A procedure to calculate the maximum torque to current ratio operating conditions for the inverter-fed induction machine is proposed. The proposed procedure minimizes the finite element analysis computation load by identifying the variation range of stator current and slip frequency for the entire torque-speed map. Thus, the total calculation time for machine steady state performance estimation can be significantly reduced. The effectiveness of the proposed procedure is validated by experiment.

The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges, such as the need for sophisticated decision making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field.

The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges such as the need for sophisticated decision-making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field.

In this paper, a novel deep reinforcement learning based navigation policy for mobile robot is proposed to address the challenges in multi-dynamic obstacles environments. Firstly, this method designs a risk perception function to evaluate the collision probability (CP) between robot and dynamic obstacles. Then, the observation space with risk perception is designed for the robot with a sense of the danger level between dynamic obstacles. In order to guide the robot to actively avoid such high-risk obstacles, the velocity obstacles(VO)-based reward is used to find desired direction angle only considering these critical obstacles. In addition, a novel safety constraint is formulated based on control barrier function (CBF) theory, it could adaptively adjust the safety distance according to the risk level of dynamic obstacles. The policy is then optimized within a CBF-guided training framework to enhance safety and adaptability in multi-dynamic obstacles environments. A series of simulation and real-world experiments demonstrate that the proposed policy achieves superior performance in both safety and efficiency compared with state-of-the-art methods, and it is well-suited for safety-critical navigation tasks in dense, dynamic environments such as indoor service and warehouse logistics scenarios.