面向时空信息规划的多目标模型预测树搜索
Multi-Objective and Model-Predictive Tree Search for Spatiotemporal Informative Planning
AI总结：
针对机器人环境监测中时空变化环境下自适应采样规划的决策易失效问题，提出融合多目标优化与前瞻模型预测奖励的蒙特卡洛树搜索方法，利用预测时空环境过程，在相关指标上优于基线方法。
摘要：
机器人环境监测中的自适应采样与规划在目标环境过程随时空变化时颇具挑战。底层环境动态要求规划模块整合未来环境变化，以免较早做出的行动决策很快失效。我们提出一种蒙特卡洛树搜索方法，该方法不仅能在空间上很好地平衡环境探索与利用，还能跟上环境的时间动态变化。这一效果通过引入多目标优化和前瞻式模型预测奖励机制实现。我们表明，通过让机器人利用模拟和预测的时空环境过程，所提出的信息规划方法在环境模型均方根误差及与真值的距离指标上，相较于其他基线方法取得了更优性能。
英文摘要：
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planning module to integrate future environmental changes so that action decisions made earlier do not quickly become outdated.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.
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