Bolivian citizen, he has a BEng in electronics engineering and has also
successfully completed more than 15 MOOCs in robotics, computer vision
and artificial intelligence. In his spare time, he likes to experiment
with robotics, artificial intelligence, embedded systems and IoT.
March, 2020, NXP HoverGames Challenge 1: Fight Fire with
project "HoverGames: Air Strategist Companion" received the "PX4 Award"
to the best use of code and services from the PX4 platform. Award
issued by Auterion.
October, 2012, Renesas RL78 Green Energy Challenge: His
Quality Mapper" received one of five honorary mentions.
May 2012, DesignSpark chipKIT Challenge: His project "Home
Gateway" was awarded second place.
Station Uses Arduino” (Published by
Magazine #338, September 2018)
“GPS Guides Robotic
Car” (Published by Circuit Cellar
“Build a 4-DOF Robotic
Arm - Using MATLAB and Arduino”
Circuit Cellar Magazine #353, December 2019 & #355, February
“Intro to Ardupilot and
PX4” (Published by Circuit Cellar
#357, April 2020 & #358, May 2020)
Drone Applications: Autonomous Flying
(Published by Circuit Cellar Magazine #362 September 2020)
“V5 Plus Drone Flight
Controller: Hands-On Evaluation”
Circuit Cellar Magazine #363 October 2020)
The Real-Time Physical
Distancing Monitor is a system for the
of monitoring and evaluating physical distancing (known also as social
distancing) policy compliance in open public areas. The system
comprises one or more Detection Nodes and a Cloud Server. Each
Detection Node comprises an OAK-D camera and an embedded computer to
detect pedestrians and compute real distances between them. These data
are then uploaded to a Cloud Server and stored in a database for
further analysis and visualization. The Cloud Server presents a home
page in which a map of the city can be seen with markers in all places
where there is a Detection Node installed. System users can access the
home page and click any marker to browse to the corresponding node's
monitoring page, in which graphical data about physical distancing in
the current location is seen.
These graphical data
comprise a timeline plot of the total
pedestrians detected by the system, the total number of physical
distancing violations among the detected pedestrians and the violations
as a percentage of all possible one-to-one physical interactions
between them. The system is meant to be used by authorities in charge
of enforcing physical distancing policies in order to evaluate, correct
and re-issue better policies. It is meant to be used as well by regular
citizens to access real-time data about how crowded is a given public
spot. This gives them insight about the risk degree of being infected
by the virus in that place, due to the degree of physical distancing
violations. Because the monitoring page can show historical data as
well, it is possible to evaluate over-the-time the place's behavior
regarding physical distancing. For instance, to determine which days
and which hours of the day the physical distancing violation index is
higher, with consequently higher risk degree of infection. With this
information at hand, the authorities can devise alternatives to reduce
crowds in peak hours, or the citizens can voluntarily avoid certain
places and/or time of the day with higher infection risk.
To protect the privacy of
the individuals, the system blurs
detected pedestrians in the image and a low-resolution copy of it is
sent to the Cloud Server for visualization purposes. No other images or
data regarding the individuals detected in the image are stored
permanently in the Detection Nodes or in the Cloud Server.
Cloud Server Main Page
Individual Node Monitoring Page
The OAK-D Device
will be given to phase one winners to help them complete
their phase two projects. The OAK-D is a variant of the OpenCV AI Kit
(OAK) capable of Spatial AI.
What is Spatial AI?
capability for AI to be applied to the physical world – to tell you
what an object is and where it is in 3D space – in real time.
does this by running object detection off of its integrated 12MP RGB
camera and combining the results with its integrated stereo-depth
engine. You can run a variety of deep learning models support by
OpenVINO and OAK-D automatically augments them with spatial data from
the integrated stereo depth engine.
We were notified about being one of the finalist teams of