Improved Q-Learning-Based Motion Control for Basketball Intelligent Robots Under Multi-Sensor Data Fusion

Abstract

With the continuous development of science and technology, the application of artificial intelligence in robot technology, especially in the progress of sports robot technology, has received great attention. This development provides new opportunities for the development of intelligent basketball robots. However, achieving high accuracy and stability in motion control remains a challenge in the research field. The motion control technology of basketball intelligent robots is crucial. The research proposes a study on enhancing Q-learning-based action control technology for basketball robots with multi-sensor data fusion to address the problem of inaccuracy and instability. The first step involves examining how multi-sensor data fusion technology can offer more accurate and comprehensive environmental information that can aid the robot in making more precise action decisions. The second area of study involves the development of enhanced algorithms for controlling basketball robots through improved Q-learning techniques. The goal is to increase the accuracy and stability of the robots’ actions. The final focus is on creating a model for controlling the basketball robots’ actions, which is based on the improved Q-learning algorithm. This model effectively increases the accuracy and stability of the robots’ actions. The experiment was conducted on a computer equipped with Intel Core i9-11900K processor and NVIDIA GeForce RTX 3080 graphics card. MATLAB R2023a was used for system parameterization and simulation, while Webots R2023a software was used to simulate robot actions. The sensor adopted Hokuyo URG-04LX-UG01 laser rangefinder, and the robot model was KUKA youBot. At the same time, in order to improve the control performance of basketball intelligent robots, comparisons were made with existing methods such as PID control, trajectory planning, traditional control methods, multi-sensor fusion technology, and visual perception basketball shooting correction technology. The experimental results demonstrated that the enhanced Q-learning algorithm performed exceptionally well, achieving a recognition rate of 99.35%, a response speed of 67.85%, remarkable performance stability of 97.55%, and a low error rate of 7.65%. The improved Q-learning algorithm’s efficiency and stability were confirmed in the application of robot control modeling. Compared to existing methods for controlling basketball robots’ motion, the proposed method can handle environmental uncertainty more effectively and improve decision-making accuracy. The research has several advantages in robot motion control. It enhances the applicability of basketball intelligent robots in actual competitive sports and provides technical support for robot strategy formulation, rapid adaptation, and efficient execution in actual competitions. It serves as a crucial source for future studies on technology for controlling robot movements and the practical implementation of intelligent basketball robots.

Keywords

• Multi-sensor data fusion
• Q-learning algorithm
• motion control
• basketball robot
• DQN algorithm