2022, Volume 23, Issue 11
Frontiers of Information Technology & Electronic Engineering >> 2022, Volume 23, Issue 11 doi: 10.1631/FITEE.2200109
A deep Q-learning network based active object detection model with a novel training algorithm for service robots
山东大学控制科学与工程学院，中国济南市，250061
Next Previous
Abstract
This paper focuses on the problem of (AOD). AOD is important for to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.
京公网安备 11010502051620号
