Biomimetics - Biologically Inspired Technologies - Yoseph Bar Cohen Episode 2 Part 9 pps

Figure 4.7 Artificial ontogeny: Growing machines using gene regulatory networks.. 2004 Functional freeform fabrication for physical artificial life, Ninth International Conference onArti

Minimum jerk criterion, 410

Minimum joint torque-change criterion, 412

Minimum muscle force change criterion, 412–413

Mitochondria, 45 Mixed integer quadric programming (MIQP) algorithms, 423

Mixed logical dynamical (MLD) system M-line, 45–46

Mobility, 177–178, 496–498, 500–501 Modern control theory, 400–401 Modular design, 51

Modular organization, 212–214, 217; see also Bio-nanorobot

representation of, 216 Modulation, 260

of electrical pulses, 262

of pulse width, 260 Molecular antenna, 234 Molecular building blocks, 230–231 Molecular ink, 234–235

biologically active, 235 Molecular machines, 204, 210, 229–230, 234; see also Biomolecular machines

DNA-based, 205 field of, 204 natural, 204 Molecular mechanical methods, 215 Molecular self-assembly, 231–232, 234 definition, 233

Monkey-see/monkey-do principle, 78, 98 Monolayer, 249, 254

fibroblast, 255 Morphology, 132–133, 135–136, 145 evolving, 144

new physical, 147 representations, 138 robot’s, 140 Motility, 178, 180 Motor cells, 482; see also Hydrostat motor cells Motor cortex, 423

Motor learning, 402, 404, 410 approach, diffusion-based, 405

of biological system, 405 Motor unit, 44

M-protein, 46 Multicomponent braid, 334 Multifunctional composite, 311, 328, 332, 337 Multifunctional materials, 497–498

Multi-legged dynamic walking movements, 423

Multiresolution computational mesh, 436 Muscle fiber, 43–44, 49

number of, 50 set of, 44 Muscle function, 43–45, 53 influence on, 43 variety in, 51

Nanomachine, 203, 205, 209 components, creation of, 202 Nano-materials, 229

Nanomedicine, 204 Nanometer coatings, 234 Nanorobot; see Nanorobotics Nanorobotics, 202, 204, see also Bio-nanorobotics field of, 203, 224

Nanoscaffold, 234 Nanosensors, 210 Nanostructures, 499 Nanotechnology, 202, 204, 224 field of, 223

research in, 205, 224 Nanotechnology, 466–467 Nanotubes (NT), 276 Nastic movements, 473–474, 481 Nebulin, 45

Necrosis, 251–252 cellular, 252 rapid, 252 Negative refractive index, 312, 323

Nematic structure, 349 Nephila clavipes, 367 Nerve, 246, 256; see also Nerve muscle interfaces motor, 258

Nerve muscle interfaces, 258 Network sensors, 337 Network, 404; see also Neural network interaction, 423 Neural network, 99, 116, 403–404 artificial, 404, 417, 421 cascade, 412

Neural superposition eye, 301–302 Neuronal network, 121

Neurones, 355 Nitric oxide, 410 Noncognitive functions, 63 Noncovalent weak interactions, 233 Nonlinear coordination

transformation, 403

Nonlinear programming, 158 Novacor, 455, 457–458 Nucleotides, 229

Partial differential equation (pde), 405

Passive velocity field control (pvfc), 415

Peptide nanofiber scaffolds, 230 Peptide surfactants, 230, 233, 236 Perflurocarbons

Perfusion, 246, 252–253, 256–257 bioreactor, 257

Phenotype, 138, 244, 246, 251, 254, 257 encode a, 143

adult, 249, 256, 258 arrested or retrogade, 253 muscle, 246, 249, 256–257 neonatal, 248

skeletal muscle, 257 tissue, 253

Photonic crystals, 149 Photoreceptors, 428–429, 432 Physiological, 359

diversion, 360 neurochemical, 359 Physiological cross sectional area (PCSA), 50

PI control, 422 Piecewise affine (PWA) system, 423 Piezoelectricity, 271

Pith parenchyma, 483–484 Planetary robotics, 280 Planning, 401–402, 412 Plant movements, 474, 480, 482, 491 Plant pumps, 475–476

Pleated columns, 490 Plywood, 484 p-Median, 164, 166 Pneumatic activation, 268 Pole assignment principle, 400 Pollution, 507

