基于多源信息融合的温室芦笋采收机器人自主导航控制方法OA

Aiming to address the challenges of localization accuracy degradation caused by slippage on wet soil and significant environmental noise in greenhouse conditions,an autonomous navigation method that fused data from multiple sensors was proposed,including IMU,odometry,and LiDAR.At the local localization level,an extended Kalman filter(EKF)algorithm incorporating a slippage coefficient was designed to achieve high-precision fusion of IMU and odometry data.At the global localization level,an adaptive Monte Carlo localization(AMCL)algorithm was optimized by using a mutation-crossover mechanism to enhance the global search capability and robustness of the particle filter.For path planning,an improved artificial potential field method based on target direction coupling was proposed to overcome the local minima problem of traditional methods,thereby improving path stability and environmental adaptability.Experimental results showed that the improved AMCL algorithm achieved localization standard deviations of 0.060 m in the x-direction and 0.027 m in the y-direction,significantly outperforming conventional methods.The EKF fusion algorithm achieved localization standard deviations of 0.120 m and 0.022 m in the x and y directions,respectively,surpassing standalone IMU and odometry-based localization.At three driving speeds(0.1 m/s,0.2 m/s,and 0.3 m/s),the average lateral and longitudinal tracking errors were maintained within 0.121 m and 0.096 m,respectively,with a maximum heading angle deviation of no more than 17.027°.The proposed method demonstrated good localization accuracy and path tracking performance,significantly improving the operational reliability of the robot in slippery environments.It met the requirements of high-precision mapping,localization,and navigation for greenhouse autonomous walking systems and provided theoretical and technical support for the deployment of harvesting robots in agricultural environments.