Bavovna AI navigation kit

  • Sensor fusion Al algorithm
  • Low SWAP hybrid INS solution
  • End Point Positioning Error < 0.5%
  • Composite radio shielding
  • Mission planner for GNSS-denied
  • No maps required
Bavovna AI navigation kit

Al-Driven Hybrid Navigation for UAV in GPS-denied

Fixed Wing

Fixed Wing

Multi Rotor

Multi Rotor

VTOL

VTOL

Technical specifications

Weight:

The entire system, including IMU and Al-powered flight control, weighs only 800g, making it lightweight and easy to install on various types of UV platforms

Power:

Standard +5V power supply
Max current consumption: 10A, peak of 12A, 50W, Voltage/rated input current: 4.1-5.7 V / 2.5 A,
Output/input power: 14 W, USB port voltage/rated input current: 4-5.7 V / 250 mA,
Servo rail input voltage: 3.3 V / 5 V

Standard Sensors:

Accelerometer, Gyroscope, Compass, Barometric Pressure, Airflow

Connectivity:

Bavovna seamlessly connects to power, CAN, PWM, RF comm, GPS, optical flow, and other sensors, providing flexibility and compatibility across UV platforms

Mission Planner:

The Bavovna Mission Planner displays Bavovna as an optional Al-powered navigation system to allow mission execution through GPS-denied / EW-threatened areas while offering an advanced API and Ul based on the Ardupilot open-source platform

Flight Controller:

Bavovna uses PX Cube as the primary flight controller component, ensuring reliable and accurate control of UVs

EMI-protected Case:

Bavovna’s EMI-protected case has undergone EMF resistance tests, ensuring reliability and durability in a variety of conditions

End Point Positioning Error (EPPE):

The range of EPPE is minimal with simple trajectories, without additional maneuvers. On a sophisticated trajectory, Bavovna maintains an EPPE < 0.5% at a range of 30 km.

SIGINT RF Module:

the system Bavovna can be augmented with SIGINT RF module, enabling the reconnaissance of EM threats to ID and bypass EW and EM obstacles

No maps required:

The Bavovna system does not rely on power-hungry computer vision or unreliable maps of the flight environment

Customers Onboarding Path

UV Platform Valuation

STEP 01

UV Platform Valuation

Order of Two DevKits of Bavovna Navigation

STEP 02

Order of Two DevKits of Bavovna Navigation

Integration to UV

STEP 03

Integration to UV

100 hours of real flight logs data gathering

STEP 04

100 hours of real flight logs data gathering

Sensor Fusion AI Model Training and finetuning

STEP 05

Sensor Fusion AI Model Training and finetuning

Autonomous UV flight tests

STEP 06

Autonomous UV flight tests

Order Delivery

Comparison to other non-GPS Systems

Type
Range
Accuracy
Nature of errors

Inertial Navigation Systems (INS)

Short
80%

Drift, biases, cumulative error of integration

Radio (eg: VOR, LORAN, TACAN)

Short
90%

Signal interference and propagation delays

Landmark (optical)

Short
95%

Environmental conditions and landmark changes

Magnetic

Short
95%

Distortion by ferromagnetic, electrical currents and geological formations

Bavovna Al-enhanced Inertial

Long
>98%

Hybrid systems can be affected by the combined errors of the systems they integrate, especially if one system’s errors are not adequately compensated by others.
Using continuous ML decreases the error rate.

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