Informative path planning for unmanned aerial vehicles using cost-benefit spanning tree

Abstract Informative path planning (IPP) is one of key applications for unmanned aerial vehicles. It can be applied to terrain monitoring problems, which are to find the static targets from bird’s eye view. In this research, the proposed algorithm generates the cost-benefit spanning tree (CBST) to boost the IPP performance. The CBST is able to generate different tree structures based on different parameters. The proofs show that the theoretical guarantees depend on the tree structures (e.g., minimal spanning tree and shortest path tree). The simulations and experiments demonstrate that the proposed method outperforms the benchmark approaches.