A micro unmanned aerial vehicle (UAV) only equipped with a monocular camera is hard to accomplish a flying task with obstacles avoidance and target tracking simultaneously. In this article, a bionic dynamic path planning algorithm was developed for cooperation of obstacles avoidance and target tracking. An improved bat algorithm (BA) optimized transfer learning convolutional neural network (CNN) and bio-inspired optical flow balance algorithm was combined for obstacles avoidance. The Hawk-eye algorithm with line of sight (LOS) tracking rules is aimed at UAV dynamic tracking with obstacles avoidance. All of perception information, including avoidance and tracking were fused in UAV motion decision phase. The experiments include “obstacles avoidance” and “obstacles avoidance + target tracking” parts. Comparing with manual control and other algorithms, the bionic dynamic path planning algorithm in this article showed certain advantages in success rate, less obstacles collisions, and less major accidents.

Compound-Gaussian model with the inverse Gaussian texture (IG-CG) is recognized to be one of the best models to characterize high-resolution sea clutter at low grazing angles. The model parameters are often estimated by the second- and fourth-order amplitude sample moments, which are of low precision and easily interfered by outliers of high power such as returns of ships and reefs and sea spikes. In this letter, an iterative maximum likelihood (ML) estimator and an outlier-robust bipercentile estimator are proposed and are compared with the moment-based estimator. The experimental results show that the iterative ML estimator is better in performance than the moment-based estimator when samples are without outliers and the bipercentile estimator behaves better when samples contain a small number of outliers.

The binocular stereo vision is the lowest cost sensor for obtaining 3D information. Considering the weakness of long‐distance measurement and stability, the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture. To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade, inspired by hawk eyes, higher resolution perception and the adaptive HDR (High Dynamic Range) were introduced in this paper. Simulating the function from physiological structure of ‘deep fovea’ and ‘shallow fovea’ of hawk eye, the higher resolution reconstruction method in this paper was aimed at accuracy improving. Inspired by adjustment of pupils, the adaptive HDR method was proposed for high dynamic range optimisation and stable perception. In various light conditions, compared with default stereo vision, the accuracy of proposed algorithm was improved by 28.0% evaluated by error ratio, and the stability was improved by 26.56% by disparity accuracy. For fixed distance measurement, the maximum improvement was 78.6% by standard deviation. Based on the hawk‐eye‐inspired perception algorithm, the point cloud of orchard was improved both in quality and quantity. The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud reconstruction in orchard navigation map.

Improving irrigation efficiency in order to balance water supply and demand has become an urgent need for social development in the northwest of China. In this paper, we propose an irrigation water distribution model based on the genetic backtracking search algorithm (GBSA). This algorithm is composed of two main modules, the vector evaluation genetic algorithm (VEGA) and the backtracking search algorithm (BSA). We applied the GBSA model in the Xijun irrigation district of Heihe River Basin. The VEGA module was first used to optimize water distribution in the irrigation district, and ensure a uniform flow rate and a minimum hydraulic loss in the main canal. Moreover, the advantage of BSA module in rapid water distribution was utilized to further improve the overall water distribution velocity of the GBSA model. To evaluate the performance of the GBSA, both the grey relational analysis and the TOPSIS method were used to comprehensively evaluate its various indicators. The results show that the GBSA can meet the water distribution requirements of the whole canal irrigation system, maintaining uniform flow rate, minimizing unused water and water distribution time to optimize irrigation water distribution. At the same time, the GBSA has better performance compared to other existing methods for irrigation water distribution.