A novel robot capable of performing maintenance and inspection tasks for railway bridges is proposed in this paper. Termed CMBOT (climbing manipulator robot), the robot is a combination of a five-degrees-of-freedom (5-Dof) biped climbing robot with two electromagnetic feet and a redundant manipulator with 7-Dof. This capability offers important advantages for performing maintenance and inspection tasks for railway bridges. Several fundamental issues of the CMBOT, such as robotic system development and motion planning algorithms, are addressed in this paper. A series of simulations and prototype experiments were conducted to validate the proposed robotic systems and motion planning algorithm. The results of the experiments show the reliability of the robotic systems and the efficiency of the motion planning algorithm.

The electrodeposition process parameters were optimized for the acquisition of high-strength monolithic nickel layers on Q235A substrates based on the Watts nickel plating solution using the DC electrodeposition method. Based on the study of the electrochemical polarization behavior of nickel ions in Watts’ plating solution, 16 experimental protocols were selected according to the orthogonal test method. The residual stress, microhardness, modulus of elasticity, and surface roughness of the nickel plating were tested by X-ray diffractometer, nano-mechanical test system, and surface profilometer, respectively, to investigate the influence of current density, temperature, and PH on the mechanical properties of nickel plating, so as to determine the best process solution for the preparation of high-strength nickel plating. The results of the study show that the mechanical properties of the nickel deposits electrodeposited onto Q235A are optimized when plating at a current density of 3 A/dm², a bath temperature of 45 °C, and a pH of 3.5. The nickel-plated layer has a minimum grain size of 34.8 nm, a microhardness of 3.86 GPa, a modulus of elasticity of 238 GPa, and a surface roughness Ra of 0.182 μm.

Inspired by the analysis of the ant locomotion observed by the high-speed camera, an ant-like gait transition model for the hexapod robot is proposed in this paper. The model which consists of the central neural system (CNS), neural network (NN), and central pattern generators (CPGs) can produce the rhythmic signals for different gaits and can realize the transition of these gait automatically and smoothly according to the change of terrain. The proposed model suggests the neural mechanisms of the ant gait transition and can improve the environmental adaptability of the hexapod robot. The numerical simulation and corresponding physical experiment are implemented in this paper to verify the proposed method.

The growing diversity and number of industrial robots make energy consumption prediction and optimization increasingly essential. Current data-driven approaches, particularly those based on multi-layer perception (MLP), have shown feasibility but typically overlook the variability or unknown nature of load-related parameters in real-world applications. This paper presents a KAN-LSTM model designed to accurately predict energy consumption under unknown load conditions, alongside a particle swarm optimization (PSO) algorithm for minimizing energy use. First, an industrial robot dynamics and energy consumption model is established. Then, the KAN-LSTM model is trained on datasets from the AUBO-E5 robot, with its predictions compared to alternative network models. Finally, PSO is applied to optimize energy consumption. Experimental results indicate that the KAN-LSTM model achieves high prediction accuracy (95.7–97.1%) and offers substantial energy optimization potential (53.1–64.7%). Optimized industrial robots are particularly suitable for tasks such as picking and palletizing in the courier industry, saving operational costs and increasing the sustainability of automated systems in logistics environments.

Energy consumption is one of the most critical factors in determining the kinematic performance of quadruped robots. However, existing research methods often encounter challenges in quickly and efficiently reducing the energy consumption associated with quadrupedal robotic locomotion. In this paper, the deep deterministic policy gradient (DDPG) algorithm was used to optimize the energy consumption of the Cyber Dog quadruped robot. Firstly, the kinematic and energy consumption models of the robot were established. Secondly, energy consumption was optimized by reinforcement learning using the DDPG algorithm. The optimized plantar trajectory was then compared with two common plantar trajectories in simulation experiments, with the same period and the number of synchronizations but varying velocities. Lastly, real experiments were conducted using a prototype machine to validate the simulation data. The analysis results show that, under the same conditions, the proposed method can reduce energy consumption by 7~9% compared with the existing optimal trajectory methods.

Motivated by the potential applications of maintenance and inspection tasks for railway bridges, we have developed a biped climbing robot. The biped climbing robot can climb on the steel guardrail of the railway bridge with two electromagnetic feet and implement the maintenance and inspection tasks by a redundant manipulator with 7 degrees of freedom. To reduce the vibration of the manipulator caused by the low rigidity of the guardrail and the discontinuous trajectories of joints, a motion planning algorithm for vibration reduction is proposed in this paper. A geometric path accounting for obstacle avoidance and the manipulator’s center of gravity is determined by the gradient projection method with a singularity-robust inverse. Then, a piecewise quintic polynomial S shape curve with a smooth jerk (derivative of joint angular acceleration) profile is used to interpolate the sequence of joint angular position knots that are transformed from the via-points in the obstacle-avoidance path. The parameters of the quintic polynomial S-curve are determined by a nonlinear programming problem in which the objective function is to minimize the maximus of the torque exerted by the manipulator on the guardrail throughout the jerk-continuous trajectory. Finally, a series of simulation experiments are conducted to validate the effectiveness of the proposed algorithm. The simulation results show that the tracking errors of the trajectory with the proposed optimization algorithm are significantly smaller than the tracking errors of the trajectory without optimization. The absolute values of mean deviation of the tracking errors of the three coordinate axes decreased by at least 48.3% compared to the trajectory without vibration-reduction in the triangle working path and linear working path trajectory following simulations. The analysis results prove that the proposed algorithm can effectively reduce the vibration of the end effector of the manipulator.