Efficient Dynamic Object Search in Home Environment by Mobile Robot: A Priori Knowledge-Based Approach

The capability of object search is a prerequisite for mobile robot to perform everyday tasks in the home environment. Due to the dynamic nature of object and the particularity of home environment, it still remains a challenging problem for mobile robot to find the dynamic target object with the low total cost. To address this problem, a priori knowledge-based approach for dynamic object search is presented in this paper. Inspired by human search process, the common sense knowledge about typical spatial location and the relationship between objects is modeled as a priori knowledge. On this basis, we propose a novel search strategy to improve the search efficiency of mobile robot at the scale of the entire home environment. Unlike the existing methods, a trade-off between the inferred spatial location knowledge and the distance of robot position and candidate room is considered, which permits robot to prioritize the search effort for the candidate room in which the target object is most likely to be found. Also, the search space inside the room is further reduced by using a priori knowledge-guided search heuristics. Meanwhile, the update of knowledge is also investigated to maintain its reliability. The proposed approach is implemented in both simulation and real environments, and evaluated through extensive experiments. The experimental results are provided to demonstrate the feasibility and efficiency of our proposal.