Our paper on informative path planning is accepted to CDC 2016

[2016.07.23]

The following paper is accepted to IEEE Conference on Decision and Control (CDC 2016):

  • Efficient Graph-Based Informative Path Planning Using Cross Entropy by Junghun Suh, Kyunghoon Cho, and Songhwai Oh
    • Abstract: In this paper, we present a novel informative path planning algorithm using an active sensor for efficient environmental monitoring. While the state-of-the-art algorithms find the optimal path in a continuous space using sampling based planning method, such as rapidly-exploring random graphs (RRG), there are still some key limitations, such as computation complexity and scalability. We propose an efficient information gathering algorithm using an RRG and a stochastic optimization method, cross entropy (CE), to estimate the reachable information gain of each node of the graph. The proposed algorithm maintains the asymptotic optimality of the  RRG planner and finds the most informative path satisfying the cost constraint. We demonstrate that the proposed algorithm finds a (near) optimal solution efficiently compared to the state-of-the-art algorithm and show the scalability of the proposed method. In addition, the proposed method is applied to multirobot informative path planning.