  This paper addresses multi-robot informative path planning (IPP) for
environmental monitoring. The problem involves determining informative regions
in the environment that should be visited by robots to gather the most
information about the environment. We propose an efficient sparse Gaussian
process-based approach that uses gradient descent to optimize paths in
continuous environments. Our approach efficiently scales to both spatially and
spatio-temporally correlated environments. Moreover, our approach can
simultaneously optimize the informative paths while accounting for routing
constraints, such as a distance budget and limits on the robot's velocity and
acceleration. Our approach can be used for IPP with both discrete and
continuous sensing robots, with point and non-point field-of-view sensing
shapes, and for both single and multi-robot IPP. We demonstrate that the
proposed approach is fast and accurate on real-world data.
