The development of intelligent traffic light control systems is essential for smart transportation management. While some efforts have been made to optimize the use of individual traffic lights in an isolated way, related studies have largely ignored the fact that the use of multi-intersection traffic lights is spatially influenced, as well as the temporal dependency of historical traffic status for current traffic light control. To that end, in this article, we propose a novel Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) framework for effectively capturing the spatio-temporal dependency of multiple related traffic lights and control these traffic lights in a coordinating way. Specifically, we first construct the traffic light adjacency graph based on the spatial structure among traffic lights. Then, historical traffic records will be integrated with current traffic status via Recurrent Neural Network structure. Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method. Finally, the experimental results on both synthetic and real-world data have demonstrated the effectiveness of our STMARL framework, which also provides an insightful understanding of the influence mechanism among multi-intersection traffic lights.

Redundant parameters in robot kinematic error model may affect the accuracy and robustness of identification results, and should be removed before calibration. In this paper, the pose error model of end-effector was derived, and redundant parameters of robot with arbitrary structure were obtained by analyzing the linear correlation columns of Jacobian matrix. To verify the correctness of the proposed method, a self-calibration method based on plane constraint was proposed. The simulation results show that non-redundant parameter identification model can improve the position accuracy regardless of the presence of noise, while the full-parameter identification model is susceptible to noise and the identification results do not converge. The correctness of the parameter redundancy analysis is proved by the experiments on Franka Emika serial robot. After removing redundant parameters, the position errors reduced from 2.41mm to 1.20mm.

As an important part of the smart grid, the operation control of the microgrid group is the key technology to maintain its safe and stable operation. The reasonable distribution of microgrid energy is closely related to the stability of system control. Multiple micro-grids in a certain area are uniformly regulated and controlled to form a micro-grid group operation mode. Aiming at the operation and management requirements of the multi-energy complementary microgrid group, the article proposes a design scheme for the energy management system of the microgrid group. The entire system has high operational reliability and economy, and has certain engineering value for improving the comprehensive benefits of the microgrid.