Coordinate calibration accuracy is a prerequisite for the achievement of collaborative motion of dual robots and visual positioning. In the case of AXB=YCZ, the associated coordinate relationships of hand-eye (X), robot-robot (Y), tool-flange (Z) need to be determined in order to provide accurate data for dual-robot manipulation. The classical independent estimations and separable solving of rotational and translational components inevitably cause certain inaccuracy and inconsistency. To this end, the work presented herein describes an efficient approach based on dual quaternion (DQ) to calibrate the hand-eye (X), robot-robot (Y), tool-flange (Z) transformations, corresponding to the dual-robot collaborative motion. After establishing the closed-form kinematic equation of dual robots in DQ form, the calibration process is divided into two steps: 1) the hand-eye transformation (X) calibration is performed by letting one robot with a visual sensor move, while keeping the other robot with end-tool in a fixed configuration, thereby allowing the calibration result to be obtained by solving the DQ-based linear equations with the SVD algorithm; 2) the simultaneous calibration of robotrobot (Y) and tool-flange (Z) is transformed into a DQ-based equation, and the results are obtained in a similar way. A simulation analysis is carried out, accounting for the presence of noise levels and different data pairs, while calibration experiments are also conducted to verify the accuracy and efficacy of the proposed method.

Accurate estimation of the dynamic parameters comprising a robot's dynamics model, is of paramount importance for simulation and real-time model-based control. The conventional approaches for obtaining the identification model are extremely tedious, and incapable of offering universal applicability, as well as physical feasibility of dynamic parameter identification. To this end, the work presented herein proposes a novel and generic identification methodology for retrieving the dynamic parameters of serial manipulators with arbitrary DOFs based on Lie theory. In this approach, the robot dynamics model that includes frictional terms, is analytically represented as a closed-form matrix equation by rearranging the classical recursive Newton-Euler formulation. The link inertia matrix that comprises inertia tensors, masses and CoM positions, together with the joint friction coefficients, are extracted from the regrouped linear dynamics model by means of the Kronecker product. Meanwhile, the introduced Kronecker-Sylvester identification equation is formulated as an optimization problem involving dynamic parameters with physical feasibility constraints, and is ultimately estimated via LMI (Linear Matrix Inequality) techniques and SDP (semi-definite programming) using the joint position, velocity, acceleration and torque data. Identification results of dynamic parameters are accurately procured through a series of practical tests that entail providing a 7-DOF Rokae xMate robot, with optimized Fourier-series-based excitation trajectories. Experimental validation serves the purpose of demonstrating the proposed method's efficacy, in terms of accurately retrieving a serial manipulator's dynamic parameters.

The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.

In this paper, we propose a new optimal trajectory planning method for the manipulator to optimize its operating efficiency and ensure the smoothness of the motion process. The position sequences in joint space corresponding to a specified trajectory in task space are obtained by using the inverse kinematic algorithm, and the seventh-degree B-spline curve interpolation method is used to construct the joint trajectory with controllable start–stop kinematic parameters, and continuous velocity, acceleration and jerk. The kinematic constraints of the manipulator are transformed into the control point constraints of the B-spline curve, the sequential quadratic programming method is used to solve the optimal motion time node, and then the time-optimal continuous jerk trajectory satisfying the nonlinear kinematic constraints is planned. Simulation results show that the proposed trajectory planning method provides the ideal trajectory for the joint controller, ensuring the manipulator can smoothly track any specified trajectory in the task space in the shortest time.