Scalable multirobot planning for informed spatial sampling

This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatials fields. We address the problem efficient data collection using multiple autonomous vehicles and consider effects communication between robots, acting independently, on overall performance team. focus where robots operate independent their teammates, but have ability to communicate current state other neighbors within fixed range. Our proposed approach is adaptive various environmental scenarios, changing robot team configurations, runs in real-time, which are important features many real-world applications. compare our baseline strategies through simulated experiments that utilize models derived from both synthetic field deployment data. The results show even when operating with limited range, thus demonstrating scalability method large-scale environments.