Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning
This work addresses the challenge of balancing exploration and exploitation in environmental monitoring for robots, representing an incremental improvement over existing methods.
The paper tackles the problem of multi-objective informative planning for environmental monitoring robots by introducing Pareto Monte Carlo Tree Search, which optimizes decisions for competing objectives like exploration and exploitation, resulting in logarithmically bounded sub-optimal node selections and polynomial convergence to optimal choices.
In many environmental monitoring scenarios, the sampling robot needs to simultaneously explore the environment and exploit features of interest with limited time. We present an anytime multi-objective informative planning method called Pareto Monte Carlo tree search which allows the robot to handle potentially competing objectives such as exploration versus exploitation. The method produces optimized decision solutions for the robot based on its knowledge (estimation) of the environment state, leading to better adaptation to environmental dynamics. We provide algorithmic analysis on the critical tree node selection step and show that the number of times choosing sub-optimal nodes is logarithmically bounded and the search result converges to the optimal choices at a polynomial rate.