Robots are called “intelligent” if they succeed in moving in safe interaction with an unstructured environment, while autonomously achieving their specified tasks. This definition implies that a device can only be called a “robot” if it contains a movable mechanism, influenced by sensing, planning, actuation and control components. It does not tell whether a minimum number of these components must be implemented in software, or be changeable by the “consumer” who uses the device; for example, the motion behavior can have been hard-wired into the device by the creator.

Definition of Robotics:-

Robotics can be defined as a system integration, achieving a task by an actuated mechanical device, through an “intelligent” integration of components, many of which it shares with other domains, such as systems and control, computer science, character animation, machine design, computer vision, artificial intelligence, cognitive science, biomechanics, etc.

Components of robotic systems:-

Robotics as an integrated system having interaction with the physical world.

Modelling. The input-output relationships of all control components can (but need not) be derived from information that is stored in a model. This model can have many forms: analytical formulas, empirical look-up tables, fuzzy rules, neural networks, etc.

The other components discussed below can all have models inside. A “System model” can be used to tie multiple components together, but it is clear that not all robots use a System model. The “Sensing model” and “Actuation model” contain the information with which to transform raw physical data into task-dependent information for the controller, and vice versa.

Planning. This is the activity that predicts the outcome of potential actions, and selects the “best” one. Almost by definition, planning can only be done on the basis of some sort of model.

Regulation. This component processes the outputs of the sensing and planning components, to generate an actuation setpoint. Again, this regulation activity could or could not rely on some sort of (system) model.

Scales which are commonly used in robotic systems:-

Mechanical scale. The physical volume of the robot determines to a large extent the limites of what can be done with it. Roughly speaking, a large-scale robot (such as an autonomous container crane or a space shuttle) has different capabilities and control problems than a macro robot (such as an industrial robot arm), a desktop robot (such as those “sumo” robots popular with hobbyists), or milli micro or nano robots.
Spatial scale. There are large differences between robots that act in 1D, 2D, 3D, or 6D (three positions and three orientations).

Time scale. There are large differences between robots that must react within hours, seconds, milliseconds, or microseconds.

Power density scale. A robot must be actuated in order to move, but actuators need space as well as energy, so the ratio between both determines some capabilities of the robot.

System complexity scale. The complexity of a robot system increases with the number of interactions between independent sub-systems, and the control components must adapt to this complexity.

Computational complexity scale. Robot controllers are inevitably running on real-world computing hardware, so they are constrained by the available number of computations, the available communication bandwidth, and the available memory storage.

Research in engineering robotics follows the bottom-up approach: existing and working systems are extended and made more versatile. Research in artificial intelligence robotics is top-down approach : assuming that a set of low-level primitives is available, how could one apply them in order to increase the “intelligence” of a system. The border between both approaches shifts continuously, as more and more “intelligence” is cast into algorithmic, system-theoretic form. For example, the response of a robot to sensor input was considered “intelligent behaviour” in the late seventies and even early eighties.