As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F₁ and F₃, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

Human-robot interaction is crucial for the future of smart factories and new industrial systems. Safety in robotics has always been a top priority, with external sensors being studied to construct safety perception systems for robots. This paper proposes an obstacle avoidance strategy based on an efficient distance estimation method using a vision sensor to address the challenge of robot occlusion. The method fuses depth images with a predefined robot skeleton model to estimate robot pose in real time, and uses the optimized potential field model to achieve full-body collision avoidance. Comparative experiments validate the efficiency of the proposed method, which represents a significant contribution to enhancing human–robot interaction and safety in industrial settings.

To address the limitations of traditional wall-climbing robots in ferromagnetic wall operation scenarios, such as poor surface adaptability, insufficient motion stability, and difficulties in negotiating complex surface transitions, this paper proposes a four-wheel permanent magnet wall-climbing robot and systematically investigates its stability. First, the center of gravity (CoG) of the robot was determined based on a 3D prototype model developed in SolidWorks2020. To ensure reliable adhesion, the anti-slip and anti-overturning stability criteria under various operating conditions were derived. Furthermore, combining the mechanical characteristics, a magnetic field simulation model for the magnetically adsorbed wheels was established. Electromagnetic simulations of the permanent magnets were conducted using Ansoft Maxwell, followed by adhesion force experiments. Finally, the robot prototype was assembled, and a test system was constructed to evaluate its overall performance. The experimental results demonstrate that the designed robot can smoothly negotiate inner right-angle corners. This validates the effectiveness of the proposed structural design and stability analysis, providing a feasible technical solution for wall-climbing robots in complex scenarios and exhibiting significant potential for engineering applications.