Informative Path Planning

Informative path planning (IPP) focuses on designing optimal routes for robots or sensors to efficiently gather valuable data in unknown or partially known environments, maximizing information gain while adhering to resource constraints like time or energy. Current research emphasizes the use of reinforcement learning (RL), particularly deep RL and offline RL methods, along with techniques like Monte Carlo Tree Search and Bayesian optimization, to address challenges in multi-robot coordination, adaptive planning in dynamic environments, and efficient sensor utilization. These advancements are significantly impacting various fields, including precision agriculture, environmental monitoring, space exploration, and autonomous navigation, by enabling more efficient and effective data collection for improved decision-making and task completion.