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[2602.13166] Optimal Take-off under Fuzzy Clearances
Summary
This paper discusses a hybrid obstacle avoidance system for unmanned aircraft that combines optimal control with fuzzy logic to improve decision-making under uncertainty.
Why It Matters
The integration of fuzzy logic with optimal control addresses critical challenges in aviation safety, particularly in managing uncertainties during take-off. This research could enhance the reliability of unmanned aircraft systems, making them safer and more efficient in real-world applications.
Key Takeaways
- Introduces a hybrid architecture for obstacle avoidance in unmanned aircraft.
- Utilizes fuzzy logic to adaptively handle constraints based on regulatory guidelines.
- Demonstrates feasibility for near real-time applications with optimal trajectory generation.
- Identifies software compatibility issues that affect constraint enforcement.
- Future work aims to optimize fuzzy functions and extend the system's capabilities.
Computer Science > Artificial Intelligence arXiv:2602.13166 (cs) [Submitted on 13 Feb 2026] Title:Optimal Take-off under Fuzzy Clearances Authors:Hugo Henry, Arthur Tsai, Kelly Cohen View a PDF of the paper titled Optimal Take-off under Fuzzy Clearances, by Hugo Henry and Arthur Tsai and Kelly Cohen View PDF HTML (experimental) Abstract:This paper presents a hybrid obstacle avoidance architecture that integrates Optimal Control under clearance with a Fuzzy Rule Based System (FRBS) to enable adaptive constraint handling for unmanned aircraft. Motivated by the limitations of classical optimal control under uncertainty and the need for interpretable decision making in safety critical aviation systems, we design a three stage Takagi Sugeno Kang fuzzy layer that modulates constraint radii, urgency levels, and activation decisions based on regulatory separation minima and airworthiness guidelines from FAA and EASA. These fuzzy-derived clearances are then incorporated as soft constraints into an optimal control problem solved using the FALCON toolbox and IPOPT. The framework aims to reduce unnecessary recomputations by selectively activating obstacle avoidance updates while maintaining compliance with aviation procedures. A proof of concept implementation using a simplified aircraft model demonstrates that the approach can generate optimal trajectories with computation times of 2,3 seconds per iteration in a single threaded MATLAB environment, suggesting feasibility for near real...