Autonomous navigation / Implemented capabilities

From point A to point B.
With intelligence in between.

Implemented autonomous navigation and proprietary planning algorithms for drones, demonstrated in recorded simulation scenarios. Watch demos →

Implemented · Simulation-tested configurations

Route & trajectory planning

Plan a route toward the destination, choose locally feasible trajectories, and replan as observations change. Evaluate mission completion, obstacle clearance, path efficiency, and response latency.

Supported functionality

GPS-denied visual-inertial navigation

Navigate without satellite positioning using camera-and-IMU motion estimates, with depth-camera obstacle sensing and local mapping. Current support covers local, body-relative navigation with an explicit launch reference. Loss of qualified localization withdraws dependent navigation commands.

Development goal

Offline geospatial maps

Explore offline terrain and geospatial data for route context, terrain awareness, and operating boundaries. Maps need defined coverage, freshness, and handling of unmapped obstacles.

Design goal

Authorized operating areas

Design routes around operator-supplied operating boundaries and geofences. Maps and geofences support those constraints; operators obtain applicable flight authorizations and maintain current restrictions.

Supported functionality

Dynamic-object collision avoidance

Track moving and approaching obstacles, predict collision risks from relative motion, and adjust the planned trajectory to avoid them. Watch the demo → Fast-moving hazards require sufficient sensing range and maneuver time; bullet, interceptor, or hypersonic-missile evasion is not a demonstrated capability.

Research

Challenging flight conditions

Investigate low-visibility perception and wind disturbances. Navigation decisions must account for sensor confidence and the aircraft’s flight envelope, with fallback behavior evaluated for the operating context.

Where we are today

EI2 is a defense technology company developing proprietary AI vision and autonomy for unmanned aircraft. Current engineering includes PX4, ROS 2, simulation-based autonomy development, and computer vision model experiments. Supported autonomy capabilities include GPS-denied camera-and-IMU navigation and dynamic-object collision avoidance. Advanced multi-sensor localization fusion and robust operation in degraded environments remain development objectives. Model and capability maturity vary; production readiness is not established across all use cases.

Define a focused navigation evaluation

Bring a mission, platform, sensors, available footage, onboard compute, and operating constraints. Together we define the software integration interfaces and evidence needed; your team handles aircraft integration.

Book a demo

Your details will be saved with your inquiry. Please don't include confidential footage or sensitive information.