Polyaniline (PAN), 275 Polydisperse, 367 Polymer cracking, 329–330 Polymer healing, 329 Polymer interactions, 367 Polyvinylidene Fluoride (PVDF), 269 Pose sensitivity, 63

Power dissipation of implanted electronics, 440 P-proteins, 481

Precedence principle, 70, 91 interactions, 119

Programmed assembly, 231–232, 234 Projectile, 353, 355, 358

water stream, 358 Prosthesis, 431, 437, 440 cortical, 428–429 epiretinal, 431, 436

Removal of population members, 161, 164

Replication fork of DNA, 365

Rotaxanes, 209 parent of, 209 S

Sampling, 393 Sarcomere, 43, 44 activation of, 49 arrangement of, 47 damaged, 50 dependence of, 46 design of, 44, 47 force production of, 46 invertebrate, 47 length of, 46 local disruptions of, 51 organization of, 45 popping, 51 serial addition of, 49 use of, 52

vertebrate, 46 Sarcomeres, 373 Sarcoplasmic reticulum, 45 Satellite cell, 255

Scheduling, 400, 423 Screens, 15

Self healing, 202, 220 Self replication, 218, 220–221, 224 concept of, 219, 223

mimetics of, 218 Self-assembling peptides, 232, 234–235 Self-assembly, 7, 10, 23

definition of, 9 guided device-to-substrate, 36 Self-balancing, 220

property of, 221–222 Self-organization, 254–255, 402, 404–405 algorithm, 405

Self-replicating mechanisms, 219 classification of, 219

Sensitive fern, 5, 6 Sensors, 497, 500, 501, 504, 507 Sensory Substitution devices, 430 Sensory-motor coordination, 403, 405, 407 Septicemia, 353

Sequence control, 400 Servomechanism problem, 400 Set covering, 158

Setae, 13 Shakey robot, 8 development of, 8

Shape memory alloys (SMA), 268

Shark skin, 366, 371–372

Sheets, b-, 367–368

Shells, 366, 369–370

Sherrington’s law, 402

Short-term memory, 66, 101, 112; see also Memory

Side-effects of long term implantation, 433

functional, 383 hydrophobized, 382 self-cleaning, 390 Surveillance, 360 electrosensing, 360 Survival of the fittest, 158, 159 concept of, 160

principles of, 172 Swelling bodies, 477–478, 481 Symbol to action command association, 119, 122–123 Symbols, 58

active language, 63 active visual, 63 complex feature detector, 91 excitation of, 72

excited, 59 high level, 63 indices of, 67 multi, 80 multiple lower level, 102 primary layer, 95 small set of, 109 world of, 81 Synaptic modification, 111 mechanisms of, 112 plasticity, 410 Synchronization, 423 Synergy, 402, 406 Synthetic life, 6 Synthetic molecular motors, 209 System control engineering, 399 T

Tabu search, 159, 162 Tactic movement, 474 Target symbol, 67, 112 high-frequency, 68 particular set, of a, 125 Teleoperation, 34 Telepresence, 34, 35 Template-based synthesis, 366 Tendon, 44, 244, 252, 255–256, 258 achilles, 50

elastic, 44 geometry, 246 long, 44, 47 muscle, 52 scaffold-based, 258 self-organizing, 258

Total hip replacement, 461–462

Total knee replacement, 462

Ultrasonic/sonic driller/corer (USDC), 10 V

Valine, 235, 236, 238 Van der Waals force, 385, 387 Van der Waal forces, 368, 373 Van der Waals interactions, 233 Variational method, 407 Vascular, 246, 256 bed, 244, 247, 252, 256–257 cardio, 249

neuro, pedicles, 246 tissue interface, 256 Velamen, 479 Ventricular assist devices, 454–455, 464 Venus fly trap, 5, 474, 482, 487 Vertebrate, 45, 46, 47

function of, 49 myosin filaments of, 46 skeletal muscle, 47 Virtual reality, 204 techniques, 203 Virtual robots, 502 Visual cognition, 91 W

Water-mediated hydrogen bonds, 233 Wet adhesion, 13

Wetting, 381; see also anti-wetting, 390–391 Wheatgrass, 488

Whole arm cooperative manipulation, 403 Wiki online, 511

Wilting, 484, 490 Wire network, 1-, 333 Woodpecker, 496, 498–499 Workloop, 43

Z Z-disk, 45,46 Zuotin, 232, 236

Figure 1.3 Bug-eating plants with traps that developed from their leaf.

