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An autonomous, multi-agent, IoT-empowered space logistics system for mission-critical inventory packing
K L Yung1, Y P Tsang1, C H Wu2
1Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.
ISA Transactions
|November 20, 2022
Summary
This study introduces autonomous agents for space logistics, optimizing replenishment and payload utilization. These agents enhance decision-making for efficient space mission operations.
Area of Science:
- Aerospace Engineering
- Operations Research
- Artificial Intelligence
Background:
- Space logistics operations are critical for aerospace missions, involving high costs and long replenishment cycles.
- Efficient spacecraft payload utilization is paramount due to operational constraints and mission-driven requirements.
Purpose of the Study:
- To extend inventory packing problem theory for autonomous agents in space logistics.
- To enhance decision support systems for replenishment, payload optimization, and quality inspection.
Main Methods:
- Development of autonomous agents using interval type-2 fuzzy logic for demand forecasting.
- Application of the differential evolution algorithm for space chunk loading optimization.
- Integration of 3D object scanning for quality inspection and feedback.
Main Results:
- Improved accuracy in chaotic time-series demand forecasting for re-order quantities.
- Maximized spacecraft payload and capacity utilization, especially for cylindrical items.
- Optimized chunk-loading instructions derived from real-time item data and agent feedback.
Conclusions:
- Autonomous agent interactions within a decision support system significantly improve space logistics operations.
- The developed system supports mission-critical decisions, leading to enhanced operational excellence in space missions.
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