Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows
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所属单位：School of Computer Science and Information Engineering, Hefei University of Technology
发表刊物：2022 IEEE 8th International Conference on Computer and Communications (ICCC)
关键字：edge computing, deep Q-Iearning, task offloading, social force model
摘要：Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay.
合写作者：Lusheng Wang,Caihong Kai,Min Peng
第一作者：Dawen Zheng
论文类型：论文集
学科门类：工学
文献类型：C
页面范围：2271-2275
ISSN号：978-1-6654-5051-5
是否译文：否
发表时间：2022-12-09
收录刊物：EI