This work presents a strategy for coordinated multi-agent weeding under conditions of partial environmental information. The goal is to demonstrate the feasibility of coordination strategies for improving the weeding performance of autonomous agricultural robots. It is shown that, given a sufficient number of agents, a team of autonomous robots can successfully weed fields with various initial seed bank densities, even when multiple days are allowed to elapse before weeding commences. Furthermore, the use of information sharing between agents is demonstrated to strongly improve system performance as the number of agents increases. As a domain to test these algorithms, a simulation environment, Weed World, was developed, which allows real-time visualization of coordinated weeding policies, and includes realistic weed generation. In this work, experiments are conducted to determine the required number of agents for given initial seed bank densities and varying allowed days before the start of the weeding process.
ASJC Scopus subject areas
- Agronomy and Crop Science
- Computer Science Applications