The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Agricultural machinery automatic navigation technology

In this paper, we review, compare, and analyze previous studies on agricultural machinery automatic navigation and path planning technologies. First, the paper introduces the fundamental components of agricultural machinery autonomous driving, including automatic navigation, path planning, control systems, and communication modules. Generally, the methods for automatic navigation technology can be divided into three categories: Global Navigation Satellite System (GNSS), Machine Vision, and Laser Radar. The structures, advantages, and disadvantages of different methods and the technical difficulties of current research are summarized and compared. At present, the more successful way is to use GNSS combined with machine vision to provide guarantee for agricultural machinery to avoid obstacles and generate the optimal path. Then the path planning methods are described, including four path planning algorithms based on graph search, sampling, optimization, and learning. This paper proposes 22 available algorithms according to different application scenarios and summarizes the challenges and difficulties that have not been completely solved in the current research. Finally, some suggestions on the difficulties arising in these studies are proposed for further research.

Mobile robotics in smart farming: current trends and applications

In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

Research on the local path planning of an orchard mowing robot based on an elliptic repulsion scope boundary constraint potential field method

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.

End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network Considering Traversability Cost

Path planning plays an important role in navigation and motion planning for robotics and automated driving applications. Most existing methods use iterative frameworks to calculate and plan the optimal path from the starting point to the endpoint. Iterative planning algorithms can be slow on large maps or long paths. This work introduces an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN) for grid maps with the concept of the traversability cost, and this trains a general path-planning model for 10 × 10 to 80 × 80 square and rectangular maps. The algorithm outputs the lowest-cost path while considering the cost and the shortest path without considering the cost. The FCNN model analyzes the grid map information and outputs two probability maps, which show the probability of each point in the lowest-cost path and the shortest path. Based on the probability maps, the actual optimal path is reconstructed by using the highest probability method. The proposed method has superior speed advantages over traditional algorithms. On test maps of different sizes and shapes, for the lowest-cost path and the shortest path, the average optimal rates were 72.7% and 78.2%, the average success rates were 95.1% and 92.5%, and the average length rates were 1.04 and 1.03, respectively.

Efficient Data-Driven Crop Pest Identification Based on Edge Distance-Entropy for Sustainable Agriculture

Human agricultural activities are always accompanied by pests and diseases, which have brought great losses to the production of crops. Intelligent algorithms based on deep learning have achieved some achievements in the field of pest control, but relying on a large amount of data to drive consumes a lot of resources, which is not conducive to the sustainable development of smart agriculture. The research in this paper starts with data, and is committed to finding efficient data, solving the data dilemma, and helping sustainable agricultural development. Starting from the data, this paper proposed an Edge Distance-Entropy data evaluation method, which can be used to obtain efficient crop pests, and the data consumption is reduced by 5% to 15% compared with the existing methods. The experimental results demonstrate that this method can obtain efficient crop pest data, and only use about 60% of the data to achieve 100% effect. Compared with other data evaluation methods, the method proposed in this paper achieve state-of-the-art results. The work conducted in this paper solves the dilemma of the existing intelligent algorithms for pest control relying on a large amount of data, and has important practical significance for realizing the sustainable development of modern smart agriculture.