Adaptive sampling and planning in robotic environmental monitoring are challenging when the target environmental process varies over space and time. The underlying environmental dynamics require the planning module to integrate future environmental changes so that action decisions made earlier do not quickly become outdated. We propose a Monte Carlo tree search method which not only well balances the environment exploration and exploitation in space, but also catches up to the temporal environmental dynamics. This is achieved by incorporating multi-objective optimization and a look-ahead model-predictive rewarding mechanism. We show that by allowing the robot to leverage the simulated and predicted spatiotemporal environmental process, the proposed informative planning approach achieves a superior performance after comparing with other baseline methods in terms of the root mean square error of the environment model and the distance to the ground truth.

翻译：在机器人环境监测中进行自适应采样与规划时，若目标环境过程在空间和时间上均存在变化，则面临挑战。潜在的环境动态变化要求规划模块整合未来环境变化，以确保早期制定的行动决策不会迅速过时。我们提出了一种蒙特卡洛树搜索方法，该方法不仅在空间上良好平衡了环境探索与利用，还能适应时间环境动态。这是通过引入多目标优化与前瞻性模型预测奖励机制实现的。研究表明，通过让机器人利用模拟与预测的时空环境过程，所提出的信息规划方法在环境模型的均方根误差以及与真实值的距离方面，相较于其他基准方法取得了更优性能。