Lecture 1 Introduction No Video Part3

8/14/2019 Lecture 1 Introduction No Video Part3

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Modular Robots

Modular Reconfigurable Robotics is an approach to building robots for

various complex tasks. Instead of designing a new and different

mechanical robot for each task, you just build many copies of one simple

module. The module can't do much by itself, but when you connect many

of them together you get a system that can do complicated things. In fact,

a modular robot can even reconfigure itself -- change its shape by moving

its modules around -- to meet the demands of different tasks or differentworking environments.

http://www2.parc.com/spl/projects/modrobots/index.html


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Self-Reconfigurable Robots

Traditional approaches of building separate robots for separate tasksmay not be cost efficient and appropriate for those complex tasks inenvironments that are not human friendly.

Reconfigurable robot is modular, multifunctional, and reconfigurablefor different tasks at different mission stages.

Challenges: how to coordinate all modules to achieve a common goal

dynamically? Four layers: hardware, locomotion control, transform control, and

cognitive control.

Available Reconfigurable Robots

MTRAN( National Institute of Advanced Industrial Science and Technology,Japan)

SuperBot (Polymorphic Robotics Lab, University of Southern California)

Molecube (Cornell University)

Others


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M-TRAN (Modular Transformer)

http://unit.aist.go.jp/is/dsysd/mtran3/


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SuperBot (Polymorphic Robotics Laboratory, USC)

http://www.isi.edu/robots/superbot.htm


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CrossCube (Stevens Embedded Systems and Robotics Lab)

Limitations on locomotion designs andhigh-level control algorithms on theavailable reconfigurable robots

Our objective: to tackle those limitationsand develop a highly flexible locomotionmechanism and more intelligent GRN-based cognitive control algorithm to adaptto dynamic environments and tasks.

CrossCube Hardware and locomotion: lattice-based

robot module that is able to rotate, climband parallel move on other modulessurface

Transform and cognitive control: evolvinggene regulation network(GRN) basedalgorithms.


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CrossCube

Self-reconfigure robot modules tovarious shapes/forms based on

different task requirements orenvironments.

Can self-detect module failures andself-repair malfunctions byreconfiguration

From homogeneous modules toheterogeneous models

Challenges Flexible, robust, adaptive, reliable,

interactive, integration, etc.. Potential applications

Urban search and rescue, security,space exploration, transportationthrough narrow and complex space,

etc. (video demos)


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Biological Inspired Robot: Snake Robot (Tokyo Institute

of Technology, Shigeo Hirose Group)

On the evening of December 26, 1972, for the first time in the world wesucceeded in producing artificial serpentine movement at a speed ofapproximately 40 cm/sec using the principles of a serpentine movementwhich is the same as actual snakes. The entire length of the device is 2 m,and it has 20 joints.

From http://www-robot.mes.titech.ac.jp/robot/snake/acm3/acm3_e.html


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Biological Inspired Robot: Snake Robot (Tokyo Institute

of Technology, Shigeo Hirose Group)

Raise headSerpentine Propulsion

The system consists perpendicularly connected as a straight chain by the unit

that has batteries, a control board, and actuator of 1 DOF, shell structure hadlightweight and high rigidity.


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Swimming Snake Robot


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Biological Inspired Robots: legged robots


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Centralized versus Distributed Control Laws

Global Centralized Control

Allow for more coherent team cooperation

Often results in increased communication requirements

The knowledge is computationally costly

Oftentimes all the needed global knowledge is not known

Vulnerable with robot failures and in dynamic environment

Local Distributed Control

Computationally Simple

Handle dynamic environments well Oftentimes unclear as to howto design local control laws

Must rely on physical sensors

Oftentimes unclear as to how to design local control laws


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Biological-Inspired Swarm Robots

Swarm intelligence is an artificial intelligence (AI) technique based on

and modeled after the emergent, decentralized, self-organized,collective behavior of insect colonies, bird flocks, and animal herds.


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SI Natures Design: Insects

Organizing highways to and from their foraging sites by leaving

pheromone trails

Form chains from their own bodies to create a bridge to pull and hold

leafs together with silk

Division of labour between major and minor ants


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SI Natures Design: Birds

A flight of ducks use V formation to reduce air drag and conserve

energy

Optimize food searches by using the eyes of other ducks

Ducks in a flight gain protectionbetter predator avoidance odds


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Swarm Intelligence Principles

Positive Feedback

Ants are able to attract more help when a food source is found

More ants on a trail increases pheromone and attracts even moreants

Negative Feedback

Pheromone Decay

Distant food sources are exploited last

Randomness

Ant decisions are random

Food sources are found randomly

Multiple Interactions

No individual can solve a given problem. Only through the

interaction of many can a solution be found


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Swarm Robots

Many of the dangerous, dirty, or Null jobs can be performed more effectively by

groups of robots working together, such as swarms.

