Mobile robots not only need to navigate autonomously in complex environments, but also need to avoid collisions with dynamic obstacles, especially pedestrians. This paper realizes dynamic obstacles detection and state...
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Mobile robots not only need to navigate autonomously in complex environments, but also need to avoid collisions with dynamic obstacles, especially pedestrians. This paper realizes dynamic obstacles detection and state estimation algorithm with LiDAR data, and proposes the path planning and obstacle avoidance algorithm based on dynamic obstacle trajectory prediction. The current planning methods of mobile robots are mostly applicable to static scenes and lack adaptability to pedestrian dynamics. The core idea of the proposed algorithm is to guide the mobile robot towards the back of the pedestrian to reduce the psychological pressure on the pedestrian as well as to avoid interfering with the pedestrian's movement and to achieve dynamic obstacle avoidance, which is in line with the idea of going around from the back in the Convention on the International Regulations for the Avoidance of Collisions at Sea (COLREGS). Firstly, the pedestrian detection method is studied on the basis of Lidar data, and the pedestrian motion state is estimated based on Kalman filter to avoid error detection. Secondly, the dynamic window approach (DWA) method is improved and pedestrian dynamic is added with carefully designed algorithm for path planning and obstacle avoidance. Furthermore, to improve the computational efficiency and flexibility, the goa(Grasshopper Optimization algorithm) is used to improve the DWA search schema. Finally, the effectiveness of the proposed method is proved by comparing the existing methods with simulation and experimental tests. The research of this paper can improve the safety of the robot in the human-machine crowd scene.
Managing fluctuations in wind energy to ensure a stable and reliable energy supply remains a significant challenge in the operation of wind generators. Various solutions have been developed to address this problem and...
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Managing fluctuations in wind energy to ensure a stable and reliable energy supply remains a significant challenge in the operation of wind generators. Various solutions have been developed to address this problem and mitigate the impact of wind energy fluctuations, such as the deployment of Flywheel Energy Storage Systems (FESSs). This study focuses on developing an advanced control strategy for a Brushless Dual-Fed Induction Generator (BDFIG) integrated with a Nonlinear Energy Storage System. In the first stage, a robust Sliding Mode Control (SMC)-based nonlinear decoupled control algorithm is designed to efficiently regulate BDFIG operation. The system is further enhanced with Optimal Torque Control (OTC) and Maximum Power Point Tracking (MPPT) to maximize wind energy extraction. In the second stage, a Synergetic Control (SC) strategy is implemented for FESS management, integrated seamlessly into the overall system. SC is selected for its ability to handle parametric and nonparametric uncertainties, offering a robust solution that ensures fast response times and asymptotic stability across varying operating conditions. To optimize parameter tuning within the control system, the Grasshopper Optimization algorithm (goa) is applied, ensuring optimal performance of the proposed framework. After optimization, the SMC settling time was significantly reduced from 0.7 seconds to 19.97 milliseconds, achieving a 96.9% improvement in response speed, while its steady-state error decreased from 0.48 to 0.06, marking an 87.5% reduction in tracking error. Similarly, the SC settling time improved from 0.85 seconds to 0.3 seconds, resulting in a 64.7% faster response, and its steady-state error was minimized from 0.044 to 0.03, enhancing accuracy by 31.8%. These enhancements contributed to a rapid dynamic response, reduced tracking error, and improved overall system stability under variable wind conditions. The proposed system was extensively simulated on MATLAB/Simulink under va
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