teleoperation experimental setup was constructed by using the developed exoskeleton and an industrial robot, and the verification experiments of the proposed mapping method were performed. The experimental results indicate that the position error of the proposed heterogeneous mapping method is 0.48 mm. The experimental results demonstrate that the proposed mapping method can effectively realize master-slave heterogeneous mapping and realize continuous motion control of the teleoperation system, which satisfies the application requirements of human-robot integration, robot skill enhancement, and robot skill learning.

Trajectory learning is an important part of robot skill learning, and a trajectory learning method based on improved Dynamic Movement Primitives (DMPs) is proposed to improve trajectory reproduction accuracy. In this method, the truncation processing is used to improve the Gaussian kernel function of DMPs to eliminate the impact of tail exponential decay on fitted target forcing term, and the optimization on the number of shape parameters is used to make the model better approximate the local gradient of the target forcing term. The principle of trajectory accuracy improvement is described in detail. The trajectory reproduction simulation is performed, which verifies the feasibility of the proposed method. An experimental setup for robot skill trajectory learning is constructed and the relevant comparison experiments are performed, which verifies the effectiveness of the proposed method in improving trajectory learning accuracy.