Applications

Urbane search and rescue,Surveillance systems, Exploration, Constructions

Much more .

Advantages

Parallel processing, cover more areas, coordination, robust and flexible Main challenges

Adapt their behaviors based on interaction with the environment and

other robots Become more proficient in their tasks over time

Adapt to new situations as they occur

Coordination and cooperation


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Swarm-Bots Project ( Marco Dorigo group in Europe)

The main objective of the Swarm-bots project is to study a novel approach to the

design and implementation of self-organizing and self-assembling artefacts.

This approach was inspired by the recent studies in swarm intelligence in social

insects and other animal societies. An artefact composed of a number of simpler, insect-like, robots, built out of

relatively cheap components, capable of self-assembling and self-organizing to adapt

to its environment


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Swarm Robots MIT/iRobot

http://people.csail.mit.edu/jamesm/swarm.php


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Multi-cellular based Multi-Agent Systems (Stevens

Embedded Systems and Robotics Lab)

Self-organization of large collective systems is a challenging task

Autonomous, adaptable, evolvable, robust, self-repairable, emergent

Suboptimal, non-controllable, non-predictable, not (easily) understandable

Trade-off between global (centralized) and local (distributed) control

Biological development, including cell growth, cell differentiation and morphogenesis,

can be seen as a self-organizing process

Robust to genetic and environmental changes

Use of global and local control

Predictable and relatively understandable

Biological development is under the temporal and spatial control of a gene regulatory

network(GRN) Can we borrow some ideas from developmental biology, in particular the

morphogenesis?

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Preliminary Experimental Results on Multi-Robot

Formation

The video demo can be downloaded from http://www.ece.stevens-tech.edu/~ymeng/lab_home.htm

Th H d W lki R b t1


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The Honda Walking Robot http://www.honda.co.jp/tech/other/robot.html1

http://www.youtube.com/watch?v=kLGk9Q49y7k

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Entertainment Robots: Humanoid Robots (SONY)


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DARPA Grand Challenge

The DARPA Grand Challenge has been the most significant event for

the robotics community in more than a decade.

A mobile ground robot had to traverse 132 miles of unrehearsed desert

terrain in less than 10 hours.

In 2004, the best robot only made 7.3 miles.

In 2005, Stanford won the challenge and the $2M prize in less than 7hours travel time, and ahead of four other finishers.


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DARPR Grand Challenge 2007

The Urban Challenge. Teams will compete to build an autonomousvehicle able to complete a 60-mile urban course safely in less than 6hours.

The DARPA Urban Challenge will take place in Victorville, Californiaon November 3, 2007.

"It was an important step to have autonomous ground vehicles thatcan navigate and drive across open and difficult terrain from city tocity. But the next big leap will be an autonomous vehicle that cannavigate and operate in traffic, a far more complex challenge for a'robotic' driver. So this November we are very excited to be moving

from the desert to the city with our Urban Challenge."

Dr. Tony Tether, Director, DARPA


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Unmanned Maritime System (Senior Design project)

Point of Contact: M. DeLorme (Center for Maritime Systems)

No of Students: 2

Fields of Interest: Robotics, autonomous systems

Project Sponsor: Office of Naval Research

DESCRIPTION:

The project involves the design, development and demonstration deployment of an unmanned

maritime system (UMS) or systems to perform a task to be specified by the project sponsor.

Students will be responsible for developing the system and deployment specifications based

on independent research and planning. This team will be part of a larger multidisciplinary

team working with students in Mechanical Engineering and Naval Engineering to accomplishthe project goals. Interested students MUST meet with Michael DeLorme

([email protected]) to further discuss the responsibilities and expectations of this project

and to submit a one page resume highlighting their qualifications as related to the proposed

project.

#


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Homework #1

In order to prepare your project, you may want to search for some

robot simulators from the websites. Please try to find at least two

robot simulators you like and try to use them to see if it is possible for

you to write control programs, such as localization, navigation, multi-

robot coordination, on those simulators.

You can find your project partners and build up a group (at most 3

persons for undergraduates, and 2 persons for graduates), or you like todo it individually (more credits).

For the course project, you have two options

Theoretical exploration: real research papers and propose some new

approaches

Building robotic systems, which includes building real robotic systems or

running on a simulation

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