The aim of “BLUESLEMON” project is to develop a low-cost automatic system for monitoring landslide surface displacement using drones and BT beacons. The proposed drone architecture is developed to go beyond the current state-of-the-art techniques and is characterized by autonomous navigation capabilities. The UAV platform is equipped with obstacle-detection sensors and collision-avoidance algorithms, allowing the smart UAS to be easily employed for autonomous navigation, even in case of diverse environments or applications (search-and-rescue operations in alpine environments or automatic surveillance in urban areas).

1. BLUESLEMON project: autonomous UAS for landslides
monitoring in alpine environment
Alex Bojeri
MAVTech Srl
NOI Techpark Südtirol/Alto Adige, Bolzano, 13th November 2020 – SFScon2020
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2. BLUESLEMON Project
Overview
Low-cost automatic system for monitoring
landslide surface displacement through the
integration of Bluetooth (BT) Beacons
localization and Unmanned Aerial Systems
(UAS) also named Remotely Piloted Aircraft
System (RPAS) technologies.
Technologies:
• Wireless Power Transfers, BT Beacons
• Radio-frequency identification (RFID)
• Unmanned Aerial Systems (UAS)
Objectives
The project aims to…
I. …build upon innovative technologies
at the highest level of the State of the
Art
II. …integrate these technologies to
create an innovative natural hazard
monitoring system for diverse
environmental conditions (e.g. alpine
environment)
III. …enable field information collection,
in order to support provincial services
by providing accurate field
measurements.
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3. BLUESLEMON
Partners
• MAVTech S.r.l.
MAVTech s.r.l. was founded as a spin-off company of
Politecnico di Torino (2005-2014), currently also
located in Bolzano/Bozen as a Technology Company
of NOI Techpark Südtirol/Alto Adige.
The main focus of MAVTech s.r.l. is the prototyping
and production of Remotely Piloted Aircraft System
(RPAS) with competitive performance and costs
(including customer support and end-user training)
resulting from the development of projects based on
the transfer of new aerospace technologies from
the research field to the operational and industrial
sector.
MAVTech S.r.l. is the technology partner of the
project.
• FOS S.p.a.
• Eurac Research
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4. Project Peculiarities
Practical Aspects
• Study and design of a system capable of
detect and avoid obstacles through reliable
mechanisms
• Respect project requirements (three-
dimensional collision avoidance, outdoor
application, budget, timing,...)
• Implement, simulate and optimize a collision
avoidance system
Implementation Steps
• Specific application to the Alpine
environment and landslide monitoring
• Validate a system implementable and
testable in the real world
• Application to automatic or semi-automatic
flight missions
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SIMULATE
OPTIMIZE
DESIGN

5. Experimental Setup
Guide, Navigation and Control (GNC)
System
1. Autopilot: Pixhawk 4 – PX4 firmware (open-
source)
2. Altimeter: Lightware laser model SF11/C
3. Radio controller and RC receiver: Hex
Herelink
4. GPS RTK: Drotek Sirius RTK GNSS rover and
base
5. Depth camera: Intel RealSense Depth
Camera model D435i
6. Companion Computer: Nvidia Jetson NANO
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6. Simulation Environments
Scenarios
Different scenarios for different flight missions
1. No Obstacle Environment
2. Corvara (BZ) Landslide Environment
3. Rough Environment
Verification of the variation of the 3DVFH+
algorithm performances as a function of the
variation of different parameters. Optimization
of simulation parameters through free
simulation software like ROS and Gazebo.
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7. Scenario 1 Simulations
Description
• Terrain altitude variations: waypoints are set
near the hill.
• The collision avoidance algorithm
continuously recalculates the optimal path to
reach the target, respecting the flight and
collision avoidance parameters set.
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Results
• The vehicle is able to detect risky terrain
variations as obstacles.
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8. Scenario 2 Simulations
Description
• Real standard monitoring flight mission
simulation.
• Drone positioning on the QGC map at
Corvara landslide coordinates.
• Waypoints altitude: 5 m.
• Mission duration: ~10 minutes.
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Results
• The UAV moves around avoiding obstacles,
until it reaches all targeted waypoints
• The collision avoidance system has not been
implemented for the landing phase.

9. Scenario 3 Simulations
Description
• Mission planned in order to test collision
avoidance algorithm maximum
performances.
• Maintaining previous flight and avoidance
parameters, the mission cannot be
completed.
• The mission has been replanned by moving
the last waypoints slightly further away from
the rocks.
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Results
• The UAV reaches all waypoints making
trajectories at minimum distance radius from
the desired path, according to parameters set.
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10. Parameters Optimization
Standard Parameters
• Set and maintained constant for all the
simulations (weights, dimensions, UAV
endurance, octomap resolution, 2D Binary
Polar Histogram ranges,…)
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Simulation Parameters
• Initially set with standard values considered
until this phase.
• Hitboxes define objects physical properties
of weight and size.
• The variation of simulation parameters
affects the collision avoidance system
functioning and aims to optimize the
algorithm for the specific study application.
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11. Conclusions
Goals Achieved
• The detection and avoidance method is able
to maintain a minimum safety distance to
obstacles, continuously redefining the
optimal path when obstacles are detected
near the UAV.
• The system does not continuously interact
with the desired route when no obstacle
have been detected
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Optimization Objectives
• High number of simulations has been carried
out to verify that the random behavior of the
optimal route calculation could not lead to
negative factors or critical results
• Optimization of flight and simulation
parameters, according to different scenarios,
increases system performances
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12. Future Developments
Further Simulations
• Expand the algorithm operation to all other
mission phases
• Evaluate the use of several sensors or hybrid
solutions, to ensure a higher vehicle FOV for
the obstacle detection
• Develop Hardware In The Loop (HITL)
simulations
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Real Tests
• Improve reliability, accuracy and sensibility
of the system
• Perform real world flight missions, testing the
complete real system developed.
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13. THANK YOU FOR YOUR ATTENTION!
“Bluetooth ” icon by il Capitano, “Landslide” icon by Laymik, UA, “Drone” icon by Rflor from thenounproject.com with changes.
www.mavtech.eu
mavtech@mavtech.eu
+39 011 5808482
Via Ipazia 2, 39100 Bolzano – Italy
@mavtechsrl @MAVTech S.r.l. @mavtech_srl
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