Subscribe To Robotics | IntroDuction | History | Home

Friends Dont Forget To check the archieve at the left end of the page

Make Your Own Robot Tutoials

Simple Beetle Bot | Wired Robot | Combat Robot | Solar Engine |

Beam Symet | Photopopper | Beam Trimet | Line Follower |

Latest Updates
Driver Less Car | I-Sobot | MotherBoard | MicroController | Artificial Brain |

Camera Sensors Hardware | Remote Control Working


Saturday, December 29, 2007



AC Motor AC Motor

note: I have never actually used an AC motor, so feel free to correct and verify this information

note: this page is a place holder until a better tutorial is written

Unlike DC motors which work using a single constant current, AC motors run under 3 phase current. To have 3 phase power on a robot, you either need a big bulky/expensive DC->AC converter, or you must tether it to a wall socket. You probably won't use AC motors unless your robot is stationary, such as a robot arm or robot pancake maker. Unless you want the pancake maker to also walk your dog or something . . . But here they are anyway:

* Polarized (current cannot be reversed)
* Typically from 120-240V AC, usually to match mains power
* Higher voltages generally mean more torque, but also require more power
* Rarely used on mobile robots due to power requirements
* note: A universal motor has brushes like a DC motor, but will operate on AC or DC

* When buying a motor, consider stall and operating current (max and minimum)
* Stall Current - The current a motor requires when powered but held so that it does not rotate
* Operating Current - The current draw when a motor experiences zero resistance torque
* It is best to determine current curves relating voltage, current, and required torque for optimization
* When a motor experiences a change in torque (such as motor reversal) expect short lived current spikes
* Current spikes can be up to 2x the stall current, and can fry control circuitry if unprotected
* Use diodes to prevent reverse current to your circuitry
* Check power ratings of your circuitry and use heat sinks if needed

Power (Root-Mean Squared Voltage x Current)
* Running motors close to stall current often, or reversing current often under high torque, can cause motors to melt
* Heat sink motors if not avoidable

* When buying a motor, consider stall and operating torque (max and minimum)
* Stall Torque - The torque a motor requires when powered but held so that it does not rotate
* Operating Torque - The torque a motor can apply when experiencing zero resistance torque

* Motors run most efficient at the highest possible speeds
* Gearing a motor allows the motor to run fast, yet have a slower output speed with much higher torque
* Remember that torque determines acceleration, so a fast robot with poor acceleration is really a slow robot
* If uncertain, favor torque over velocity

* More efficient than DC motors
* Typically most efficient at rated voltage and frequency
* Use gearing (opt to buy motors with built-in gearing or gear heads)

Control Methods
* Modifying the AC frequency can alter speed and torque
* Encoder - device which counts rotations of wheel or motorshaft to determine velocity for a control feedback loop
* Tachometer - device which measures current draw of motor to control output torque

This circuit will allow you to control the speed of an AC motor.
The bridge rectifier produces DC voltage from the 120VAC line.
A portion on this current passes through the 10K ohm pot.
The circuit comprised of the 10k pot rated at 3W+, the two 100 ohm resistors and the 50uf capacitors delivers gate drive of the SCR.
The diode D1 protects the circuit from reverse voltage spikes.
The ratings of the bridge rectifier and the SCR should be 25 amps and PIV 600 volts.
The diode D1 should be rated for 2 amps with PIV of 600 volts.
The circuit can handle a load up to 10 amps. The SCR should be very well heat sinked.

AC Motor Speed Control Schematic

Saturday, December 15, 2007


Hi Guys,

I hope the funda of C.G location, drives, gears,etc is clear from the past two chapters.
Now lets begin with the 3rd chapter ie. The circuitry of a normal robotic car.
This circuitry will enable the robo to move forward, backward, left and right.

Now starting with circuitry we need 2 two way switches. These switches conduct in both directions hence the name two way switches.

Firstly we sort the terminals

  • 1 and 6 { first switch}

  • 2 and 5 { “ }

  • 1’ and 6’ { second switch}

  • 2’ and 5’ { “ }

    Then we choose the terminals of first switch ie. 5 as positive or 6 as positive . either way one wants to, can do so.
    But one thing has to be kept in mind that if 5 is positive in 1st switch then 5’ should be the + ve terminal of 2nd switch.
    The same convention for negative terminal.


  • Terminals 3,4 from 1st switch and terminals 3’, 4’ from 2nd switch are the power supply terminals.
    In this too we have to use the same convention like the +ve and –ve terminals of either switches.
    If we take 3 as +ve than we have to take 3’ as +ve and give this combined combination to the powe supply as +ve.
    The same case for negative ie. Combination of 4 and 4’ for negative.
    Or the reverse can also be done.

  • The terminals 3 and3’ are sorted with the help of wires and similarly we sort the terminals 4 and 4’.
    The output single wire from 3 and 3’ combination will be given to + ve of the power supply and the output single wire from the combination of 4 and 4’ will be given to the – ve of the power supply.

