Implementation of Adaptive Digital Beamforming using Cordic
myPresentation
1. Next generation radiation
mapping using UAS assisted
dynamic monitoring networks
Supervisor: Dr. Thomas B. Scott
Student: Angelos Plastropoulos
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Supported by: and
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Project Aims
Introduction
The problem: To get the best resolution for the
radiation mapping, the UAV needs to fly low, slow
and with precision. It needs to fly close to buildings
or generally surfaces of interest. Neither is feasible
with a helicopter or current UAS technology.
In addition, nuclear sites have a very complex
geometrical shape.
The solution: Explore the development of a UAV,
which will be equipped with devices and will execute
algorithms that will facilitate the radiation mapping
procedure and yield high quality measurements.
5. Ultrasonic and infra-red Sensors
Sensors
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Devantech SRF02
Sharp
GP2Y0A02YK0F
Low cost (10£), low power, low weight
Range 0.3-6m
Supports I2C & Serial interface
Resolution 3-4cm
Perpendicular
measurements
Side lobes
Wind noise
Low cost (10£), low power, low weight
Range 0.2-1.5m
Low resolution for distances > 0.9m
Supports only
analog interface
Depends on the
reflectivity of the
surface
6. Optical flow and laser sensor
Sensors
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PX4Flow Smart Camera LIDAR-Lite laser rangefinder
Medium size, medium weight
Range 0.3-40m
Resolution 1cm
Supports I2C and PWM interface
Measurement speed 100Hz
Good performance on non-perpendicular
surfaces.
No interference from wind
Versatile to custom projects
Cost 70£Cost 100£
Small size, medium weight
Measures distance from ground and ground
speed
Useful in GPS denied environments allowing
long-term dead reckoning navigation
Supports I2C, serial and micro USB interface
High rate of optical flow estimation (400Hz
over an area of 64x64 px)
8. UAV’s advanced tech components
UAV configuration
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Flight controller On-board computer Sensors
32-bit ARM
cortex M4 processor
256KB RAM + 2MB flash
3D Acc/Gyro/MAG/Baro
Abundant connectivity
options (UART, I2C, CAN,
SPI, ADC…)
MicroSD card slot
for data logs
Card-size, lightweight
1.7GHz Quad-core
processor
2GB ram
SD & eMMC storage
3xUSB 2.0
100Mbps LAN
32-bit ARM cortex M4
processor
752x480px
CMOS camera
3D gyro
Max range 40m
Accuracy +/- 2.5cm
Acquisition time < 0.02s
Rep rate 100Hz
9. UAV simulation
Simulation
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The simulation was developed using ROS and Gazebo. For the UAV, the Hector quad-rotor
ROS stack was utilised.
Simulation configuration Obstacle avoidance scenario
11. Radiation mapping Application
Radiation mapping
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App screenshots
Schematic representation of the
radiation mapping implementation
Using this app the users can have real-time
mapping of the radiation contamination
levels on their iPhones
12. iOS-ROS control app
Human robot interaction
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App screenshots
Topology of the
tele-operation application
Using this app the users can control any
ROS-enabled UAV. The app also shows
streaming video of the on-board cam.
13. Real UAV experiments
Real UAV experiments
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Basic approach: A custom laser scanner device is mounted on the UAV. The device
communicates via Bluetooth with an iPhone app. The operator gets immediate
feedback about obstacles in 0o, 90o and -90o direction.
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Real UAV experiments
Advanced approach: The UAV will get inputs from the px4flow and the custom laser
scanner device. The distance from the ground as well as the distance in 0o, 90o and
-90o directions will feed the obstacle avoidance algorithm which will be executed in
the on-board computer.
Real UAV experiments
15. All roads lead to Rome
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During this project a set of useful
tools has been developed. This
aims to improve existing UAVs
used in radiation mapping.
Different approaches aim at the
same target. The successful
combination of all will enable us
to have a radiation mapping UAV
closer to the one that the initial
requirements have set.
Conclusion
