This work presents a strategy for coordinated multi-agent weeding under conditions of partial environmental information. Our goal is to demonstrate the feasibility of coordination strategies for improving the weeding performance of autonomous agricultural robots. We show that, given a sufficient number of agents, the algorithm 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 coordination between agents is demonstrated to strongly improve system performance as the number of agents increases, enabling the system to eliminate all the weeds in the field, as in the case of full environmental information, when the planner without coordination failed to do so.
This work presents advances in predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process (E-GP) method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment, Weed World. This method also provides physical insight into the seed bank distribution of the field. In this work, we extend the E-GP model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Second, we show that one may decouple the component of the model representing weed growth from the component, which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model and show that the E-GP method can drive down the total weed biomass for fields with high seed bank densities using less agents, without assuming this model information. We use an improved planning index, the Whittle index, which allows a balanced tradeoff between exploiting a row or allowing it to accrue reward and conforms to what we show is the theoretical limit for the fewest number of agents, which can be used in this domain.
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