  • Power supply can be an adaptor or an eliminator of 12 volts.

    This is the connection we make in switch. Now how to proceed with combining this connection with motors.


    We will call the top and bottom motors on left hand side the left pair and
    The top an bottom motors on right side the right pair
    The above picture shows the left pair . similarly we have to connect the right pair.


    We will bring together the positive of both the top and bottom motor on left hand side and sort them . similarly we will sort the negative of both the motors of the left pair.
    Now as we did in the left pair we will sort the +ve wire of both motors together and sort the –ve of the motors .

    So in all we get 4 output wires from the motor connection .
    2 from left pair and 2 from right pair.


    Now to connect the switch to motors we have to choose four output from the switch connection as we have four output from the motor connection.

    We will consider the terminal 5 and 6 from the first switch and 5’ and 6’ from second switch.
    Considering the terminal 5 from first switch positive terminal 5’ will be positive from second switch. Similarly 6 and 6’ are considered as negative.

    Now the connection goes this way

    positive wire combination from left pair motors will be connected to point 5
    negative wire combination from left pair motors will be connected to point 6
    positive wire combination from right pair will be connected to point 5’
    negative wire combination from right pair will be connected to point 6’

    these connections are made with the help of intermediate wires called the rainbow wires. I suppose…..
    these are a bunch of thin wires used for internal circuitry.

    If suppose u connect positive wire from left hand pair from motors to red wire then the other end of red wire will be connected to point 5.
    Similarly for all connections.

    The length of the intermediate wire depends on the user. Normally we take 8 to 9 meters. That will be the range of your robo.

Wednesday, December 12, 2007

Micro Robots

Swarm robotics
is a new approach to the coordination of multirobot systems which consist of large numbers of relatively simple physical robots. The goal of this approach is to study the design of robots (both their physical body and their controlling behaviors) such that a desired collective behavior emerges from the inter-robot interactions and the interactions of the robots with the environment, inspired but not limited by the emergent behavior observed in social insects, called swarm intelligence. It has been discovered that a set of relatively primitive individual behaviors enhanced with communication will produce a large set of complex swarm behaviors.

Unlike distributed robotic systems in general, swarm robotics emphasizes a large number of robots, and promotes scalability, for instance, by using only local communication. Local communication is usually achieved by wireless transmission systems, using radio frequency or infrared communication.
Potential application for swarm robotics include tasks that demand for extreme miniaturization (nanorobotics, microbotics), on the one hand, as for instance distributed sensing tasks in micromachinery or the human body. On the other hand, swarm robotics is suited to tasks that demand for extremely cheap designs, for instance a mining task, or an agricultural foraging task. Artists are using swarm robotic techniques to realize new forms of interactive art installation.
Both miniaturization and cost are hard constraints that emphasize simplicity of the individual team member, and thus motivate a swarm-intelligent approach to achieve meaningful behavior on swarm-level.
Further research is needed to find methodologies that allow for designing, and reliably predicting, swarm behavior, given only features of the individual swarm members. Here, video tracking is an essential tool for systematically studying swarm-behavior, even though other tracking methods are available. Recently Bristol robotics laboratory has developed an ultrasonic position tracking system for swarm research purposes.

Swarm intelligence (SI):Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.

SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is no centralized control structure dictating how individual agents should behave, local interactions between such agents lead to the emergence of global behavior. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.
In SI systems, the agents follow very simple rules generally out of the need for survial which leads to very complex rules/algorithms at the systems level.
The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms.

Example algorithms:

Ant colony optimization:

Ant colony optimization is a class of optimization algorithms modeled on the actions of an ant colony. Artificial 'ants' - simulation agents - locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions. One variation on this approach is the bees algorithm, which is more analogous to the foraging patterns of the honey bee.

Particle swarm optimization:

Particle swarm optimization or PSO is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles. Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.
Stochastic diffusion search
Stochastic Diffusion Search or SDS is an agent based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis. In the standard version of SDS such partial function evaluations are binary resulting in each agent becoming active or inactive. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in some species of ant. A positive feedback mechanism ensures that, over time, a population of agents stabilise around the global-best solution. SDS is both an efficient and robust search and optimisation algorithm, which has been extensively mathematically described.


Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. ESA is thinking about an orbital swarm for self assembly and interferometry. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George

A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds. Tim Burton's Batman Returns was the first movie to make use of swarm technology for rendering, realistically depicting the movements of a group of penguins using the Boids system. The Lord of the Rings film trilogy made use of similar technology, known as Massive, during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.

The inherent intelligence of swarms has inspired many social and political philosophers, in that the collective movements of an aggregate often derive from independent decision making on the part of a single individual. A common example is how the unaided decision of a person in a crowd to start clapping will often encourage others to follow suit, culminating in widespread applause. Such knowledge, an individualist advocate might argue, should encourage individual decision making (however mundane) as an effective tool in bringing about widespread social change.