Long-Term Dynamic Object Relocalization for Mobile Robots in Human–Robot Coexisting Environments

This article proposes a long-term dynamic object relocalization (L-DOR) solution to address the challenge of mobile robots efficiently relocalize task-related objects in human–robot coexisting environments over extended periods. Existing methods mainly focus on one-time object localization, neglecting long-term relocalization amidst dynamic changes caused by human activities. To tackle this issue, a probabilistic model of object distribution based on spatio-temporal patterns is first established. Then, a robot-object perception interaction is introduced to achieve dynamic object category discrimination, enabling the robot to handle sporadic events caused by human activities. Besides, a cost-expectation balance-based object matching method is presented to determine the suitable task object and gradually infer its potential locations with probabilistic model. On this basis, we suggest a hierarchical global planning and local decision-making to prioritize search efforts to improve localization efficiency. Extensive comparisons and long-term experiments in real-world scenarios with a Fetch robot demonstrate the efficacy of L-DOR in terms of search performance, adaptability to different scenarios, and long-term effectiveness.