Waleed has 60+ answers in Yes, this happens quite frequently. The standard robotics tools (e.g. Robot Operating System) can be hooked up to a simulated environment (such as Gazebo) and then without any changes, the same code can run on a physical robot. For example: Page on youtu.beshows the simulated environment being used for scene understanding.Most Viewed Writer in with 30+ answersAh the good old simulation vs reality. A world in a MMO is s simplified model of the world: physics is poor if even present, NPC are not seeing the world but more dealing with abstract views of it (otherwise it would require a lot complex 3D computation to be done with each NPC as opposed to do this only for the player), the world is often very staticAnd the same goes when you use a simulation as present in Robotic community such as Gazebo (Gazebo). While it has a fairly advanced physic engine (at least compared to your favorite MMO) and image perception is done through proper projection the data still remains cleaner and the dynamic more contained than what you will experience in the real world.

A simulation by definition relies on models which are an abstraction/simplification of the much more complex world. So yeah you can use it to do initial tests but will quickly realize that reality is much prone to Murphy's law if you ever try to run the same thing in a real physical system.Chocolate Lab Puppies For Sale In Baton Rouge LouisianaMost Viewed Writer in with 150+ answersHomes For Sale Andres Place Bryant Ar The goal of this project was to create a general use simulated environmentMiniature Pomeranian Puppies For Sale Sydney (written in prolog) for the use of experimenting with autonomous logic basedThe original idea came from Dr. Donald Nute. The head of the Artificial Intelligence Center at UGA and the professor for my first AI class

He had the idea of creating an environment used as an example in the book we used (Artificial Intelligence: A Modern Approach, by Russell & Norvig). This environment was a very simple one refered to as "Vacuum World". Nute took this idea and created the environment and had us create the vacuum cleaners to run in it. great fun and worked really well. Thus Dr. Nute and myself set about making a long term project of it. We decided it needed to be more general and easily I made it as modular as possible while trying to keep it extensible for use beyond just vacuum world. To this end I created a set of properties that objects could have and implemented the effects of actions on objects in termsThis is done with the hope of letting environments and objects be described easily in terms of these properties, without having to worry (much) about the code. used properties are: height, weight, energy (eg. food, electricity), score (value for scoring), permanence (how it

reacts to being taken), attack (bonus to attack action), defensive (subtract from attackers attack), heal (damage regained), and add (randomly add this object to the running I also made the agents somewhat more flexible by giving them more actions to perform (assuming that they make sense for that environment) and giving them some basicThe current actions implemented are: push, attack, climb, look, take, eat, sit, move, and drop. current characteristics are: strength, height, stamina(modifies max energy level), speed, charge (current energy level), and damage. There are two versions. The SWI version has no GUI, but the simulator and documentation are pretty much done (the code is fairly well documented as well). The LPA version has a graphical interface, but it was reworked by Dr. Nute and thus has been simplified quite a bit and I don't have the source (use this as a test version on windows, if you like it and want to mess with it more, get the

If you are interested in this type of thing, please download it and let me know what you think. The SWI version: vworld-swi-v0.2.tar.gz The LPA version: lpa-vworld.tgz [This software is public domain] These changes deal primarily with the SWI version Changes from 0.1 to 0.1a: Added a few comments. Fixed one minor bug (if agent failed to decide what to do, the simulator had it do nothing at all. Moved the agent definitions to the startup (wstart.pl) file. Added a README file. Changes from 0.1a to 0.2One for LPA-prolog and one for SWI-prolog. Fixed bug in object property 'attack' so that it is actually figured into the damage done in an attack actionWe've all seen robot vacuum cleaners scuttling around people's homes. These robotic suckers scoot around the floor, picking up dust and dirt and then returning to their electronic nests to recharge. We have reviewed products like the iRobot Roomba 980 that include a scary amount of electronic smarts.

So, how do these marvels of modern cleaning technology navigate their way around your home? There are two parts to the answer: sensors and smarts.Robotic vacuum cleaners don't use cameras to see the world. Instead, they use various types of sensors to detect and measure the worlds around them and their own progress through it, including cliff sensors, bump sensors, wall sensors and optical encoders. Cliff sensors measure the distance between the robot base and the floor, usually by bouncing infra-red light off the floor. If there is a sudden increase in the distance to the floor, that means the robot is getting close to a stair edge or something similar, so it will back off to avoid falling over it (hence the "cliff sensor" name).The name of the bump sensors also gives away what they do: if the robot vacuum bumps into something (like a wall or a chair leg), the impact triggers the sensor. Wall sensors are like cliff sensors, but in a different direction: they tell the robot when it is close to a wall or other object, so it can follow the wall.

Optical encoders are the most important: these sensors on the wheels of the robot tell it how far it has gone. They are called optical encoders because they use a light sensor to detect how many times the wheels have rotated. From this (and any difference between wheels, which indicates a turn), the robot can figure out how far it has traveled. Different models may include additional sensors (such as a dust scanner to see how much dust is being picked up), but those are the basic sensors that all robotic vacuums include.This combination of sensors means that the robot knows a few things about the world around it: how far it has gone, things it has bumped into and things it could fall off from. These are the things that a basic robot vacuum will need to know to navigate the world around it.So, the robot knows a few things about the world as it moves around it. What gives a robotic vacuum the smarts to work out what it has(and has not) cleaned yet? The answer might surprise you: insects.Most modern robot vacuums were born of the work of Rodney Brooks, a roboticist at MIT (and one of the founders of iRobot, makers of the Roomba) who was studying simple animals like insects and flatworms.

He was part of a new wave of artificial intelligence (AI) research that stepped away from complex problems like teaching a computer to play chess to focus on the basics of intelligence. This movement reasoned that an ant isn't smart, but it navigates the world. They realized that, by following a simple set of rules, these simple animals could create complex behaviors. An individual ant doesn't have much brainpower, but it has a simple set of rules that allow it to search for food, return to the nest and guide others. Likewise, a robotic vacuum doesn't need to know the exact dimensions of a room to clean it. Instead, it just needs to know how to react in a few different situations, and it will be able to clean a room. Roboticists call these rules "behaviors," and they are simple things like if you hit a wall, turn away from it.These behaviors are outlined in a patent filing from iRobot from 2002. These behaviors are extremely simple: the "straight" behavior tells the robot to keep going straight until it hits something.

The "bounce" behavior tells it that when it hits something, it should stop, turn to an angle away from the wall and move straight again. The "spiral" behavior tells it to move outward in a spiral, cleaning the floor in expanding circles. The "wall-following" behavior tells it to, well, follow the wall by "bouncing" and going "straight" until the wall is constantly a certain distance away.When you apply them to a robotic vacuum, these rules allow it to navigate a room. The patent outlines a typical sequence of behaviors:A similar set of behaviors determine what to do if the vacuum cleaner gets stuck: it has a variety of moves (moving slowly, rotating, backing up) that it will try to free itself, before it gives up and starts beeping pathetically to be rescued.If you want to see these behaviors in action, the Flickr photo pool of Roomba art is a good place to look. The artists behind these stick LED lights on Roombas in dark rooms and use long-exposure photos to capture the result, creating a trail of light that shows how the vacuum moved around the room.