Research on autonomous navigation of mobile robots based on IA-DWA algorithm.

To improve the efficiency of mobile robot movement, this paper investigates the fusion of the A* algorithm with the Dynamic Window Approach (DWA) algorithm (IA-DWA) to quickly search for globally optimal collision-free paths and avoid unknown obstacles in time. First, the data from the odometer and the inertial measurement unit (IMU) are fused using the extended Kalman filter (EKF) to reduce the error caused by wheel slippage on the mobile robot's positioning and improve the mobile robot's positioning accuracy. Second, the prediction function, weight coefficients, search neighborhood, and path smoothing processing of the A* algorithm are optimally designed to incorporate the critical point information in the global path into the DWA calculation framework.

A novel heuristic target-dependent neural architecture search method with small samples.

It is well known that crop classification is essential for genetic resources and phenotype development. Compared with traditional methods, convolutional neural networks can be utilized to identify features automatically. Nevertheless, crops and scenarios are quite complex, which makes it challenging to develop a universal classification method.