Open AccessOptimized Offline-Coverage Path Planning Algorithm for Multi-Robot for Weeding in Paddy Fields
Author(s) -
Murugaraj Govindaraju,
Daniele Fontanelli,
S. Selva Kumar,
Anju. S. Pillai
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3322230
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The coverage path planning (CPP) algorithms play a key role in autonomous robot applications, making area coverage operations efficient and cost-effective. The extension of coverage path planning algorithms to multi-robot operation is still widely unveiled despite the cyclical nature of agricultural operations, i.e., comprising repeated actions. The problem of coverage path planning for multi-robot operations is addressed in this paper. The three possible forms of multi-robot coverage algorithms evolved from the basic single-robot coverage algorithm based on the elementary trapezoidal method or zig-zag movements. Furthermore, an optimized coverage path planning algorithm for multiple in-row robots meant to control the weeding in an agricultural field is proposed. The parameters of the agricultural field are supposed to be known upfront, opening the application of an offline planning algorithm. The proposed algorithm stands tall in terms of distance covered with no repeated coverage compared with other possible solutions, nearing the results of single robot coverage (for which the planning is trivially simpler and there is no coverage repetition). Online adjustments in the multi-robot area coverage are also considered, and the proposed algorithm proves to be effective in simulation in this respect as well. The quantitative evaluation proves that, in the proposed algorithm with a team size of 15(n = 15), the average distance consumed by each robot to cover the field taken for the study is only 65% of that of the other two algorithms. Also shows increase in the team size (n) leads to a decrease in consumption. This algorithm provides a solution for the autonomous operation of multi-robots to cover the fields with static obstacles at a regular pattern which is a common demand of many agricultural processes.