Robots consist of some sort of mechanical construction. The mechanical aspect of a robot helps it complete tasks in the environment for which it’s designed. For example, the Mars 2020 Rover’s wheels are individually motorized and made of titanium tubing that help it firmly grip the harsh terrain of the red planet.Robots need electrical components that control and power the machinery. Essentially, an electric current — a battery, for example — is needed to power a large majority of robots.

Robots contain at least some level of computer programming. Without a set of code telling it what to do, a robot would just be another piece of simple machinery. Inserting a program into a robot gives it the ability to know when and how to carry out a task.

Dexterous industrial manipulators and industrial vision have roots in advanced robotics work conducted in artificial intelligence (AI) laboratories since the late 1960s. Yet, even more than with AI itself, these accomplishments fall far short of the motivating vision of machines with broad human abilities. Techniques for recognizing and manipulating objects, reliably navigating spaces, and planning actions have worked in some narrow, constrained contexts, but they have failed in more general circumstances.

The first robotics vision programs, pursued into the early 1970s, used statistical formulas to detect linear boundaries in robot camera images and clever geometric reasoning to link these lines into boundaries of probable objects, providing an internal model of their world. Further geometric formulas related object positions to the necessary joint angles needed to allow a robot arm to grasp them, or the steering and drive motions to get a mobile robot around (or to) the object. This approach was tedious to program and frequently failed when unplanned image complexities misled the first steps. An attempt in the late 1970s to overcome these limitations by adding an expert system component for visual analysis mainly made the programs more unwieldy—substituting complex new confusions for simpler failures.

The year 2020 the process will have produced the first broadly competent “universal robots” with lizardlike minds that can be programmed for almost any routine chore. With anticipated increases in computing power, by 2030 second-generation robots with trainable mouselike minds may become possible. Besides application programs, these robots may host a suite of software “conditioning modules” that generate positive- and negative-reinforcement signals in predefined circumstances.

By 2040 computing power should make third-generation robots with monkeylike minds possible. Such robots would learn from mental rehearsals in simulations that would model physical, cultural, and psychological factors. Physical properties would include shape, weight, strength, texture, and appearance of things and knowledge of how to handle them.

Software robotics, also called bots, are computer programs which carry out tasks autonomously. One common use case of software robots is a chatbot. A chatbot is a computer program that simulates conversation both online and over the phone and is often used in customer service scenarios. Chatbots can either be simple services that answer questions with an automated response or more complex digital assistants that learn from user information.

Robots are built to present solutions to a variety of needs and fulfill several different purposes, and therefore, require a variety of specialized components to complete these tasks.

Sensors provide a robot with stimuli in the form of electrical signals that are processed by the controller and allow the robot to interact with the outside world. Common sensors found within robots include video cameras that function as eyes, photoresistors that react to light and microphones that operate like ears. These sensors allow the robot to capture its surroundings and process the most logical conclusion based on the current moment and allows the controller to relay commands to the additional components.