Active Object Detection Based on PPO Learning Algorithm with Decision Knowledge Guidance  

作　　者：Fujing Yao Guohui Tian Yuhao Wang Ning Yang 

机构地区：[1]School of Control Science and Engineering,Shandong University,Jinan 250061,China

出　　处：《Machine Intelligence Research》2025年第2期386-396,共11页机器智能研究(英文版)

基　　金：supported in part by the National Natural Science Foundation of China(Nos.62273203 and U1813215);in part by the Special Fund for the Taishan Scholars Program of Shandong Province,China(No.ts2015110005).

摘　　要：After detecting a target object,a service robot must approach the target object to perform the associated service task.In active object detection(AOD)tasks,effective feature information representation and comprehensive action execution strategies are crucial.Currently,most AOD tasks are accomplished by traditional reinforcement learning algorithms,but there are still problems such as high task failure rates and model training efficiency.To solve these problems,this paper proposes a combined data-driven and knowledge-guided solution.First,semantic information features,depth information features and target object bounding box information are used as inputs to comprehensively represent feature information.Second,a policy network is constructed based on the proximal policy optimizaton(PPO)algorithm.The reward value is set according to the robot′s action,the position of the bounding box,and the distance to the target object,and then applied to the robot′s training process.Finally,the knowledge of the path experience in the task,the robot′s collision avoidance ability and the prediction of target object loss are combined to guide the robot′s behavior,and a comprehensive decision model is proposed to enable the robot to make the best decision.Relevant experiments were conducted on an active vision dataset.The robot achieves an average success rate of 91.36%and an average step size of 9.3631 in performing the AOD task in the test scenes,which verifies the effectiveness of the proposed scheme.

关 键 词：Service robot active object detection reinforcement learning path experience comprehensive decision model 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置] TP242[自动化与计算机技术—控制科学与工程]