Mobio: augmenting everyday objects into moving IoT devices
using 3d printed attachments generated by you demonstration.
Hi, I am Abul Al Arabi from HCIED Lab, Texas A&M University.
And I welcome you all to this video presentation with Jiahao
Li and Xiang from UCLA HCI research and Jeeeunfrom HCIED lab.
Nowadays, we observe plenty of robots working continuously in
industries and manufacturing areas.
In our household, we observe IoT devices devices such as smart thermostats.
However, mobile agents are found in a limited scope of vacuum cleaning
or entertainment.
Although home with smart agents can do much more, such as assisting
in cooking, serving food as well as accessibility and elderly care.
Mobiot utilizes personal fabrication to augment everyday objects with
3d printable mobile platforms.
Most of the legacy objects in our everyday life are passive in nature and we cannot
replace them with smart agents overnight.
One possible solution could be retrofitting them with
mobility or actuation mechanism.
But just to create a self-shaking spice jar, a user will need to
purchase a multi DOF robotic arm and equip it with kinematics programming.
Besides of the shelf platforms are often expensive.
They have fixed dimensions and they are designed for domain specific individuals.
As a result, they can not adapt to a variety of everyday objects and
are not accessible by end-users.
Adding mobility to these passive objects is a convoluted process for the
end-users and poses multiple challenges.
First, the user will need to capture the design requirements, such as the geometry
information of the target object, as well as the types of required motions.
Then the user needs to decompose this information to design functional
mechanisms, determine the required electronics and program them
with motion planning information.
Moreover, different individuals have different need for such mobility.
And they may need to reuse, repurpose or reauthor them to fit
their dynamic everyday life settings.
Mobiot automatically captures the design requirements from user's demonstration.
Then it generates 3D printable mechanisms, creates a list of electronics and
automatically generates the code to augment every object with mobility.
Mobiot uses a cell phone both as a designing and authoring tool.
The user first needs to take a photo of the target object.
Then the user performs a demonstration using cell phone
by mimicking the desired motion.
Afterward, the Mobiot editor offers the user to fine tune
motion parameters if required.
The user can also design custom actions and tasks.
The user then gets the 3d printable files, a list of required electronics,
a program that contains the authored motion information as well as an
instruction to assemble the mechanism.
And after fabricating and assembling the platform, the legacy object is
finally transformed into a smart one.
Now let's have a look, how mobile works.
From the input image, Mobiot uses a machine learning technique to
detect the object and fetches a 3D model from a pre-built repository
through a semantic search.
Mobiot then classifies and quantifies the demonstration.
Currently we consider translation, rotation and lifting motion
in different combination.
The decomposed information provides geometry requirements types of
required mechanisms, as well as motion planning information.
Afterward, Mobiot uses a parametric design approach to generate the
mechanisms that fits the target object.
Then it determines the required electronics, such as motors,
Arduino Board, or the batteries.
Mobiot also decomposes the demonstration to generate an Arduino code that
contains the authored motion information.
From the cellphone's IMU data we use Ronin to get the 2d horizontal trajectory.
We use a double integration method to determine the lifting height
and to obtain the rotation angle we exploit the rotation vector data.
The raw trajectory contains noise, and we pass it through a moving
average filter to get a smoother one.
And then we segment the trajectory to get the anchor points.
For the other types of motion processing, please refer to our paper.
Mobiot keeps track of the motion sequence, and their spatial location, which is
finally used to generate the Arduino code.
Mobiot also allows the user to reuse or repurpose an existing mechanism
to fit a different use case scenario.
Such as, reauthoring a trash bin mechanism to adapt a laundry basket.
Using Mobiot, the user can also append a new motion mechanism to an existing one.
Such as a spice jar that initially had only the rotation motion can have
a lift and rotation motion combined.
These mobile agents can ease and automate a variety of our everyday tasks, such
as a mobile trash bin that can go to the kitchen to collect garbage.
A laundry basket to collect dirty clothes, and move to the washer dryer.
A camera stand that can adjust the height
A self-serving salt and spice jar to assist in cooking.
An assistive cooking bucket to mix salt and pepper with beaten egg,
or maybe give an initial wash.
A self-organizing coffee mug to take away the hot coffee while you are working.
An automated plant watering bottle to water the plants while you are away.
For further technical details, limitations and discussions,
please refer to our paper.
Instead of being a consumer waste of the past, we envision today's legacy
legacy objects to be transformed into everyday robots by the end
users in the upcoming future.
Thank you for being with us throughout the presentation.