AI News, Difference between revisions of "Robotics and the World"
- On 30. september 2018
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Difference between revisions of "Robotics and the World"
Given that in the next two decades robots will be capable of replacing humans in most manufacturing and service jobs, economic development will be primarily determined by the advancement of robotics.
As the ultimate goal of industrial robotics has been the development of sophisticated production machines with the hope to reduce costs in manufacturing areas like material handling, welding, spray-painting and assembly, tremendous efforts have been undertaken by the international robotics community to design user-friendly and at the same time powerful methods.
The evolution reaches from early control concepts on the hardware level via point-to-point and simple motion level languages to motion-oriented structured robot programming languages.
The robot programming languages can be classified according to the robot reference model, the type of control structure used for data, type of motion specification, the sensors, the interfaces to external machines, and the peripherals used.
The focus is not on low-level coding issues, but on high level concepts about the special situations robots will encounter and ways to address these peculiarities.
Sensor Unreliability - Sensors will provide noisy data (data that is sometimes accurate, sometimes not) or data that is simply incorrect (touch sensor fails to be triggered).
Design a simple tracker that follows the beam of a flashlight, or use a light sensor to help your robot to avoid getting stuck under furniture by making it steer away from shadows.
While dealing with the kinematics used in the robots we deal each parts of the robot by assigning a frame of reference to it and hence a robot with many parts may have many individual frames assigned to each movable parts.
Each frames are named systematically with numbers, for example the immovable base part of the manipulator is numbered 0, and the first link joined to the base is numbered 1, and the next link 2 and similarly till n for the last nth link.
Our contribution is a framework to consider shape and kinematics together in a exact manner, in the obstacle avoidance process, by abstracting these constraints from the avoidance method usage.
The main idea is to construct (centered on the robot at any time) the two-dimensional manifold of the configuration space that is defined by elementary circular paths.
Therefore, the 3-dimensional obstacle avoidance problem with kinematic constraints is transformed into a simple obstacle avoidance problem for a point moving in a 2-dimensional space without any kinematic restriction (the usual approximation in obstacle avoidance).
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.
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.
avoiding dangerous situations such as collisions and staying within safe operating conditions (temperature, radiation, exposure to weather, etc.) come first, but if any tasks are to be performed that relate to specific places in the robot environment, navigation is a must.
In the following, we will present an overview of the skill of navigation and try to identify the basic blocks of a robot navigation system, types of navigation systems, and closer look at its related building components.
One of the most fundamental tasks that vision is very useful for is the recognition of objects (be they machine parts, light bulbs, etc) Evolution Robotics introduced a significant milestone in the near-realtime recognition of objects based on various points.
The CMUCam (initially created at Carnegie Mellon) is by far the most popular vision camera that can track an object based on its color and even move the camera if it is mounted on servos (small motors) to track the object.
In the first phase based on the primitives (curved edges, corners etc.) and the explicit specification of the content of the image given by the user a sequence of operators will be generated and all their free parameters will be computed adaptivelly.
Using the hierachical object model facilitates a rapid interpretation of the result obtained from the previous image processing for the subsequent object recognation.
Major AI textbooks define the field as 'the study and design of intelligent agents,' where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success.
The field was founded on the claim that a central property of human beings, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.
This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity.
Artificial intelligence has been the subject of breathtaking optimism, has suffered stunning setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.
- On 25. oktober 2021
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