Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.
Trajectory planning is a crucial step in controlling robot motion. The efficiency and accuracy of trajectory planning directly impact the real-time control and accuracy of robot motion. The robot’s trajectory is mapped to the joint space, and a mathematical model of trajectory planning is established to meet physical constraints during motion and avoid joint coupling problems. To enhance convergence speed and avoid local optima, an improved quantum particle swarm optimization algorithm is proposed and applied to solve the mathematical model of robot trajectory planning. The trajectory planning in robot joint space is then tested based on the improved quantum particle swarm optimization algorithm. The results demonstrate that this method can replace the traditional trajectory planning algorithms and offers higher accuracy and efficiency.
All‐solid‐state lithium batteries (ASSLBs) have become a recent research hotspot because of their excellent safety performance. In order to better reflect their superiority, high‐voltage cathodes should be applied to enhance the energy density of solid batteries to compete with commercial liquid batteries. However, the introduction of high‐voltage cathodes suffers from many problems, such as low electrochemical stability, inferior interface chemical stability between cathode and electrolyte, poor mechanical contact, and gas evolution. These drawbacks significantly influence the battery performance, even causing battery failure and hindering the commercialization of solid‐state batteries. This paper first reviews the above failure mechanisms of high‐voltage cathode‐based ASSLBs from different perspectives. Then, recent advances in solid‐state electrolytes for ASSLBs are summarized, mainly including polymer solid electrolytes, sulfide solid electrolytes, and oxide solid electrolytes. In addition, the influence of the cathode materials is also highly critical, and strategies to improve electrochemical performance are put forward, which can be divided into coating protection, synthesis modification, and structure improvement. Finally, guidelines for the future development of solid‐state batteries are also discussed.
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