  In multi-robot informative path planning the problem is to find a route for
each robot in a team to visit a set of locations that can provide the most
useful data to reconstruct an unknown scalar field. In the budgeted version,
each robot is subject to a travel budget limiting the distance it can travel.
Our interest in this problem is motivated by applications in precision
agriculture, where robots are used to collect measurements to estimate
domain-relevant scalar parameters such as soil moisture or nitrates
concentrations. In this paper, we propose an online, distributed multi-robot
sampling algorithm based on Monte Carlo Tree Search (MCTS) where each robot
iteratively selects the next sampling location through communication with other
robots and considering its remaining budget. We evaluate our proposed method
for varying team sizes and in different environments, and we compare our
solution with four different baseline methods. Our experiments show that our
solution outperforms the baselines when the budget is tight by collecting
measurements leading to smaller reconstruction errors.
