Parallel Task Scheduling in Autonomous Robotic Systems: An Event-Driven Multimodal Prediction Approach
Authors: 
Wen Gao, Zhiwen Yu, Hui Xiong, 
Bin Guo, Liang Wang, Yuan YaoAuthors Info & Claims
Wen Gao, Zhiwen Yu, Hui Xiong, Bin Guo, Liang Wang, Yuan YaoAuthors Info & ClaimsPages 742 - 751
Abstract
In autonomous robotic systems, the parallel processing of multiple tasks often competes for limited resources, affecting system performance and the robot’s responsiveness to environmental changes. Traditional computational task scheduling methods often overlook the dynamic nature of task priorities in autonomous robotic systems, where task importance can shift based on interactions with the external environment. Therefore, there’s a crucial need for a mechanism capable of adaptively adjusting task scheduling in response to environmental changes, ensuring timely access to resources for critical tasks. To address this challenge, this study presents Priorest, a neural network model that incorporates multimodal data processing and multitask learning. Priorest integrates sensor data with logs monitoring computational device performance to predict events influencing task priority, enabling task adjustments while preserving essential resource allocations. When deployed in autonomous robotic systems, Priorest’s event-prediction-based adjustment strategy reduced critical task completion times by 18.7%, which demonstrates the effectiveness of Priorest in enhancing parallel task scheduling.
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Index Terms
- Parallel Task Scheduling in Autonomous Robotic Systems: An Event-Driven Multimodal Prediction Approach
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Published In
August 2024
1279 pages
ISBN:9798400717932
DOI:10.1145/3673038
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 12 August 2024
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- National Natural Science Foundation of China
- Natural Science Foundation of Shaanxi Province for Distinguished Young Scholars
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ICPP '24
ICPP '24: the 53rd International Conference on Parallel Processing
August 12 - 15, 2024
Gotland, Sweden
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Overall Acceptance Rate 91 of 313 submissions, 29%
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