次模樹於資訊軌跡規劃

The AI community has been paying more attention to informative path planning (IPP). IPP is to plan trajectories for robots to maximize information gathering and subject to a path cost. Potential applications include cooperative map exploration, spatial search and disinfection robots etc. However, finding optimal solutions for the aforementioned problems are to solving two NP-hard problems. To make a breakthrough of the IPP research status, this research proposes the submodular tree structure for path cost functions instead of focusing on information functionsas prior work and further analyzes it via Fourier methods. The goal of this research is to explore some issues of IPP:(1)When the submodular tree is adopted as path cost functions, what’s theboosting theoretical guarantees?(2)What’s the sparsity of the submodular tree in the Fourier domain?(3)For transfer learning applications, when the environments are changed, what’sthe invariant property for IPP problems?(4)When the problem is multiple robots IPP, how to distribute multiplesubmodular trees?