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2018 IJCAI IJCAI 2018

Data-driven Onboard Scheduling for an Autonomous Observation Satellite

Abstract

Observation requests for autonomous observation satellites are dynamically generated. Considering the limited computing resources, a data-driven onboard scheduling method combining AI techniques and polynomial-time heuristics is proposed in this work. To construct observation schedules, a framework with offline learning and onboard scheduling is adopted. A neural network is trained offline in ground stations to assign the scheduling priority to observation requests in the onboard scheduling, based on the optimized historical schedules obtained by genetic algorithms which are computationally demanding to run onboard. The computational simulations show that the performance of the scheduling heuristic is enhanced using the data-driven framework.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
📈 Trend Setter — Autonomous Vehicles
🧭 Keyword Pioneer — efficient computing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🐣 Hot Topic Early Bird — neural network training

Authors

CHAO LI , Yingwu Chen , Patrick De Causmaecker

Topics

Artificial Intelligence > Core AI > Autonomous Vehicles Machine Learning > Optimization & Theory > Neural Network Optimization Machine Learning > Application Areas > Efficient Computing Machine Learning > Learning Types > Deep Learning Machine Learning > Learning Types > Optimization

Keywords

neural network training trajectory prediction efficient computing autonomous vehicle genetic algorithm scheduling algorithm data-driven method neural network onboard scheduling satellite scheduling polynomial-time heuristic
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