ADVANCED AUTONOMY

ADVANCED AUTONOMY

Our Auto-Router, Sensor Footprint, RF Line-Of-Sight, and Route & Area Nomination Algorithms can be combined to create a powerful mission analysis, image planning, and collision avoidance application.  The result is the capability to effectively and efficiently plan a mission, avoid terrain, avoid obstacles, and gather information on whether or not the desired EO/IR imagery can be obtained – all in near-real-time.

Auto-Routing

This component finds the most efficient path around obstacles in real-time. Kutta’s auto-router has been proven in Monte-Carlo simulations to achieve routing around a randomized mix of 50 to 100 circles and polygons in under an average time of 0.12 msec on an Intel 1 GHz processor. All flight planning modes work in conjunction with the integrated auto-routing algorithm. Inputs: Circular or polygon (convex or concave) no-fly zones, starting waypoint, goal waypoint. Output: Multi-waypoint path to achieve goal waypoint.

Radio-Frequency Line-of-Sight Analysis (RF LOS)

The Radio-Frequency Line-of-Sight component allows an operator to easily define the Radio-Frequency (RF) characteristics of the transmit and receive power of the Unmanned Aircraft System (UAS). Using a Digital Terrain Elevation Model (DTED), Kutta’s efficient RF LOS algorithm determines the vertices of a polygon, where 100% RF coverage from the Ground Control Station (GCS) to the unmanned vehicle exist. The vertices of the resulting area are provided in modular fashion for display on mapping applications. Inputs: Location of antenna, RF characteristics, environmental and terrain conditions, vehicle performance characteristics, DTED data. Outputs: Vertices of a polygon, STANAG 4586 private messages.

Sensor Footprint

This extremely efficient Sensor Footprint component gathers real-time input from the unmanned vehicle’s position (latitude, longitude, altitude) and the pan, tilt, zoom parameters of an EO/IR payload to determine the sensor’s actual projection onto the ground. Since the algorithm takes into account Digital Terrain Elevation Data (DTED), the sensor footprint can be overlaid in both 2D and 3D environments, offering exceptional situational awareness of where the sensor has pointed, where it is pointing, and where it is capable of pointing. Inputs: Vehicle position and orientation, payload pan, tilt, zoom parameters, DTED, NIIRS rating. Outputs: Vertices of a polygon and polygon fill parameters.

Autonomous Route & Area Survey

The Route Nomination component provides the capability to select a route or designate an area to survey. The algorithm determines an optimum flight plan that keeps the Air Vehicle (AV) within defined airspace restrictions, allows the AV to avoid terrain, and gather the designated imagery based on pertinent mission planning characteristics (i.e. dwell time, image overlap, object detection size, etc). It then outputs a STANAG 4586 compliant flight plan and a series of payload commands that can be sent to the AV for execution. Inputs: Select route or Points of Interest (POI) to survey. Outputs: Air Vehicle (AV) stare points, and loiter points.

Airspace Management

This component allows a user to input Restricted Operating Zones (ROZs), fly zones, and flight corridors defined by airspace management authorities. Kutta’s algorithm merges the zones and flight corridors with the RF LOS coverage to define a Safe Airspace Volume (SAV). The result provides a visual reference to ensure the unmanned vehicle’s flight path remains within the defined SAV. Inputs: Safe Airspace Zones (SAV), Restricted Operating Zones (ROZ), and Radio Frequency Light-of-Sight (RF-LOS) data. Outputs: Safe Airspace Volume with bounding points.

Flight Planning

Allows the operator to insert, delete, add, and change speed and heading of waypoints while viewing a 2D or 3D geo-rectified environment and to execute and monitor the plan. The UGCS also allows an operator to save and reload any previous flight plans stored on board the Ground Control Station (GCS).

MPEG Metadata Solutions

Kutta utilizes a very efficient, light-weight MPEG hardware encoder to digitize MPEG 1, 2, or 4 video and display in real-time. Our recorded MPEG data contains time-stamped and embedded unmanned vehicle parameters such as the vehicle’s position and payload gimbal position for improved video analysis and playback. When used in conjunction with Kutta’s sensor footprint, the video review mode provides the ability to visualize the vehicle’s location and the payload pointing position relative to the real-time video for improved geo-referenced situational awareness.

Battle Damage Assessment (BDA)

This UGCS feature allows a user to easily designate standoff distance and direction of photographic and video reconnaissance. The user simply indicates a place on the ground that needs to be imaged and then drags a cursor in a given direction to define the bearing and standoff distance for the photo/video to be taken. Based upon the Vehicle Specific Module for the payload (i.e. Fixed or Gimbaled), Kutta’s BDA algorithm autonomously determines the waypoints for the air vehicle.