控制理论与应用2026，Vol.43Issue(5)：1043-1051,9.DOI:10.7641/CTA.2025.40013
基于概率语义模型的最优期望时间目标搜索
Optimal expected-time based target search strategy with a probabilistic semantic model
摘要
Abstract
This paper proposes a novel method for 3D probabilistic discrete bi-level programming(3D-PDBP)to address the limitations of current discrete target search methods in dynamic 3D environments.The proposed method enables the search for low-maneuverability targets with uncertain positions,particularly suited for scenarios where the target's state changes intermittently in the short term.To dynamically update the distribution probability of the target,the paper introduces a probabilistic dynamic update model HMF-PU that can mimic human memory and a forgetting mechanism is built based on the Ebbinghaus forgetting curve.By combining the target's prior semantic information with distribution probability,the paper constructs a probabilistic semantic map for effective target search.The proposed 3D-PDBP combines observation point search sequence planning with depth vision sensor motion planning,planning the observation point search sequence based on the probabilistic semantic map and orchestrating the servo process of the visual sensor using the target's probability distribution model HMF-PU.Experimental results reveal that 3D-PDBP can accomplish target search tasks efficiently in unpredictable situations,and HMF-PU can successfully balance the importance and real-time performance of target information.关键词
移动机器人/运动规划/概率语义模型/最优期望时间Key words
mobile robot/motion planning/probabilistic semantic model/optimal expected-time引用本文复制引用
李昕哲,张波涛,汪江平,吕强,陈云..基于概率语义模型的最优期望时间目标搜索[J].控制理论与应用,2026,43(5):1043-1051,9.基金项目
国家自然科学基金项目(62073108,U22A2044),浙江省自然科学基金项目(LZ23F030004)资助.Supported by the National Natural Science Foundation of China(62073108,U22A2044)and the Natural Science Foundation of Zhejiang Province(LZ23F030004). （62073108,U22A2044）
