Monitoring the health and vigor of grasslands is vital for informing management decisions to optimize rotational grazing in agriculture applications. To take advantage of forage resources and improve land productivity, we require knowledge of pastureland growth patterns that is simply unavailable at the state of the art. In this paper, we propose to deploy a team of robots to monitor the evolution of an unknown pastureland environment to fulfill the above goal. To monitor such an environment, which usually evolves slowly, we need to design a strategy for rapid assessment of the environment over large areas at a low cost. Thus, we propose an integrated pipeline comprising data synthesis, deep neural network training, and prediction along with a multi-robot deployment algorithm that monitors pasturelands intermittently. Specifically, using expert-informed agricultural data coupled with novel data synthesis in ROS Gazebo, we first propose a new neural network architecture to learn the spatiotemporal dynamics of the environment. Such predictions help us to understand pastureland growth patterns on large scales and make appropriate monitoring decisions for the future. Based on our predictions, we then design an intermittent multi-robot deployment policy for low-cost monitoring. Finally, we compare the proposed pipeline with other methods, from data synthesis to prediction and planning, to corroborate our pipeline’s performance. Note to Practitioners—Pasturelands are an integral part of agricultural production in the United States. To take full advantage of the forage resource and avoid environmental degradation, pastureland must be managed optimally. This paper focuses on the question of how to deploy robot teams to sense and model physical processes over varying timescales. The goal of this work is to develop a new integrated pipeline for the long-term deployment of heterogeneous robot teams grounded in the problem of autonomous monitoring in precision grazing to improve land productivity. By using the proposed pipeline in grassland ecosystem management, we will have a better understanding of the physical environment while respecting energy budgets.

In this paper, we formulate and solve the intermittent deployment problem, which yields strategies that couple when heterogeneous robots should sense an environmental process, with where a deployed team should sense in the environment. As a motivation, suppose that a spatiotemporal process is slowly evolving and must be monitored by a multi-robot team, e.g., unmanned aerial vehicles monitoring pasturelands in a precision agriculture context. In such a case, an intermittent deployment strategy is necessary as persistent deployment or monitoring is not cost-efficient for a slowly evolving process. At the same time, the problem of where to sense once deployed must be solved as process observations yield useful feedback for determining effective future deployment and monitoring decisions. In this context, we model the environmental process to be monitored as a spatiotemporal Gaussian process with mutual information as a criterion to measure our understanding of the environment. To make the sensing resource-efficient, we demonstrate how to use matroid constraints to impose a diverse set of homogeneous and heterogeneous constraints. In addition, to reflect the cost-sensitive nature of real-world applications, we apply budgets on the cost of deployed heterogeneous robot teams. To solve the resulting problem, we exploit the theories of submodular optimization and matroids and present a greedy algorithm with bounds on sub-optimality. Finally, Monte Carlo simulations demonstrate the correctness of the proposed method.

In this paper, our motivating application lies in precision agriculture where accurate modeling of forage is essential for informing rotational grazing strategies. Unfortunately, a major difficulty arises in modeling forage processes as they evolve on large scales according to complex ecological influences. As robots can collect data over large scales in a forage environment, they act as a promising resource for the forage modeling problem when combined with a data-driven Gaussian processes (GPs) technique. However, GPs are nonparametric in nature and may be blind to certain nuances of a process that a parameterized expert model may predict well. Indeed, for the forage modeling problem specifically, there exist several highly parameterized models from agricultural experts that exhibit powerful predictive capabilities. Expert models, however, often come with two shortcomings: (1) parameters may be difficult to determine in general; and (2) the model may not make complete spatiotemporal predictions. For example, a stochastic differential equation (SDE) that models the dynamics of the average output of an environment may be available from experts (a typical case). In such cases, we propose to take advantage of both data-driven (GPs) and expert (SDE) models, by fusing data collected by robots, which often yields spatial insight, with models from experienced professionals that often yield temporal insights. Specifically, we propose to leverage Bayesian estimation to combine these two methods, resulting in a posterior prediction that is a hybrid of data-driven and expert models. Finally, we provide simulations to demonstrate the effectiveness of the proposed method.

In this paper, we formulate an environmental sensing problem for multi-robot teams that couples intermittent deployments with the selection of team composition and sensor type over time. We suppose that a multi-robot team needs to autonomously sense an environmental process and find the optimal policy for deploying heterogeneous robots. In addition, heterogeneous robot teams can be composed in various ways by selecting different mobility and sensor types which have varying accuracies and costs, resulting in a more complex problem. The question is then how to find an optimal intermittent deployment and sensor selection policy that captures both cost and estimation accuracy based on partial environmental information. By utilizing structural results from partially observable Markov decision processes (POMDP) and exploiting submodularity, an optimal policy, which minimizes cost while maintaining a high accuracy, can be achieved in this paper. The effectiveness of this method is demonstrated by simulation results and comparisons with naive policies.