Rapid Prototyping of Dynamic Robots

Rapid Prototyping of Dynamic Robots - Nick Morozovsky - Oct 4, 2016 -
Engineering Tomorrow’s Robots and Drones Today
Rapid Prototyping of Dynamic Robots
Nick Morozovsky, PhD

Co-Founder, Accel Robotics

@DrNickMo

October 4, 2016
1

Outline
• Introduction
• Tools for Rapid Prototyping
• Sensors
• Simulation Case Study
• Conclusions
2

Introduction
Robotics Challenges
MobilityPerception
Manipulation
“Go get me a beer
from the fridge”
Stairs
Opening 
a door
Sand, eggs, clothes
Unstructured 
terrain
Where to 
grasp object
Localization 
Mapping
3

Introduction
Robotics Landscape
Cost ($)
Functionality
Toys
Service
102 106103 104
Cleaners
101
Medical
Manufacturing
Military
Consumer Commercial Industrial
4
105

Tools for Rapid Prototyping
Paradigm Shift
• Digitally fabricated custom mechanical structure
• Ecosystem of off-the-shelf single board computers
and sensors
• Powerful open-source software available
• Trade-off between optimizing for rapid prototyping
and production
• Trend: 3D printing for production, niche/custom
parts that aren’t cost-effective to tool up
5
racewaredirect.co

Tools for Rapid Prototyping
3D Printer vs. Laser Cutter
6
3D Printer Laser Cutter
Speed Slow Fast
Dimensions 3D 2D
Material Selection Limited, but growing Diverse
Limitations Anisotropic, Surface Finish Flat
Design Tips
Print Orientation Selection,
Captive Nuts
Tab & Slot, T-Slot, Living
Hinge, Kerf
Cost $300+ $3,000+

Sensors
• Cost reduction driven by smartphone development
• Accelerometers, gyroscopes, magnetometers, light sensors,
cameras, GPS, WiFi, Bluetooth, etc.
• Be smarter than the sensor
• Filters: low-pass, high-pass, moving average, median
• Calibration: use estimator (Kalman ﬁlter) for bias and drift
• Redundancy: decrease noise, add robustness
7

Sensors
Complementary Filter
• MEMS accelerometer can measure absolute
angle of gravity vector
• Susceptible to high frequency noise and
body accelerations
• MEMS gyroscope can be integrated to
measure incremental angle
• Susceptible to thermal drift and  
integration error
• Use complementary ﬁlter to combine
accelerometer and gyroscope measurements
atan2
1/s
Low
Pass
High
Pass
s
Accelerometer
Gyroscope
Encoder
+
+
++
++ ˙
˙✓
✓
µGHP =
1/!c
1/!c + h
, µALP =
h
1/!c + h
θ
LC
r
mC
mW x
y
ϕ
8

Sensors
Encoder Velocity Estimation
• Limited by encoder and clock resolution
• Quadrature sub-periods are not equal
• Measure four separate periods
• Average multiple periods when possible (M ≥ 2)
• Bound low speed by time since last edge (M < 1)
• Mount encoder before gearbox for increased resolution
A
ARF
B
AFR
BFR BRF
AR BR BR AF AF BF BF AR
ARR
BFF
M =
2!h CPR
⇡
9

Simulation Case Study
Switchblade UGV
• Tread assemblies can pivot w.r.t. the
central chassis
• Signiﬁcantly changes the  
center of mass
• Different modes of locomotion
• Applications: search & rescue, border
patrol, mine exploration, toy/entertainment
• Patent pending
10

Backlash Modeling
• Backlash can be modeled as a
switched system
• Derive coupled and uncoupled
dynamics and conditions for
coupling and uncoupling
if coupledOld == 0 % uncoupled
% if gap is >= backlash and relative speed is same sign as gap, couple
if (abs(gap) >= delta) && (sign(relVelocity) == sign(gap)) % +'ve or -'ve engagement
coupled = sign(gap);
resetVel = ( J2*x(6) + Jg*x(7) )/(Jg+J2);
resetPos = [x(1); x(3)+sign(gap)*delta; x(3); x(4); x(5); resetVel; resetVel; x(8)];
else % stay uncoupled
coupled = 0;
end
else % coupled
% if relative acceleration is opposite sign as gap, uncouple
if sign(relativeAcceleration) == -sign(gap) % accelerating to open gap, uncouple
coupled = 0;
else % stay coupled
coupled = coupledOld;
end
end
2δ
Motor Load
γ α
11

θ
α
ϕ
x
Results: Simulation vs. Experiment
0 1 2 3 4 5 6
−500
0
500
t(s)
φ(deg.)
0 1 2 3 4 5 6
−50
0
50
100
t(s)
α(deg.)
Simulation
Experimental
0 1 2 3 4 5 6
−50
0
50
t(s)
θ(deg.)
)
)
d
=
,
r
)
d
s
s
,
d
,
)
at unity magnitude.
An important ﬁnding is that simply running the con-
troller from certain statically stable positions (e.g. the tread
assembly horizontal ↵ = 90 and the chassis just past
vertical ✓ = 15 ) is sufﬁcient to upright and stabilize
the robot, see Fig. 4. Given these initial conditions, the
center of mass is near the end of the treads by the chassis
(Fig. 4a), and the control law derived from LQR will drive
the treads backwards (Fig. 4b), which will cause the robot
to tip forwards leaving only the tread sprocket in contact
with the ground (Fig. 4c). Simultaneously, the V-angle is
reduced by actuation of the motors between the chassis and
tread assemblies (Fig. 4d) and the treads are driven until the
sprocket is back in the original position (Fig. 4e).
(a) (b) (c)
(d) (e)
Fig. 4: Maneuver for uprighting into V-balance mode with
LQR control with center of mass indicated.
12

Rapid Prototyping of Dynamic Robots - Nick Morozovsky - Oct 4, 2016 - 13

Conclusions
Rules of Robotics
1. Never disassemble a working robot.
2. If it works the ﬁrst time, you’re testing it wrong.
3. When in doubt, lubricate.
4. Never underestimate the estimation problem.
5. If specs for a part are listed differently in two
places, they’re both wrong.
6. Glue, tape, and zip-ties are not engineering
solutions (though they might work in a pinch).
7. Do not leave lithium polymer batteries charging
unattended.
8. Always have a complete CAD model, including
screws and fasteners, before constructing your
robot.
9. Avoid using slip rings if at all possible.
10.Clamping collars are always better than set
screws. If you have to use set screws (e.g. for
cost reasons), use a driving ﬂat and an
appropriate thread-locking agent.
11.Always check polarity before plugging a
component into a power source.
14

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