ME 599/EE 546 - faculty.washington.edu
Transcript of ME 599/EE 546 - faculty.washington.edu
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ME 599/EE 546: Biology-inspired
roboticsLecture 1
Sawyer B. Fuller
Goals: • Describe the need for “biology-inspired robotics” • Describe how this course works
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biology-inspired robot control• MW 10:30-11:50 in MEB259
• Instructor: Prof. Sawyer B. Fuller ([email protected])
• Office hours: Wednesdays 1:30-2:20 in MEB 321
• Prereq’s: an undergraduate degree in ME, EE, or Aero
• Website: http://faculty.washington.edu/minster/bio_inspired_robotics/
• lecture slides, papers, and other materials will be posted there
• submit all coursework on Canvas
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Robot (noun)a machine capable of carrying out a complex series of
actions automatically
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UW Classes that take a more classical approach to robotics:
• Mechatronics:
• ME581: Digital control systems (spring)
• Dynamics and control:
• EE 543/544: Kinematics and dynamics of robot arms/manipulators
• ME583: Nonlinear control
• Perception and planning:
• EE 576: computer vision and robotics (spring)
• CSE 571 Probabilistic Robotics: Perception, localization, mapping (fall)
• AMATH / CSE 579: Intelligent control through learning and optimization (Emo Todorov)
• ME599: Advanced Robotics: Perception and multi-robot control (fall. Ashis Banerjee)
• Also:
• CSE 590: Robotics colloquium seminar (weekly speakers)
• BI 427: Animal biomechanics (fall, Tom Daniel)
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“biology-inspired robotics”
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current state of the art
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• very power hungry (20x human of same weight) • only in controlled environments
Honda’s Asimo
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current state of the art
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Google’s self-driving car
• power hungry - requires a bank of computers • only in controlled environments
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application areas where current robotics is still outperformed by nature
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tiny robots (minimal computation,
limited sensing)
complex environments and robots (models are inadequate
and behavior must be learned)
agile robots (limited time to compute)
soft robots (nonlinear
stress/strain curve hard to model)
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animal locomotion
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• rich behavioral repertoire
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animal locomotion
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Animal locomotion
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• aggressive, dynamic motions
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Animal locomotion
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• complex environments • minimal energy expenditure on computation
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Aerial autonomy at insect scale
(images to scale)
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Autonomous Insect Robotics Laboratory Est. 2015
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Dr. Sawyer B. Fuller 15
Ma, Chirarattananon, Fuller, and Wood, Science 2013
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Dr. Sawyer B. Fuller 16
RoboBee flight control
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Dr. Sawyer B. Fuller17
previous work
• external power • external sensing • external computation
• improved capabilities • onboard sensing • onboard computing • onboard power
current research
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Dr. Sawyer B. Fuller18
355 nm laser micromachining
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assembly
fold, add
piezo & wing
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Dr. Sawyer B. Fuller
piezo actuation
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wing
flexure joints
V+
Vs
bimorph actuator
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Dr. Sawyer B. Fuller21compound eyes
gyroscopic halteres
(angular velocity)
ocelli(direction of sun/sky)
antennae (wind, smell,
sound, gravity)
flapping wings
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current research
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vision (tiny camera)
odor (using moth antenna) onboard power
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biology-inspired robotics
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this course focuses on two areas where biology excels
1. mechanical intelligence
2. adaptation through evolution and learning
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mechanical intelligence
• system is stable without active feedback
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this fish is dead!
Liao, 2004
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robot mechanical intelligence
26collins 2001
walks with no feedback and very little power
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example of adaptation: reflexive/model-free control
• minimal internal representation
• cascaded behaviors
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how mud wasps build nests
Smith, 1978
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example of model-free control
28bongard 2011
a gait learned by a neural network
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how this course works• This is a research-oriented course
• Three parts to the coursework:
1. Reviewing and discussing 1-2 assigned papers per class session
2. Presenting 1-2 papers to the class
• assigned based on a lottery and your preferences
3. Final project
• this year: you’re the funding agency
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This year’s final project• You’re the funding agency!
• each team/individual submits a research proposal at the end of the quarter
• format: 3 pages, NDSEG graduate fellowship format
• includes preliminary work you did in this course
• show a “proof-of-concept” initial work in some aspect of biology-inspired robotics, probably in simulation
• can be used to for your actual application
• There will be a peer vote for best proposals
• criteria: quality of preliminary results, future promise
• top 3 proposals split the money
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Wednesday• due by Tuesday 9pm: Review of paper 0
• because of the short time available, only complete part 1 (synopsis) of the four elements of the review.
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next week• paper 1 review due Sunday
• paper 2 review due Tuesday