Navigation Strategies for a Multi-Robot Ground-Based Row Crop Phenotyping Platform

In this work, we address the problem of deploying a multi-robot system for row crop phenotyping. We propose a sampling-based navigation algorithm for the team of robots to estimate the underlying spatial distribution in a field. We use Gaussian Process Models to predict the distribution of a scalar function in a field, and choose Mutual Information as a metric for selecting the future samples. With a row crop structure, we present a collision-free assignment and scheduling algorithm for the robots to reach the goal positions which minimizes the total traveling distance. The effectiveness of the proposed algorithm is demonstrated through simulations.