Robotic Technologies in Horticulture: Analysis and Implementation Prospects

Abstract

The article contains an analytical review and perspectives of robotic technologies in horticulture. Trends in the growth of production, implementation, and sales of robots in various regions of the world are revealed. The analysis showed a lag in the introduction of agricultural robots compared to other sectors of the economy, as well as a significant gap between the countries of the Asian region and other continents. A review of technical means of three main components of ground agricultural robots is considered: navigation systems, sensors, and platform design. Examples of constructing a tree trajectory using the A* algorithm and using the Rviz visualization tools and the Github PathFindings graphical web service are given. As a result of the conducted research, the use of Lidar sensors is recommended, which will make it possible to design the route of robotic platforms, build maps by scanning a previously unknown surrounding space and updating the resulting map at each step of the algorithm in real time. The use of existing modern sensors with an optical rangefinder with a resolution of 4.5 million pixels, a frame rate of 25 frames per second and the ability to automatically adapt to the light level in combination with stereo cameras and GPS/GLONASS navigation will improve the positioning accuracy of robotic platforms and ensure autonomous operation. To perform basic technological operations for the care of plantings with row spacing of 2.5-4 m, a tree crown height up to 3-3.5 m with intensive technologies, the following design parameters of a robotic platform are required: agro-treatment of at least 1200 mm, adjustable track width of 1840-2080 mm, weight not more than 400 kg, load capacity not less than 1000 kg, the power of the power plant is not less than 5 kW.

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

“…An example of finding a path in a graph with obstacles (1-10) using the various algorithms of the Github PathFindings web service is shown in our previous work [18]. Based on the results of finding a path in a graph with obstacles by comparing four algorithms in the Github PathFindings web service, we obtained the data presented in Table 1.…”

“…A comparative analysis of various types of sensors used for the navigation of robotic platforms is given by the authors of [18][19][20][21] and shows that LiDAR sensors have an advantage over other sensors and cameras for the navigation of robotic systems in horticulture. Analyses of existing control systems for autonomous vehicles and studies conducted in different countries [22][23][24][25] have shown that the following types of algorithms can be used for navigation and control in industrial gardens: the hybrid-navigation, Dijkstra, potentialfield, SLAM and vector pursuit algorithms, among others [18].…”

“…A comparative analysis of various types of sensors used for the navigation of robotic platforms is given by the authors of [18][19][20][21] and shows that LiDAR sensors have an advantage over other sensors and cameras for the navigation of robotic systems in horticulture. Analyses of existing control systems for autonomous vehicles and studies conducted in different countries [22][23][24][25] have shown that the following types of algorithms can be used for navigation and control in industrial gardens: the hybrid-navigation, Dijkstra, potentialfield, SLAM and vector pursuit algorithms, among others [18]. An analysis of the work of well-known algorithms [18] showed that the use of the A* algorithm to traverse the graph and find the optimal path makes it possible to determine, with a high degree of accuracy, the location of an autonomous vehicle in field conditions when it moves between rows and moves to another row to collect fruits, monitor trees, prune, spray and perform other tasks [18].…”

“…Sensors 2024, 24, x FOR PEER REVIEW 3 of 28 in different countries [22][23][24][25] have shown that the following types of algorithms can be used for navigation and control in industrial gardens: the hybrid-navigation, Dijkstra, potential-field, SLAM and vector pursuit algorithms, among others [18]. An analysis of the work of well-known algorithms [18] showed that the use of the A* algorithm to traverse the graph and find the optimal path makes it possible to determine, with a high degree of accuracy, the location of an autonomous vehicle in field conditions when it moves between rows and moves to another row to collect fruits, monitor trees, prune, spray and perform other tasks [18]. The most modern tool used for making decisions on process management in agronomy and horticulture is machine learning.…”

European Green Deal: Improving the Efficiency of Using Planetary Hydraulic Machines

“…Technical progress, which reduces economic and energy costs and negative environmental consequences, and increases the efficiency of the use of technical means, is foreseen by many economic and industrial development programs of various countries, for example, the "European Green Deal" [1]. Some of the relevant scientific and practical directions are research devoted to running modules [2], mechatronic systems of self-propelled and industrial machines [3], robots [4,5], etc., which require the use of efficient hydraulic motors.…”