In this paper, the wireless power transfer system (WPT) system with dynamic loads such as batteries is studied comprehensively. An integrated control technology of load estimation and power tracking of LCC compensated is proposed, which realizes load estimation, mode judgment and charging control at the transmitter, and standard load setting and decoupling control at the receiver. Based on the influence of reflection impedance on the output current of the inverter, a method of identifying coupling coefficient and equivalent load is proposed and a mathematical model is established. Receiver controller provides standard reference load for load estimation. Transmitter controller judges battery status according to the estimation of equivalent load and adopts double closed-loop control to regulate power and current. Receiver decouples control when battery charging voltage reaches the threshold, and providing mode conversion sign for transmitter controller to realize constant current (CC) and constant voltage (CV) charging of battery. Dual-sides integrated control scheme has no data communication between transmitter and receiver, so it can control independently, which reduces the complexity of the system and is suitable for different charging modes. The proposed controller is more efficient as it maintains a track current, and dynamically alters the pick-up characteristics to suit the load demand. Finally, the simulation and experimental results validate the feasibility of proposed control method, which realizes the estimation of the load and CC/CV charging of the battery. The proposed WPT system achieved the efficiency at 91.16% while delivering 2kW to the load with a vertical air gap of 150mm.

The path planning algorithm not only ensures that the mobile robot can avoid obstacles to reach the target point at a safe speed but also ensures that the mobile robot can quickly adapt to the complex changing environment. In this paper, the existing path planning algorithms of mobile robots are analyzed, and then the fusion path planning algorithm is studied. The main work is summarized as follows: A* algorithm is used to complete global path planning and path smoothing, the basic principle of dynamic window method (DWA) is studied, and the dynamic constraints of mobile robots are discussed. The shortcomings of the dynamic window method, i.e., that the dynamic window method does not have the ability to self-learn and self-adapt in the dynamic and unknown environment, are analyzed through simulation experiments. In addition, by studying the basic principle of deep reinforcement learning, the essence and characteristics of DWA algorithm and DQN algorithm are analyzed, which provides ideas for the fusion path planning algorithm based on DQN and DWA. Finally, to cope with the complex and changeable environment and improve the real-time obstacle avoidance ability of mobile robots, a fusion path planning algorithm based on DQN and DWA is proposed. First, the dynamic window method is used to limit the driving of the mobile robot directly to the velocity space. Then, a deep Q network is designed and trained to approximate the state-action value function of the mobile robot, then dynamically interact with the environment to adjust the robot’s moving trajectory in real time, and finally, find the optimal path for the robot. The simulation results show that the fusion path planning algorithm proposed in this paper ensures that the mobile robot has strong generalization ability and robustness under the complex, variable, and dynamic unknown environment. Compared with the existing DWA and DQN algorithms, the proposed fusion path planning algorithm achieves better path planning performance with less training times, shorter computation time, and faster convergence speed.

The proposed research aims to accomplish an improved A* algorithm for mobile robots in complex environments. In this novel algorithm, the guidance of environment information is added to the evaluation function to enhance the adaptability of the algorithm in complex environments. Additionally, to solve the problem of path smoothness, the optimal selection rules for child nodes and the bidirectional optimization strategy for path smoothing are introduced to reduce redundant nodes, which effectively makes the search space smaller and the path smoother. The simulation experiments show that, compared with the colony algorithm and Dijkstra algorithms, the proposed algorithm has significantly improved performance. Compared with the A* algorithm, the average planning time is reduced by 17.2%, the average path length is reduced by 2.05%, the average turning point is reduced by 49.4%, and the average turning Angle is reduced by 75.5%. The improved A* algorithm reduces the search space by 61.5% on average. The simulation results show that the effectiveness and adaptability of the improved A* algorithm in complex environments are verified by multi-scale mapping and multi-obstacle environment simulation experiments.

The ant colony algorithm is an approach for path planning that is used in multiple industries. This paper proposes an improved robot path planning method, referred to as Improved-ACO. First, the heuristic information calculation is optimized to increase algorithm efficiency and shorten convergence time. Secondly, an enhanced Tanh function is included into the heuristic information, allowing dynamic modifications during the search period and preventing the algorithm’s convergence to local optima. Then, a novel pheromone update strategy is employed to accelerate convergence. Next, a novel pheromone diffusion mechanism is proposed to strengthen the ants’ search capability. Additionally, a collision avoidance system and improved B-spline curves are included for path smoothing, guaranteeing that the optimized pathways conform to the robot’s kinematic limitations. Simulation results indicate that the improved ant colony algorithm decreases the average number of turns by $37.5\%$ and accelerates convergence time by $39.45\%$ relative to existing methods across diverse map dimensions. The experiments confirm that Improved-ACO achieves rapid convergence and constructs smooth curves that adhere to the robot’s kinematic constraints. Consequently, Improved-ACO is confirmed as an efficient and adaptable route planning method for robotic navigation under complicated situations.

Magnetic coupling resonant wireless power transfer network (MCRWPTN) system can realize wireless power transfer for some electrical equipment real‐time and high efficiency in a certain spatial scale, which resolves the contradiction between power transfer efficiency and the power transfer distance of the wireless power transfer. A fully coupled resonant energy transfer model for multirelay coils and ports is established. A dynamic adaptive impedance matching control based on fully coupling matrix and particle swarm optimization algorithm based on annealing is developed for the MCRWPTN. Furthermore, as an example, the network which has twenty nodes is analyzed, and the best transmission coefficient which has the highest power transfer efficiency is found using the optimization algorithm, and the coupling constraints are considered simultaneously. Finally, the effectiveness of the proposed method is proved by the simulation results.

为了研制高灵敏海水盐度传感器，本文基于CO₂激光技术成功制备出一种工作在色散转折点（DTP）附近的长周期光纤光栅（LPFG）。首先，利用CO₂激光器在80 μm细单模光纤上制备出工作在DTP附近的LPFG，证明了采用CO₂激光微加工技术制备较短周期LPFG的可能性。其次，通过调控CO₂激光器的制备周期，使高阶包层模式LP_1,9工作在DTP附近，从而显著提高了LPFG的折射率灵敏度。在双峰谐振增敏效应的作用下，当海水盐度从5.001 ‰变化到39.996 ‰时，光栅周期为115.4 μm的双峰谐振LPFG平均灵敏度高达0.279 nm/‰。研究结果表明，本文制备的LPFG海水盐度传感器具有谐振损耗大和灵敏度高的优点，其在海水盐度监测领域具有较好的应用前景。