Researchers have developed a new method for ensuring separation between small Unmanned Aircraft Systems (sUAS) even when GPS signals are degraded or spoofed. This approach uses Multi-Agent Reinforcement Learning (MARL) to create a robust counter-policy for agents, treating corrupted position broadcasts as a zero-sum game against an adversary. The method derives a closed-form expression for adversarial perturbations, allowing for efficient computation and demonstrating near-zero collision rates in simulations under significant GPS corruption. AI
IMPACT This research could enhance the safety and reliability of drone operations in environments with compromised GPS signals.
RANK_REASON This is a research paper published on arXiv detailing a novel method for multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
- Alex Zongo
- arXiv
- Global Positioning System
- Kullback--Leibler divergence
- Multi-agent reinforcement learning
- unmanned aerial vehicle
- Unmanned Aircraft Systems (UAS): Commercial Outlook for a New Industry
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