User talk:Cs455grouph

ADVANCED ROBOTICS

Table of Contents

Future of Robots
The first generation of modern robots were, however, a far cry from these anthropomorphic visions, and most robot builders have made no attempt to mimic humans. The Unimate, a popular assembly-line robot from the 1960s, was capable only of moving its one arm in several directions and opening and closing its gripper. Today there are more than two million Roomba robots scurrying around performing a task (vacuuming) that used to be done by humans, but they look more like fast turtles than maids. Most robots will continue to be utilitarian devices designed to carry out specific tasks. But when we think of the word â€robot,â€ Capek´s century-old concept of machines made in our own image still dominates our imagination and inspires our goals.

Programming Concepts
This rich educational tool is designed to take students with no programming experience and teach them how to program using a C-based programming language. It is designed to teach the beginning programmer how to program Innovation First’s VEX robot using ROBOTC; the best programming language for VEX robots. Over 40 short tutorial videos, complemented by more than 275 pages of easy-to-follow, printable guides take the student step-by-step from downloading their very first program, to incorporating sensor and transmitter data into their code. Additionally, there are over 35 programming challenges, designed to re-enforce each programming concept. Teaching ROBOTC for IFI VEX Robots includes 5 sections:

1. Setup: Students build their robots, learn what firmware is, how to download the firmware to their robots, and create their first simple programs.

2. Fundamentals: Students learn their role as the programmer, how the robot sees the world, and what syntax is.

3. Movement: Students program their robots to solve the Labyrinth Challenge. They learn how to control their robot’s direction and speed autonomously.

4. Radio Control: Students learn how to incorporate feedback from the Radio Control Transmitter into their ROBOTC programs, allowing them to compete in the Minefield Challenge.

5. Sensing: Students learn how robots use feedback from sensors to interpret the world around them. Students improve their robot’s performance in the both Labyrinth Challenge and the Minefield Challenge using various sensor feedback.

Robot Control
Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems. The external input of a system is called the reference. When one or more output variables of a system need to follow a certain reference over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system.

The usual objective of control theory is to calculate solutions for the proper corrective action from the controller that result in system stability, that is, the system will hold the set point and not oscillate around it.

Robot Hardware
The main component in the kit is a brick-shaped computer called the NXT Intelligent Brick. It can take input from up to four sensors and control up to three motors, via RJ12 cables, very much similar to but incompatible with RJ11 phone cords. The brick has a 100×60 pixel monochrome LCD display and four buttons that can be used to navigate a user interface using hierarchical menus. It also has a speaker and can play sound files at sampling rates up to 8 kHz. Power is supplied by 6 AA (1.5 V each) batteries in the consumer version of the kit and by a Li-Ion rechargeable battery and charger in the educational version.

The Intelligent Brick remains unchanged with NXT 2.0. A black version of the brick was made to celebrate the 10th anniversary of the Mindstorms System with no change to the internals.

Mathematics of Robot Control
Algebraic and differential topology. Current work includes the application of techniques from loop space theory and low dimensional topology to understand configuration spaces of many-particle/many-body systems, as well as techniques from global differential topology to analyze configuration spaces of arbitrary length closed chains in two and three dimensions with spherical, revolute, and prismatic joints. Similar tools are also directly relevant to: · Distributed sensing and actuation systems, such as those made possible with MEMS and nanotechnology; and · High dimension design problems, such as intelligent vehicle-highway and air-traffic management systems.

Robot Programming Languages
C: C offers power but is much more portable than Assembly. For most µcontrollers there is a C compiler available. The differences between µcontrollers is smaller here, except for using hardware. Learning C is much easier than learning Assembly, still C isn't an easy language to learn from scratch. However these days there are very good books available on this subject. Freeware:GCC Tools for AVR Studio Software Basic: For many µcontrollers there are special flavours of Basic available. This is the easiest and fastest way to code µcontrollers, however you'll have to sacrifice some power. Still modern basic compilers can produce very impressive code. Limited Freeware/payware:Bascom AVR Very good Basic compiler for AVR. Limited to 4Kb programs. There is also a version available for the 8051 µcontrollers. Limited Freeware/payware:XCSB PIC Basic compiler. Lite version. No 32-bit integer and floating point support. (OS/2 WARP, Win95, Win98, Win2K, XP and Linux) Forth: PFAVR (GPL) Needs external RAM. ByteForth Dutch and works without external RAM, there is also a building book (Dutch only for now) available for Ushi our robotic project. Python Pyastra PyMite

Obstacle Avoidance
Obstacle avoidance is one of the most important aspects of mobile robotics. Without it robot movement would be very restrictive and fragile. This tutorial explains several ways to accomplish the task of obstacle avoidance within the home environment. Given your own robots you can experiment with the provided techniques to see which one works best.

Task Planning and Navigation
Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, such as labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrates hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.

Robot Vision
Robot Vision presents a solid framework for understanding existing work and planning future research. Its coverage includes a great deal of material that important to engineers applying machine vision methods in the real world. The chapters on binary image processing, for example, help explain and suggest how to improve the many commercial devices now available. And the material on photometric stereo and the extended Gaussian image points the way to what may be the next thrust in commercialization of the results in this area. The many exercises complement and extend the material in the text, and an extensive bibliography will serve as a useful guide to current research.

Knowledge Based Vision Systems
Knowledge-based approach to vision modeling for robot guidance, where advantage is taken of constraints of the robot's physical structure, the tasks it performs, and the environments it works in. This facilitates high-level computer vision algorithms such as object recognition at a speed that is sufficient for real-time navigation.

Robots and Artificial Intelligence
Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent.

Like the term "robot" itself, artificial intelligence is hard to define. Ultimate AI would be a recreation of the human thought process -- a man-made machine with our intellectual abilities. This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas. Roboticists are nowhere near achieving this level of artificial intelligence, but they have made a lot of progress with more limited AI. Today's AI machines can replicate some specific elements of intellectual ability.