An inertial navigation system (INS) is essential for UAVs to maintain stable flight when GNSS signals are weak or entirely unavailable. Signal degradation occurs frequently due to high-rise buildings, dense forests, electromagnetic interference from nearby structures, or even intentional jamming. Losing a UAV due to navigation failure is costly, but modern AI-driven sensor fusion technology is helping drones navigate autonomously in GPS-compromised or denied environments.
Below are three of the most advanced artificial intelligence inertial navigation systems for UAVs currently available .
Bavovna AI Navigation System
Bavovna provides a highly versatile AI-powered navigation solution for UAVs, compatible with multi-rotor, VTOL, and fixed-wing models. The system boasts an ultra-lightweight, EMI-shielded carbon case that has been extensively field-tested for durability. For additional security, SIGINT RF modules can be incorporated to counteract electronic warfare (EW) and electromagnetic (EM) threats.
The Bavovna H-INS system achieves an impressive 0.85-meter deviation in single-point positioning without GNSS, RTK, or optical navigation, even at altitudes of 500 meters in winds up to 18 m/sec. Unlike traditional INS solutions, Bavovna employs a proprietary AI-driven sensor fusion algorithm. This algorithm, pre-trained on various sensor data inputs—such as multivector aerial speed, airflow, aileron feedback, barometer, compass, gyroscope, and magnetometer—ensures high positional accuracy and environmental awareness.
The Bavovna system is sensor-agnostic, meaning it can be integrated with additional navigation aids, including LiDAR and computer vision. For applications requiring terrain mapping or object detection, Bavovna supports simultaneous localization and mapping (SLAM).
Key Advantages:
- Operates entirely without GPS, RTK, or network signals, allowing for autonomous take-off, Return-To-Home, and landing.
- AI-powered sensor fusion is custom-trained for each UAV and operational scenario.
- Modular, scalable design with low power consumption and EMI protection.
- Industry-leading ultra-low endpoint positioning error (EPPE) of under 0.5% at 30 km range.
George Autopilot by uAvionix
The George autopilot system is a reliable UAV navigation solution built on certified DAL-C hardware and CubePilot architecture. This system utilizes IMU sensors and military-grade geomagnetic sensors to provide precise positioning. For enhanced safety, users can integrate detect-and-avoid (DAA) capabilities using pingRX Pro or the ping200X Mode S Transponder.
At just 80 grams (2.8 oz), the George autopilot is exceptionally lightweight and energy-efficient. It is designed to endure extreme environmental conditions, including power anomalies, lightning, and electromagnetic interference. Certified under DO-160G and MIL-810H standards, it guarantees high resilience and operational reliability.
Key Advantages:
- Certified to aviation and military environmental standards.
- Integrates high-accuracy GPS, C2 radio, and military-grade sensors.
- Compact, plug-and-play design, allowing for easy integration with additional avionics.
Spatial FOG Dual by Advanced Navigation
Advanced Navigation specializes in high-precision inertial navigation, offering cutting-edge Micro-Electro-Mechanical Systems (MEMS) and Fiber Optic Gyroscopes (FOG) for UAVs. FOG technology surpasses MEMS in dynamic conditions, providing higher accuracy, lower noise levels, and reduced recalibration needs.
The Spatial FOG Dual system integrates fiber optic gyroscopes, accelerometers, magnetometers, and a pressure sensor with a dual-antenna RTK GNSS receiver. Its EMCORE TAC-450 fiber optic gyro IMU ensures superior inertial data accuracy, far surpassing conventional MEMS-based systems. The RTK GNSS receiver supports positioning accuracy of up to 8mm (0.3 inches) and timing precision of 20 nanoseconds.
Like Bavovna, Advanced Navigation employs an AI-based sensor fusion algorithm, which the company claims is ten times more accurate than traditional Kalman Filters, enabling prolonged navigation even without GNSS.
Key Advantages:
- Horizontal positioning accuracy of 0.01 m with RTK or post-processing kinematics (PPK).
- Gyroscopes with a bias instability of 0.1°/hr and accelerometers with 15 µg bias instability.
- AI-powered sensor fusion with robust real-time fault tolerance.
4. Honeywell Compact Inertial Navigation System
Honeywell’s Compact INS (CINS) is a lightweight yet powerful navigation solution. Weighing only 115 grams (4 oz), it features tactical-grade inertial sensors, dual-antenna GNSS heading, RTK support, and seamless integration with Pixhawk 2.1. It can also be expanded with third-party navigation aids, such as radar velocity systems or anti-jamming modules, for enhanced reliability in high-EMI environments.
In RTK mode, Honeywell CINS achieves an ultra-low positioning error of 0.03/0.015 meters, with a velocity error as low as 0.02-0.04 m/s. However, unlike Bavovna and Advanced Navigation, Honeywell does not utilize AI for sensor fusion, limiting its ability to autonomously navigate in GNSS-denied environments.
Key Advantages:
- Compact and lightweight design with top-tier hardware for superior accuracy.
- Built-in redundancy and rugged construction for enhanced mission safety.
- Expandable with third-party navigation aids for specialized applications.
Choosing the right INS for a UAV depends on the mission requirements and operational conditions. AI-powered navigation solutions like Bavovna and Spatial FOG Dual offer the highest levels of autonomy and reliability in GNSS-denied environments. Meanwhile, George Autopilot and Honeywell CINS provide robust alternatives with proven industry performance, albeit with less AI-driven adaptability. As sensor fusion technology continues to evolve, UAV navigation will become even more resilient against GNSS disruptions, ensuring more reliable and secure drone operations.