The signal acquisition and analysis of the structure vibration can effectively diagnose the operation status and fault type. The visual inspection method has the advantage of non-destructive testing and providing full-field vibration information for monitoring the vibration of rotating machinery. However, the accuracy requirements for measuring the small displacements generated by the low-amplitude vibration of high-speed rotating machinery are extremely high. It remains a challenge to achieve accurate, precise, and fast extraction of high-frequency and small-amplitude micro-vibrations using visual-based displacement measurement methods. Therefore, this paper proposes the object detection model, the Vibration Displacement Detection Network (VDDNet), for measuring vibration displacement of rotating bodies. VDDNet reduces the required computational complexity of the actual detection target and optimizes the quality of vibration displacement measurement. Additionally, by introducing an attention module and adopting different learning strategies to share rotating body target features, the real-time and accuracy of vibration monitoring are ensured. Furthermore, the evaluation method used in this paper comprehensively considers detection accuracy, real-time performance in deep learning methods, and displacement capture accuracy in vibration displacement monitoring. Actual vibration experimental results demonstrate that the proposed method is minimally affected by illumination and can achieve a better real-time capture accuracy of rotor micro-displacements, further proving its effectiveness and feasibility, which is helpful in advancing related research in this field.

In this article, a new nonlinear algorithm based on the sliding mode control is developed for the ball and plate control system to improve dynamic response and steady-state tracking accuracy of the control system. First, a new sliding mode reaching law is proposed, variable exponential power reaching law (VEPRL), which is expressed in two different forms including a nonlinear combination function term and a variable exponential power term, so that it can be adjusted adaptively according to the state of the system by the variable exponential power reaching term during the reaching process. The computation results show that it can not only effectively weaken the chattering phenomenon but also increase the rate of the system state reaching to the sliding mode surface. Moreover, it has the characteristic of global finite-time convergence. Besides, a complementary terminal sliding mode control (CTSMC) method is designed by combining the integral terminal sliding surface with the complementary sliding surface to improve the convergence rate. Based on the proposed VEPRL and CTSMC, a new sliding mode control method for the ball and plate system is presented. Finally, simulation results show the superiority and effectiveness of the proposed control method.

Multi-agent path finding is one of the key problems in the topic of multi-agent system. While some inevitable execution delays resulting from the realistic factors, such as robot faults or avoiding human etc., may make the path plan invalid. Our aim is to effectively find paths with robustness to k-delays for all agents, i.e., each agent can get a k-step margin in its paths without breaking the whole plan, especially for the large-scale systems. We propose a priority-based hierarchical framework for k-robust multi-agent path finding, where the pattern of searching path while avoiding conflict is profit to reduce the burden of conflict handling in k-robust planning. Then, the classification and generation rules of robust constraints are designed to guarantee global k-robustness of prioritized planning. Finally, for the new challenge of k-robust starting predicament, a multi-level key-agent guided priority adjustment mechanism is proposed to improve solution success rate. Experimental results show that the proposed algorithm can effectively reduce the runtime, and averagely maintain a success rate of over 95%. Especially for large-scale problems with hundreds of agents, the runtime can be reduced to a few seconds. In addition, the runtime does not increase dramatically as the k-value grows from 0 to 7.

Traditional multi-agent path planning algorithms often lead to path overlap and excessive energy consumption when dealing with cooperative tasks due to the single-agent-single-task configuration. For this reason, the “many-to-one” cooperative planning method has been proposed, which, although improved, still faces challenges in the vast search space for meeting points and unreasonable task handover locations. This paper proposes the Cooperative Dynamic Priority Safe Interval Path Planning with a multi-meeting-point and single-meeting-point solving mode switching (Co-${\mathrm{D}\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{P}\mathrm{P}}_{\mathrm{m}\mathrm{s}}$) algorithm to achieve multi-agent path planning with task handovers at multiple or single meeting points. First, the initial priority is set based on the positional relationships among agents within the cooperative group, and the improved Fermat point method is used to locate multiple meeting points quickly. Second, considering that agents must pick up sub-tasks or conduct task handovers midway, a segmented path planning strategy is proposed to ensure that cooperative agents can efficiently and accurately complete task handovers. Finally, an automatic switching strategy between multi-meeting-point and single-meeting-point solving modes is designed to ensure the algorithm’s success rate. Tests show that Co-${\mathrm{D}\mathrm{P}\mathrm{S}\mathrm{I}\mathrm{P}\mathrm{P}}_{\mathrm{m}\mathrm{s}}$ outperforms existing algorithms in 1-to-1 and m-to-1 cooperative tasks, demonstrating its efficiency and practicality.

This paper considers two kinds of stochastic reentrant job shop scheduling problems (SRJSSP), i.e., the SRJSSP with the maximum tardiness criterion and the SRJSSP with the makespan criterion. Owing to the NP‐complete complexity of the considered RJSSPs, an effective differential evolutionary algorithm (DEA) combined with two uncertainty handling techniques, namely, DEA_UHT, is proposed to address these problems. Firstly, to reasonably control the computation cost, the optimal computing budget allocation technique (OCBAT) is applied for allocating limited computation budgets to assure reliable evaluation and identification for excellent solutions or individuals, and the hypothesis test technique (HTT) is added to execute a statistical comparison to reduce some unnecessary repeated evaluation. Secondly, a reentrant‐largest‐order‐value rule is designed to convert the DEA’s individual (i.e., a continuous vector) to the SRJSSP’s solution (i.e., an operation permutation). Thirdly, a conventional active decoding scheme for the job shop scheduling problem is extended to decode the solution for obtaining the criterion value. Fourthly, an Insert‐based exploitation strategy and an Interchange‐based exploration strategy are devised to enhance DEA’s exploitation ability and exploration ability, respectively. Finally, the test results and comparisons manifest the effectiveness and robustness of the proposed DEA_UHT.