计算机工程2025，Vol.51Issue(4)：85-96,12.DOI:10.19678/j.issn.1000-3428.0069313
嵌入房间类别和边界约束的目标驱动导航算法
Target-driven Navigation Algorithm Embedded with Room Category and Boundary Constraints
摘要
Abstract
In indoor environments,the same object may have completely different uses depending on the room category.Thus,designing target-driven navigation tasks with room category constraints has important applications in robot navigation,smart home,and other fields.To improve the success rate of room category constrained target navigation task,a modular navigation algorithm is designed,combining search and motion control strategies with mapping and room classification modules.Given a navigation task as input,the mapping module combines RGB-D camera data and pose information,to construct an online semantic map that remembers environments that have been explored.The concept of boundary point cluster is proposed to quickly locate the most likely coordinates of the target object on the map as soon as possible when implementing the search strategy based on the proximal policy optimization algorithm framework.The central coordinates of these clusters are used as relay points.According to the number of boundary points contained in each cluster,the exploration value of the central point is evaluated and sorted and used to constrain the global target points.Furthermore,the concept of boundary points is introduced into the reward function of the search policy,to improve the search efficiency when the target points fall within the explored area.In response to the issue of the robot's inability to recognize room categories,YOLOv8_cls is trained to develop a room classification module based on the motion control strategy,to guide the robot towards the global target point to assist in decision-making,thereby better fulfilling navigation requirements.The feasibility of the navigation task and the effectiveness of the algorithm were verified in both simulated and real environments.Experimental results demonstrate that compared to the Semantic Exploration(SemExp)algorithm which employs Deep Reinforcement Learning(DRL)for search strategies,The proposed algorithm achieves faster map exploration and increased navigation success rates for two types of navigation tasks,with and without room category constraints by 2.0％and 4.0％,respectively.It demonstrates a better understanding of semantic information in the environment,enabling the completion of navigation tasks such as target object search in unknown environments.关键词
机器人室内导航/目标驱动/房间类别约束/搜索策略/边界点约束Key words
robot indoor navigation/target-driven/room category constraint/search strategy/boundary point constraint分类
计算机与自动化引用本文复制引用
罗锦源,谷雨..嵌入房间类别和边界约束的目标驱动导航算法[J].计算机工程,2025,51(4):85-96,12.基金项目
浙江省自然科学基金(LY21F030010). （LY21F030010）
