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Affiliation of Author(s):School of Computer Science and Information Engineering, Hefei University of Technology
Journal:2022 IEEE 8th International Conference on Computer and Communications (ICCC)
Key Words:edge computing, deep Q-Iearning, task offloading, social force model
Abstract: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.
Co-author:Lusheng Wang,Caihong Kai,Min Peng
First Author:Dawen Zheng
Indexed by:Essay collection
Discipline:Engineering
Document Type:C
Page Number:2271-2275
ISSN No.:978-1-6654-5051-5
Translation or Not:no
Date of Publication:2022-12-09
Included Journals:EI