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.

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.

In some specific conditions, UAVs are required to obtain comprehensive information of an area or to operate in the area in an all-round way. In this case, the coverage path planning (CPP) is required. This paper proposes a solution to solve the problem of short endurance time in the coverage path planning (CPP) problem of multi-solar unmanned aerial vehicles (UAVs). Firstly, the energy flow efficiency based on the energy model is proposed to evaluate the energy utilization efficiency during the operation. Moreover, for the areas with and without obstacles, the coverage path optimization model is proposed based on the undirected graph search method. The constraint equation is defined to restrict the UAV from accessing the undirected graph according to certain rules. A mixed integer linear programming (MILP) model is proposed to determine the flight path of each UAV with the objective of minimizing operation time. Through the simulation experiment, compared with the Boustrophedon Cellular Decomposition method for coverage path planning, it is seen that the completion time is greatly improved. In addition, considering the impact of the attitude angle of the solar powered UAV when turning, the operation time and the total energy flow efficiency are defined as the optimization objective. The bi-objective model equation is established to solve the problem of the CPP. A large number of simulation experiments show that the optimization model in this paper selects different optimization objectives and applies to different shapes of areas to be covered, which has wide applicability and strong feasibility.

Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model; meanwhile, the density of the crop point cloud was quadrupled.

Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has been seriously damaged. The implementation of grazing prohibition, rotational grazing and the development of a large-scale breeding industry have not only ensured the supply of animal husbandry products, but also promoted the restoration of the grassland ecosystem. For the large-scale breeding industry, the animal welfare of livestock cannot be guaranteed due to the narrow and crowded space, thus, the production of the breeding industry usually has lower competitiveness than grazing. Disorderly grazing leads to grassland ecological crises; however, intelligent grazing can not only ensure animal welfare, but also fully improve the competitiveness of livestock husbandry products. Under the development of urbanization, the workforce engaged in grazing and breeding in pastoral areas is gradually lost. Intelligent grazing breeding methods need to be developed and popularized. This paper focuses on intelligent grazing, reviews grass remote sensing and aerial seeding, wearable monitoring equipment of livestock, UAV monitoring and intelligent grazing robots, and summarizes the development of intelligent grazing elements, exploring the new development direction of automatic grazing management with the grazing robot at this stage.