Reliable Robotics Completes Detect and Avoid Testing for the FAA

Why It Matters

The data validates DAA technology for UAS operations in busy airport environments, accelerating regulatory approval and enabling broader commercial drone deployment. It also informs industry standards that will underpin safe integration of autonomous aircraft into the national airspace.

Key Takeaways

• FAA contract completed, providing real‑world DAA flight data
• ACAS X algorithm behavior differs between en‑route and terminal airspace
• Tests conducted at Hollister Municipal Airport with scripted encounters
• Data shared with RTCA SC‑228 and SC‑147 for standards development
• Partnerships include Sagetech, Collins, uAvionix, and Virginia Tech

Pulse Analysis

Detect and Avoid (DAA) technology is the linchpin for integrating large uncrewed aircraft systems (UAS) into crowded airspace. While traditional manned aircraft rely on TCAS II, the newer ACAS X suite offers more flexible, software‑driven collision avoidance, essential for autonomous platforms. Reliable Robotics leveraged this algorithm in a series of flight‑test encounters that mimicked real‑world traffic patterns, capturing how the system reacts when transitioning from high‑altitude en‑route phases to low‑altitude terminal operations. These nuanced performance metrics are difficult to reproduce in simulations, making the FAA‑contracted data uniquely valuable.

The testing campaign highlighted a critical distinction: ACAS X outputs shift noticeably between en‑route and terminal environments, reflecting the different maneuvering expectations of IFR and VFR pilots. By scripting specific encounter scenarios at Hollister Municipal Airport, Reliable demonstrated that its DAA system can issue timely alerts and maneuver guidance for remote pilots during takeoff, landing, and pattern flight. Collaboration with hardware partners—Sagetech’s prototype ACAS X radios, Collins’ ground‑based radar, and uAvionix’s C‑Band communication suite—ensured a comprehensive sensor suite, while Virginia Tech’s Mid‑Atlantic Aviation Partnership provided academic rigor and data analysis.

The broader industry impact is significant. By feeding the collected data to RTCA Special Committees 228 and 147, Reliable is directly shaping the next generation of DAA standards that the FAA will adopt. As regulatory frameworks solidify, commercial operators can anticipate clearer pathways to certify autonomous cargo drones and passenger‑capacity UAS, unlocking new revenue streams in logistics and air‑mobility. The partnership model also signals a collaborative ecosystem where technology providers, academia, and regulators co‑develop safety solutions, accelerating the timeline for scalable, certifiable autonomy in aviation.