Calibration of robot kinematic parameters can effectively improve the absolute positioning accuracy of the end-effector for industrial robots. This article proposes a calibration method for robot kinematic parameters based on the drawstring displacement sensor. Firstly, the kinematic error model for articulated robot is established. Based on such a model, the position measurement system consisting of four drawstring displacement sensors is used to measure the actual position of the robot end-effector. Then, the deviation of the kinematic parameters of the robot is identified by the least-squares method according to robot end-effector deviations. The Cartesian space compensation method is adopted to improve the absolute positioning accuracy of the robot end-effecter. By experiments on the EFORT ER3A robot, the absolute positioning accuracy of the robot is significantly improved after calibration, which shows the effectiveness of the proposed method.

This article presents a novel model-based sensorless collision detection scheme for human–robot interaction. In order to recognize external impacts exerted on the manipulator with sensitivity and robustness without additional exteroceptive sensors, the method based on torque residual, which is the difference between nominal and actual torque, is adopted using only motor-side information. In contrast to classic dynamics identification procedure which requires complicated symbolic derivation, a sequential dynamics identification was proposed by decomposing robot dynamics into gravity and friction item, which is simple in symbolic expression and easy to identify with least squares method, and the remaining structure-complex torque effect. Subsequently, the remaining torque effect was reformulated to overcome the structural complexity of original expression and experimentally recovered using a machine learning approach named Lasso while keeping the involving candidates number reduced to a certain degree. Moreover, a state-dependent dynamic threshold was developed to handle the abnormal peaks in residual due to model uncertainties. The effectiveness of the proposed method was experimentally validated on a conventional industrial manipulator, which illustrates the feasibility and simplicity of the collision detection method.

Robotic manipulators may be subject to different types of disturbances such as unknown payloads, unmodeled dynamics, and environment interaction forces. Observing these unknown disturbances in robotic manipulators is fundamental in many robotic applications such as disturbance rejection and sensorless force control. In this paper, a novel disturbance observer (DOB) is introduced based on the insights from the finite-time observer (FTO) and robot dynamics. Different from the traditional DOBs, this new observer can provide the capability to track the disturbance within a finite time. The performance of the presented observer is verified by two kinds of typical disturbances for a two-link manipulator with a comparison with several existing DOBs. The simulation results show the rapidity and accuracy of the proposed FTO.

This letter proposes a bionic robot controller equipped with intelligent perception and autonomous planning modules to address the manufacturing industry's requirements for small-batch, customized, and autonomous task. Three crucial components: motion control module, vision perception module, and autonomous planning module are integrated into the controller based on the ROS framework and ECI platform. Taking multi-object rearrangement problem as an example, a dual-robot collaborative system is established for validation of the controller. The controller deploys the YOLOv5_OBB network for object recognition and localization. Leveraging the task sequence planning and RRT-Growth-Angle algorithm improved in this letter, it autonomously plans collision-free trajectories for the robots, facilitating their movement from the starting point to the grasping position and further to the placement location. The motion control algorithm collaboratively controls dual-robot, ensuring the precise and stable rearrangement of multiple objects into predefined positions. The results affirm that the bionic robot controller effectively mimics the three essential components of human-like functionality—perceiving, pondering, and acting—enabling it to autonomously and intelligently tackle complex tasks, which verifies the viability of the method.