Semantic-Aware Informative Path Planning for Efficient Object Search Using Mobile Robot

Abstract

In this article, a novel informative path planning (IPP) framework is proposed for efficient robotic object search. We innovatively reformulate the object search into an IPP problem, which takes account of the knowledge of possible target object locations. To model the target object distribution knowledge, the semantic information of the focused environment is utilized to obtain the probabilities of finding the target object at possible locations. Then, the probability distribution is modeled by Gaussian mixture model (GMM) to generate an information map. Based on the map, a sampling-based IPP method is proposed to minimize the object search cost. It is worth noting that the object search path is planned with a tree structure and evaluated by a utility function that concerns both search information gain and path cost. Moreover, to improve the quality of the search path, a novel informative sampling strategy and a rewire mechanism are conceived. The performance of the proposed object search framework is fully evaluated through both simulation experiments and real-world tests with a mobile robot platform. Results demonstrated that our method can find the target object efficiently and robustly with shorter path length than three comparative methods in the literature and the mobile robot shows human-like behavior when searching for the target object.