Efficient Multi-Robot Inspection of Row Crops via Kernel Estimation and Region-Based Task Allocation

Modern agriculture relies on accurate and timely data. Currently, most of this data is gathered using remote sensing, which uses a combination of satellite and aerial imagery. However, ground robots are needed to fill in the gaps for finer ground-level data and the execution of physical tasks such as sample collection. The scales at which crops are produced preclude the inspection of each and every plant, thus requiring the selection of a smaller number of inspection targets. In this paper, we solve this multi-robot inspection problem using a novel task allocation algorithm. The algorithm derives its utility function from a model based on Gaussian process machine learning with a kernel that is learned from previous data. The algorithm also considers the physical limitations of moving within crop rows by dividing the plot into geodesic Voronoi regions based on robot locations. Simulation studies are performed to validate the method.