Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes

Motivated by the above limitations of prior IPP approaches, Environmental monitoring problems require estimating the we present a method that can efficiently generate current state of phenomena, such as temperature, precipitation, both discrete and continuous sensing paths, accommodate ozone concentration, soil chemistry, ocean salinity, constraints such as a distance budget and velocity limits, and fugitive gas density ([1], [2], [3], [4]). These problems handle point sensors and non-point FoV sensors, and handle are closely related to the informative path planning (IPP) both single and multi-robot IPP problems. Our approach problem ([1], [5]) since it is often the case that we have leverages gradient descent optimizable sparse Gaussian processes limited resources and, therefore, must strategically determine to solve the IPP problem, making it significantly the regions from which to collect data and the order in which faster compared to prior approaches and scalable to large to visit the regions to efficiently and accurately estimate the IPP problems.