Robots whose geometric structure does not meet the Pieper criterion are called general robots. For the inverse kinematic operation of general robots, the closed solution method cannot be solved, and the numerical solution calculation amount is too large and the singular position cannot be calculated. To solve this problem, this paper proposes an inverse kinematics calculation method based on improved particle swarm optimization (PSO) algorithm and applicable to general robots. In order to avoid the particle update rate not adapting to each stage of the optimization process, a nonlinear dynamic inertia weight adjustment method based on the concept of similarity is introduced, so that the search process is more robust; in addition, to overcome the problem of local optimal solution At the same time, multiple populations are introduced to perform optimization search at the same time, and the immigration operator is proposed to increase the diversity of the particle population in the iteration. This article uses Comau NJ-220 robot for test verification, compared with the original PSO and multi-subswarm algorithm, the results show that the proposed improved PSO has higher algorithm stability for general robot kinematics inverse solution problems, and can greatly improve the convergence accuracy and speed. This method provides a new solution to the field of robot inverse kinematics, and provides a more efficient and stable kinematics foundation for robot motion planning.

Surface defect detection is of great significance to ensure the quality of steel plate. The surface defects of steel plate are characterized by multiple types, complex and irregular shapes, large scale range, and high similarity with normal regions, resulting in low accuracy of widely used vision based defect detection methods. To overcome these issues, this paper proposes a method of detecting steel plate surface defects based on deformation convolution and background suppression. First, an improved Faster RCNN method with deformable convolution and Region-of-Interest align is proposed to enhance the detection performance for large-scale defects with complex and irregular shapes; Second, a background suppression method is proposed to enhance the discrimination ability between the normal region and the defect region. Experimental results show that, compared with the state-of-the-art methods, the proposed method can significantly improve the defect detection performance of steel plate.

To optimize the energy efficiency of edge computing system with energy harvesting, this paper proposes an energy-efficient task offloading method optimized by differential evolution. First, a wireless edge computing network model is established to analyze the energy harvesting, task offloading and task calculation of the system, as well as the total number of calculated bits and total energy consumption of the system. Second, according to the total number of calculated bits and total energy consumption of the system, an objective function is established to optimize the energy efficiency of system, and a differential evolution based optimization method is proposed, with which the optimal energy efficiency of system calculation, offloading time, calculation time and frequency are obtained. Experimental results show that the proposed method can not only achieve better convergence effect, but also can effectively solve the energy shortage problem of the micro-equipment and extend the service life of the equipment.

In this article, the Round-Robin (RR) based cooperative resilient secondary control problem for ac/dc microgrid (MG) under false data injection (FDI) attacks is solved. To solve the problem, a cooperative resilient control method is proposed, which consists of a decentralized iterative observer, a RR scheduling protocol, and a distributed resilient secondary controller. Specifically, a decentralized iterative observer is first designed to estimate the state of the ac main bus voltage amplitude, ac bus frequency, bidirectional interlinking converter (BIC) active/reactive power, and FDI attacks. Then, a RR scheduling protocol is introduced to reduce communication among BICs. Based on the designed iterative observer and the RR scheduling protocol, a distributed resilient secondary controller is designed to compensate the influence of FDI attacks. Compared with existing resilient control strategies in hybrid ac/dc MGs under FDI attacks, the proposed resilient cooperative control method can exhibit better transient performance with less communication. Finally, the efficacy of the proposed approach is confirmed through experiments in a real-time simulator OPAL-RT.

Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficiency, slow convergence speed, and even inability to converge, which seriously reduces the performance and practicability of it. To solve these problems, this paper proposes an Improved Dyna-Q algorithm for AGV path planning in large complex dynamic environments. First, to solve the problem of the large search space, this paper proposes a global path guidance mechanism based on heuristic graph, which can effectively reduce the path search space and, thus, improve the efficiency of obtaining the optimal path. Second, to solve the problem of the sparse reward function in Dyna-Q, this paper proposes a novel dynamic reward function and an action selection method based on the heuristic graph, which can provide more intensive feedback and more efficient action decision for AGV path planning, effectively improving the convergence of the algorithm. We evaluated our approach in scenarios with static obstacles and dynamic obstacles. The experimental results show that the proposed algorithm can obtain better paths more efficiently than other reinforcement-learning-based methods including the classical Q-Learning and the Dyna-Q algorithms.