基于FA-A*优化算法的实验样品配送机器人控制系统设计

为保证配送机器人能够安全稳定的将实验样品送达至指定位置,利用FA-A*优化算法,从硬件和软件两个方面优化设计实验样品配送机器人控制系统。改装配送机器人位姿传感器、数据处理器、电机驱动器和控制器等设备元件,调整系统电路的连接方式,完成硬件系统的优化。采用栅格法搭建配送机器人移动环境模型,通过图像采集、特征提取与特征匹配等环节,识别实验样品配送对象的具体位置。以实验样品当前位置为起点、配送终端位置为终点,利用FA-A*优化算法规划机器人配送路径,结合机器人实时位姿的跟踪结果,计算机器人控制量,最终从位置/速度、平衡、自主搭乘电梯等方面,实现配送机器人的控制功能。通过系统测试实验得出结论：综合静态障碍物和动态障碍物两个实验场景,与传统控制相比,在优化设计系统控制下,配送机器人的位置和速度控制误差分别降低约14m和0.38m/s。

Design of Control System for Experimental Sample Delivery Robot Based on FA-A * Optimization Algorithm

To ensure that the delivery robot can safely and stably deliver experimental samples to the designated location, the FA-A * optimization algorithm is used to optimize and design the control system of the experimental sample delivery robot from both hardware and software aspects. Modify equipment components such as pose sensors, data processors, motor drivers, and controllers for the delivery robot, adjust the connection method of the system circuit, and complete the optimization of the hardware system. Build a mobile environment model for the delivery robot using the grid method, and identify the specific location of the experimental sample delivery object through image acquisition, feature extraction, and feature matching. Starting from the current position of the experimental sample and ending at the delivery terminal, the FA-A * optimization algorithm is used to plan the robot"s delivery path. Combined with the real-time tracking results of the robot"s posture, the robot"s control amount is calculated. Finally, the control function of the delivery robot is achieved from aspects such as position/speed, balance, and autonomous elevator riding. Through system testing experiments, it was concluded that, compared to traditional control, the position and speed control errors of the delivery robot were reduced by approximately 14m and 0.38m/s respectively under the optimized design system control, combining static and dynamic obstacle experimental scenarios.