Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy-a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method-a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.

Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.