Abstract
Multi-robot systems (MRSs) are currently being used to perform agricultural tasks. In this regard, the deployment of heterogeneous MRSs will be essential for achieving more efficient and innovative farming in the future. In this paper, we propose a multiplicatively weighted (MW) Voronoi-based task-allocation scheme for heterogeneous agricultural robots. The seed points for area partitioning using a Voronoi diagram are obtained by performing node clustering using a k-means clustering algorithm. Heterogeneous robots have different specifications for performing various tasks. Thus, the proposed MW Voronoi-based area partitioning for heterogeneous robots is applied by considering various weighting factors. The path for each robot is computed such that the robot follows the nodes, and the computed paths serve as inputs for the workload distribution strategy that assigns paths to the robots. Simulations and field experiments were conducted to verify the effectiveness of the proposed approach.
Highlights
Seeding is a fundamental component of agriculture, and substantial labor is required to seed wide areas
The results show that the task times of the robots are similar, reducing the overall task time, because the numbers of nodes assigned to each robot have different weights depending on the robot specifications, despite the differences
The results confirm that the proposed multi-robot task allocation (MRTA) method is efficient because the tasking time is not significant compared to the difference in the number of nodes
Summary
Seeding is a fundamental component of agriculture, and substantial labor is required to seed wide areas. To effectively carry out the seeding process in large fields, seeding machines have been developed and distributed to farming households [1,2,3]. Human-driven seeding machines are exceedingly large and heavy, with an elevated center of mass that is prone to instability; in addition, they are difficult to maneuver in several types of agricultural fields. Such heavy seeding robots must be replaced with more intelligent and lightweight robots. Applying seeding using only one seeding machine in a large field is highly expensive and time consuming. A multi-robot system can be applied to improve the seeding process through cooperation among the robots
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