Figure 1.14 The spider constructs an amazing web made of silk material that for a given weight it is five timesstronger than steel

Figure 1.18 MACS crawling on a wall using suction cups.

Figure 1.19 JPL’s Lemur, six-legged robots, in a staged operation (Courtesy of Brett Kennedy, JPL.)

Figure 4.2 Evolving a controller for physical dynamic legged machine (a) The nine-legged machine is powered

by 12 pneumatic linear actuators arranged in two Stewart platforms The controller for this machine is an open-looppattern generator that determines when to open and close pneumatic valves (b) Candidate controllers areevaluated by trying them out on the robot in a cage, and measuring fitness using a camera that tracks the redfoot (see inset) (c) Snapshots from one of the best evolved gates (From Zykov, V., Bongard, J., Lipson, H., (2004)Evolving dynamic gaits on a physical robot,Proceedings of Genetic and Evolutionary Computation Conference,Late Breaking Paper, GECCO’04 With permission.)

Linear Actuator

Bar

Ball Joint

Infinite Plane

Morphology (Body)

Neuron

Control (Brain)

of two different evolutionary runs, showing instances of speciation and massive extinctions from generation 0 (top)

to approximately 500 (bottom), (d) progress of fitness versus generation for one of the runs Each dot represents arobot (morphology and control), (e) three evolved robots, in simulation (f) the three robots from (e) reproduced inphysical reality using rapid prototyping (From Lipson, H., Pollack, J B., (2000)Nature, 406, 974–978 Withpermission.)

Figure 4.7 Artificial ontogeny: Growing machines using gene regulatory networks (a) An example of cells thatcan differentiate into structural, passive cells (dark) or active cells (bright) which contains neurons responsible forsensing (T ¼ touch, A ¼ angle) and motor actuation (M) The connectivity of the neurons is determined by

propagation of ‘‘chemicals’’ expressed by genes and sensors, who are themselves expressed in response tochemicals in a regulatory network (b) Three machines evolved to be able to push a block, (c) The distribution ofgenes responsible for neurogenesis (red) and morphogenesis (blue) shows a clear separation that suggests anemergence of a ‘‘body’’ and a ‘‘brain’’ (From Bongard, J C., Pfeifer, R., (2003) Evolving complete agents usingartificial ontogeny, In: Hara, F., Pfeifer, R., (eds), Morpho-functional Machines: the New Species (DesigningEmbodied Intelligence), Springer-Verlag, New York, New York With permission.)

Figure 4.9 (a) Reconfigurablemolecube robots (From Zykov, V., Mytilinaios, E., Lipson, H., (2005) Nature, 435(7038), 163–164 With permission.) (b) Stochastic modular robots reconfigure by exploiting Brownian motion, andmay allow reconfiguration at a micro-scale in the future (From White, P J., Kopanski, K., Lipson, H (2004)Stochastic self-reconfigurable cellular robotics, IEEE International Conference on Robotics and Automation(ICRA04) With permission.) (c) Rapid prototyping (d) Future rapid prototyping systems will allow deposition ofmultiple integrated materials, such as elastomers, conductive wires, batteries, and actuators, offering evolution of alarger design space of integrated structures, actuators, and sensors, not unlike biological tissue (From Malone, E.,Lipson, H (2004) Functional freeform fabrication for physical artificial life, Ninth International Conference onArtificial Life (ALIFE IX),Proceedings of the Ninth International Conference on Artificial Life (ALIFE IX) Withpermission.)

Figure 6.9 ‘‘Mask,’’ a 5 in self-portrait by Ron Mueck, a graduate of Jim Henson Creature Shop, a leadinganimatronics studio (Photo by Mark Feldman [Feldman, 2002 website] With permission.)

Figure 6.13 UTD human emulation robots with F’rubber

Figure 6.17 Author’s latest robot EVA Because SPEM silicone requires little force to move, this robot’s 36 DOFrun for hours on four AA batteries.

