Transcript Effectors and Actuators

Lecture 16:
(20/11/09)
Sensing self-motion
Key points:
Why robots need self-sensing
Sensors for proprioception
in biological systems
in robot systems
Position sensing
Velocity and acceleration sensing
Force sensing
Vision based proprioception
Michael Herrmann
[email protected], phone: 0131 6 517177, Informatics Forum 1.42
Why robots need self-sensing
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For a robot to act successfully in the real world it needs to
be able to perceive the world and its relation to the world.
1. The state of the robot is not entirely up to the robot
itself, but also reflects external events. Thus,
information about the “body” is an important source of
information about the world
2. Another use of proprioceptive information is
stabilization and smoothing of planned movements
against perturbations
In particular, to control its own actions, it needs information
about the position and movement of its body and parts.
Our body contains at least as many sensors for our own
movement as it does for signals from the world.
Proprioception: Detecting our
own movements
• To control our limbs
we need feedback:
Kinesthesia
• Muscle spindles
where: length
how fast: rate of stretch
• Golgi tendon organ
how hard: force
Proprioception: Detecting our
own movements
• To control our limbs
we need feedback
on where they are.
• Muscle spindles
• Golgi tendon organ
• Pressure sensors in
skin
Pacinian corpuscle –
transient pressure response
Proprioception (cont.)
• To detect the motion of our
whole body have vestibular
system based on statocyst
• Statolith (calcium nodule)
affected by gravity (or inertia
during motion) causes
deflection of hair cells that
activate neurons
Describing movement of body
Requires:
• Three translation
components
• Three rotatory
components
• Vestibular System
• Utricle and Saccule
detect linear acceleration.
• Semicircular canals
detect rotary
acceleration in three
orthogonal axes
• Fast vestibular-ocular
reflex for eye stabilisation
Robert J. Peterka (2009) Comparison of human and humanoid robot
control of upright stance. Journal of Physiology – Paris 103, 149–158
Using proprioceptive information
Control
Efference copy
Proprioception
Exteroception
body surface
For a robot:
Need to sense motor/joint positions with e.g.:
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Potentiometer (current measures position)
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Optical encoder (counts axis turning)
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Servo motor (with position feedback)
For a robot:
• Velocity by position change over time or other
direct measurement: Tachometer
• E.g. using principal of dc motor in reverse:
voltage output proportional to rotation speed
• (Why not use input to estimate output…?)
• Acceleration: could use velocity over time, but
more commonly, sense movement or force
created when known mass accelerates, i.e.
similar to statocyst
Accelerometer:
Gyroscope: uses
measures displacement
of weight due to inertia
conservation of angular
momentum
There are many alternative forms of these devices, allowing
high accuracy and miniaturisation (e.g. ceramic piezo gyros)
Inertial Navigation System (INS)
• Three accelerometers for linear axes
• Three gyroscopes for rotational axes (or to
stabilise platform for accelerometers)
• By integrating over time can track exact
spatial position
• Viable in real time with fast computers
• But potential for cumulative error
For a robot:
To sense force: e.g.
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Strain gauge – resistance
change with deformation
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Piezoelectric – charge
created by deformation of
quartz crystal (n.b. this is
transient)
For a robot:
• Various other sensors may be used to measure
the robot’s position and movement, e.g.:
• Tilt sensors
• Compass
• GPS
• May use external measures e.g. camera tracking
of limb or robot position
Some issues for sensors
• What range, resolution and accuracy are
required? How easy to calibrate?
• What speed (i.e. what delay is acceptable)
and what frequency of sampling?
• How many sensors? Positioned where?
• Is information used locally or centrally?
• Does it need to be combined?
Haptic perception – combines muscle & touch sense
Vision as proprioception?
• An important function of vision is direct
control of motor actions
• Test: standing on one leg with eyes closed or
standing up ...
