A Voronoi Diagram-Based Workspace Partition for Weak Cooperation of Multi-Robot System in Orchard

Abstract

Nowadays, multi-robot systems are being utilized to perform agricultural tasks. It is particularly essential to robotize pesticide spraying because of the risk of poisoning workers. However, the problem is that the human-driven sprayers are big size, and difficult to maneuver in many orchards. Therefore, heavy spraying robots must be replaced with lighter robots. Also, it takes a lot of times to perform spraying using only one sprayer in a large orchard. To achieve this goal, the multi-robot system (MRS) can be applied to the spraying by robot cooperation to improve performance. In this study, we developed a task allocation system based on a Voronoi diagram for a multi-robot spraying system in an orchard. The seed point for area partition using the Voronoi diagram was obtained through node clustering using a k-means clustering algorithm. In the experiment, workspaces were partitioned according to the number of robots, from 2 to 10. A total of four metrics were used to evaluate the performance of the system. The results confirmed that our task allocation system is applicable to real orchards. INDEX TERMS Agricultural robot, multi-robot system, task allocation, area partition, unmanned ground vehicle (UGV).

Research Progress on Synergistic Technologies of Agricultural Multi-Robots

“…Seeded in a field [58]; Agricultural monitored in vineyards [28]; Sprayed in the Orchard [66] Harvested fields (simulation) [88]; Refill scheduling [78] It can be seen from Table 5 that the current task allocation methods for agricultural multi-robots are mainly centralized, and most of them are implemented in the simulation experiment.…”

“…Harvested muddy moss in farmland [13] Plowed [47] None Monitored vineyards [28] Drove in a greenhouse [56] Environmental monitored [49,50] Harvested farmland [48] Collected farmland information [71] Precision irrigation in the vineyards [72] Sprayed in the Orchard [66] Compared with 2D maps [14,[73][74][75], 3D maps can provide more information. By fusing the original measurement or small local maps generated from multiple robots to construct global maps at the same time, or matching the 3D maps constructed by heterogeneous robots, more and more abundant data are obtained, which is a new research direction of mapping.…”

“…Finally, the central controller constructed a 2D map of the environment surrounding the robot. A more scientific approach than manually labeling is the Voronoi diagram [66]. For example, a fruit tree on the robot's driving path was taken as a point in the orchard map, and the two-dimensional coordinates of this point were found in the image acquired by the UAV.…”

“…However, as the number of robots increased, manual task assignment stops when a robot hits a tree or fails to make a reliable turn. Some scholars [66] solved the manual assignment of tasks to multiple robots from the perspective of dividing maps, namely, as many sub-regions as there are robots needed. The edges of these sub-regions are generated from discrete fruit tree points and K-mean clustering points on the map boundary using an integer programming approach.…”

A Path Planning Strategy for Multi-Robot Moving with Path-Priority Order Based on a Generalized Voronoi Diagram

“…For instance, Reference [22] used a Voronoi diagram and the particle swarm optimization algorithm to achieve multi-robot navigation and obstacles avoidance. In paper [23], the Voronoi diagram divides farmland into several areas for multiple robots to perform agriculture tasks. For multi-robot exploration problems, paper [24] used the distributed Voronoi partition to disperse the robot and then used the parametric algorithms to further control the robot's dispersion behavior to achieve the maximum area exploration.…”