Materials

(proteins and DNA)

Design and Mechanisms (revolute joint, actuators)

Usability range

(applicability in diverse enviroments)

Characteristics

(durability, rigidity)

Machine

Machine nanomimetics

Bio-nano roboticsBio-nano mimetics

Figure 7.1 Biomimetics — bio-nano robotics, inspired by nature and machine

Bio-nano Swarms

A Bio-nanoComputational Cell

Automatic Fabricationand InformationProcessing

A Bio-Nano InformationProcessing cell

Automatic FabricationFloor

STEP 4STEP 3

STEP 2STEP 1

Project Progression

2023

DistributiveIntelligenceProgramming andControl

Nano made devices

Nano made devices

man-MicroDevices

Self assembly, sensing,

and trigger mechanism

Amplificationmechanisms

Macro actuators orcommunication devices

MicroDevices

PowerSource

Macro World

Figure 7.10 Feedback path from nano- to macro-world route

The SPHERE represents energy and data storage arrangements for the robat

The RING represents the spatial area defined on the inner core for the binding

of the Module B and Module C

The DISC represents spatial area defined for Module D and for possible connections

What do they have in common? Stone walls & Proteins

on the hydrophilic side (b) The second class of self-assembling peptide belongs to surfactant-like molecules Thesepeptides have a hydrophilic head and a hydrophobic tail, much like lipids or detergents They sequester theirhydrophobic tail inside of micelle, vesicles or nanotube structures and expose their hydrophilic heads to water Atleast three kinds molecules can be made, with
, þ,
/þ heads.

Figure 8.5 The bacterial ribosome 30S ribosome (left panel) and 50S ribosome (right panel) The ribosome is one

of the most sophisticated molecular machines nature has ever self-assembled It has more than 50 different kinds ofproteins and 3 different size and functional RNAs, all through weak interactions to form the remarkable assemblyline (Source: http://www.molgen.mpg.de/~ag_ribo/ag_franceschi/.)

Figure 8.8 When primary rat hippocampal neuron cells are allowed to attach to the peptide scaffolds, the neuroncells not only project lengthy axons that follow the contours of the scaffold surface, but also form active andfunctional synaptic connections, each green dot is a functional neuronal connection (upper panel) Furthermore,when the peptide scaffold was injected into brain of animals, it bridged the gap and facilitated the neural cells tomigrate cross the deep canyon (lower panel) The animals regained their visual function Without the peptidescaffold, the gap remains, and the animals did not regain visual function

Figure 8.10 Self-assembling peptide nanotubes Peptide detergents: V6D with the tube diameter ~30 to 50 nm(left panel), A6K with the tube diameter ~20 to 30 nm (middle panel) and the model for V6D The openings of thenanotubes are clearly visible The wall of the tube has been determined using neutron scattering as ~5 nm,suggestive a bi-layer structure modeled here.

Peptide detergents stabilize membrane

proteins through hydrophobic interactions

Peptide detergentmicelles and monomers

lipid-detergentcomplex

Figure 8.11 Schematic illustration of designed peptide detergents used to solubilize and stabilize membraneproteins When mixed with membrane proteins, they solubilize and stabilize them, presumably at the belt domainwhere the membrane proteins are embedded in lipid membranes

0 2 4 6 8 10 12 14 16 18

time (sec)

stim (V) Force( mN)

D B

Figure 9.1 (A) Self-organized skeletal muscle construct after 3 months in culture, length ~12 mm (B) Rat cardiacmyocyteþ fibroblast monolayer in the process of delaminating and self-organizing into a functional cardiac muscle

construct, 340 h in culture (C) Self-organized cardiac muscle construct, attached to laminin-coated suture anchors,

380 h in culture (D) Electrically-elicited force trace from the cardiac muscle construct shown in C, stimulationpulses shown below, contractile force trace shown above (raw data, unfiltered)

Figure 9.2 Left: axonal sprouting (A) from an explanted motor neuron cell cluster (V) toward a target tissue (T), inthis case, an aneural cultured skeletal muscle ‘‘myooid.’’ Right: a simple cell culture system demonstrating axonalsprouting between neural (PC–12) and myogenic (C2C12) cell lines This co-culture system allows the study ofsynaptogenesis in culture (Photographs taken by members of the Functional Tissue Engineering Laboratory at theUniversity of Michigan: Calderon, Dow, Borschel, Dennis.)