The ‘swinging room’ - Lee and Lishman (1975)
Optical flow
Optical flow:
Heading = focus of expansion
…provided that it can discount flow caused by eye movements
Optical flow:
Flow on retina = forward translation + eye rotation
Flow-fields if looking at x
while moving towards +
Bruce et al (op. cit) Fig 13.6
From optical flow to time to contact
P = distance of
image from
centre of flow
P
X = distance of object from eye
Y = velocity of
P on retina
V = velocity of approach
t = P/Y = X/V
rate of image expansion = time to contact
Lee (1980) suggested visual system can detect t
directly and use to avoid collisions e.g. correct braking.
Using expansion as a cue to avoid
collision is a common principle in animals,
and has been used on robots
• E.g. robot
controller based
on neural
processing in
locust –
Blanchard et. al.
(2000)
Proprioceptive control
Proprioceptive control
Summary
• Have discussed a variety of natural and
artificial sensors for self motion
• Have hardly discussed how the transduced
signal should be processed to use in control
for a task.
– E.g. knowing about muscle and touch
sensors doesn’t explain how to manipulate
objects
Dimensions of robotics
1. Defining goals: Tasks or models
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Reaching goals: programming or learning
Reason or emotions
Evaluation of performance
Energy consumption
Social issues:
Dynamical systems for control
Design principles
1. Biorobotics
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Robots as models of animal behaviour
Proof of (functional) principle
Bio-inspired robotics
Biomorphic engineering
Service robots
Prosthetics
Human-robot interaction
2. Programming vs. Learning
O. Lebeltel, 1996
2. Programming vs. Learning
O. Lebeltel, 1996
Programming and Learning for control
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Action languages (R. Reiter)
Middleware concepts
Machine learning algorithms
Objective functions
Self-organisation of behaviour
Evolution and development
Reinforcement learning
Neural networks
Artificial emotions, consciousness
Methods from
• Comp. Sc.
• Engineering
• Math
• Physics
• Biology
• Psychology
The uncanny valley (Masahiro Mori, 1970)
Repliee Q1 and Geminoid
(H. Ishiguro, U Osaka, 2005, 2007)
3. Emotion vs. Reason
Emotions for robots:
• Interaction with humans
• Internal evaluation
• Centralised supervision
• Kansei (emotion) engineering
Reason for robots: cf. 2. and previous lectures
4. Performance: Competition vs.
Measurement
• DARPA Grand Challenge
• RoboCup: Robot Soccer & Rescue
• Climbing, underwater, fire fighting, ...
• RunBot: Fastest robot on two legs
• Service limits, running costs, monitoring and
support, flexibility, upgradability
5. Energy consumption
• Super-human efficiency in certain tasks
• Inspiration from biology: Passive dynamics in
walking, energy re-use by springs, locking
mechanisms for posture maintenance,
modularity, hibernation
• Development of enduring batteries
• Alternative energies: Solar robots
• Fly-eating robot (UWE, 2004)
6. Social robots
• Division of labour, specialised hardware
• Communication, cooperation, collaboration
• Collaboration gain (super-linear increase with
number of robots?)
• Understanding language and social behavior
• Swarms intelligence from many very simple
robots
• Human-Robot workgrounps
7. Dynamical systems vs. control
• Closed perceptionaction loop
• Everything is in the
senses
• Evolution
• No planning, no
representation
• Exploratory
• Potentially interesting
• Feed-forward, feedback
• Objective-driven, uses
prior knowledge
• Design
• Planning reqired for
complex goals
• Dependability
• Potentially useful
8. Distributed vs. centralized
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Modularity on all levels
Re-configurability
Fast local computations
Communication partially
replaced by local decisions
• Bio-inspired solutions
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Monitoring
Simplicity
Debugging
Communication
less demanding
9. Areas of applications
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Assembly, manufacturing, manipulation
Remote operation, exploration, rescue
Science and education
Prosthetics, orthotics, surgery, therapy
Service, transport, surveillance
Entertainment, toys, sports
Military
More dimensions
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Vision
Sensing and Acting
Locomotion, reaching and grasping
Dynamics and kinematics
Control
Internal organization, architectures