Why Robots Took So Long to Use Unix:
In 1987, only one robot ran Unix, while millions use it today. Trends suggest that most robots will run Unix in the future. Holdouts may exist due to specific reasons.

Definitions of Robots:
Factory robots: no people nearby. Robot toys: Furby, Pleo, and failed business models. Robot vacuum cleaners: iRobot has a large market share. Military robots: iRobot had robots in Afghanistan and Iraq. Nuclear power plant inspection robots: a failed business model for iRobot.

Cars as Robots:
Cars are becoming robots with sensing and actuation. The person is currently the primary actuator through beeps and instructions.

Early Industrial Robots:
Unimate was the first industrial robot, introduced in a GM factory in Trenton, New Jersey, in 1959. Joe Engelberger, the business mind behind Unimate, went international in 1965, licensing and building robots with companies like Marcus and Kawasaki Heavy Industries. Early robots were analog, not digital, limiting their flexibility and control systems.

The Unimate 1900:
The Unimate 1900 series was showcased on the Johnny Carson show in 1965, conducting an orchestra and pouring a beer. Despite the showmanship, these early robots lacked computer control and real-time guarantees.

The Need for Affordable Computing in Robotics:
The PDP-7 computer, costing $72,000 in 1969, was too expensive for mass production in robotics. Robots needed something cheaper, mass-producible, and capable of over 100,000 floating-point operations per second.

Forward Kinematics and Inverse Kinematics:
Forward kinematics calculates the position of the robot’s end point given the joint angles. Inverse kinematics calculates the joint angles needed to move the robot’s end point to a desired position. Trajectory control ensures that the robot moves along a smooth path between points. Applying desired forces allows the robot to interact with its environment and manipulate objects.

The Complexity of Robot Control:
Even a simple robot with three degrees of freedom requires complex mathematics to control its movement. Six degrees of freedom, common in industrial robots, add even more complexity.

Forward Kinematics:
Forward kinematics involves calculating the position of a robot’s end effector based on the joint angles. It is a relatively simple computation that involves a series of multiplications.

Inverse Kinematics:
Inverse kinematics is the process of determining the joint angles required to move the robot’s end effector to a desired position. It is a more complex computation than forward kinematics and involves calculus and matrix inversion.

Jacobian Matrix:
The Jacobian matrix is used to calculate the relationship between the joint velocities and the end effector velocity. It is a function of the joint angles and is used to control the velocity of the robot’s joints.

Dynamics and Torque:
Dynamics and torque calculations are necessary to control the forces applied by the robot and to ensure its safe interaction with the environment. These calculations are even more complex than inverse kinematics and involve a large number of equations.

Computational Requirements:
The computations required for robotic movement are extensive and must be performed at a high frequency (typically 60 times per second). For many years, computers were not powerful enough to handle these computations in real time.

Historical Context:
In the early days of robotics, it took an entire semester to teach the kinematics of a simple six-degree-of-freedom arm. In 1992, researchers were still counting the number of additions and multiplications required for these computations.

Operating Systems in Robotics:
Early robot arms lacked computational power and operating systems, focusing solely on arithmetic computations. Commercial robot arms used embedded processes with fixed processing loops for real-time control.

Megalos Operating System:
In 1986, Bell Labs researchers Bob Gaglianello and Howard Katzeff developed Meglos, a multiprocessor operating system for real-time applications. Meglos ran on a DEC VAX with Motorola 68000 processors, providing low-latency communication between them. It featured a Unix extended kernel, with the VAX controlling satellites and handling file IO, while the 68000s managed real-time processes and communication.

Limitations of Megalos:
Despite its capabilities, Megalos lacked support for security, failure handling, and availability, making it unsuitable for commercial use. The 68000 processors, though 32-bit machines, had a 16-bit memory channel and slow division instructions, limiting computation speed.

Russell Anderson’s Ping Pong Playing Robot:
Russell Anderson, a technical staff member at Holmdel, built a ping pong playing robot using Megalos. The robot employed a custom chip with 10,000 transistors to compute the center of mass of a white ping pong ball in real time. It utilized multiple vision boards with six custom chips each and compensated for the rolling shutter camera’s time-smeared images. The robot successfully played ping pong in 1987, demonstrating advanced perception and control capabilities.

Importance of “Most Once” Semantics:
Megalos introduced the concept of “most once” semantics, which allowed real-time processes to operate independently and receive advice occasionally. This approach is crucial for modern robot architectures, enabling efficient and reliable control.

Limitations of Early Robotics:
In the early days of robotics, researchers like Rodney Brooks at MIT faced significant resource constraints compared to well-funded institutions like Bell Labs. They had to work with limited hardware, such as 8-bit processors with minimal RAM, and had to build their own components, like token rings out of serial ports.

Genghis: A Milestone in Rough Terrain Navigation:
Genghis, a robot developed by Rodney Brooks, represented a significant achievement in autonomous navigation. It could traverse naturally occurring rough terrain using its whiskers as sensors and adjust its gait to handle challenging obstacles. Despite its limited computational power, Genghis demonstrated the potential of simple algorithms and sensor-based control for effective robot navigation.

Advances in Computation and Kismet:
During the 1990s, computational resources became more powerful, enabling the development of more sophisticated robots. Kismet, a robot built by Cynthia Brazil, utilized multiple 100-megahertz PCs and 6807 microcontrollers running Lisp for real-time processing. Kismet could engage in rudimentary interactions with humans, such as responding to prosody in their voices and generating speech-like sounds with appropriate intonation.

The Challenge of Commercialization:
While these research robots demonstrated impressive capabilities, commercializing them proved challenging. Factors such as the high cost of production and the need for specialized expertise limited their widespread adoption.

COG: Learning Dynamics and Adapting to Changing Conditions:
COG, another robot developed in Rodney Brooks’ lab, showcased the ability to learn dynamics and adapt to changing conditions. It could sense and respond to forces applied to its arm, adjusting its movements accordingly. COG demonstrated the potential for robots to interact with their environment in a more fluid and natural manner.

Frameworks for Robotics:
As robotics research progressed, frameworks like YARP emerged to provide a structured approach to robot development. These frameworks offered tools and methodologies for building and controlling robots, making the process more accessible and efficient.

The Early Days of Robot Platforms:
The first robot platforms were built on real-time operating systems (RTOS). These systems were designed for embedded systems, such as toys and consumer robots, where low price was the driving factor.

The Rise of Unix-Based Robots:
As computation became cheaper, robots began to incorporate more powerful processors. This allowed for the use of Unix-based operating systems, which provided a more flexible and powerful environment for developing robot applications.

The Role of ROS:
The Robot Operating System (ROS) is a popular framework for developing robot applications. It is a set of software libraries and tools that helps developers build robot applications from drivers, powerful developer tools, etc. ROS is open source and has become the de facto standard for research labs and is starting to be used in real-world applications.

Other Robot Frameworks:
There are many other robot frameworks available, such as YARP, Mobility, and Player. These frameworks provide different features and functionalities, and some are more specialized than others.

The Use of Robots in Real-World Applications:
Robots have been used in various real-world applications, such as military operations, nuclear power plant inspection, and disaster response. These applications require robots that are reliable, robust, and capable of performing complex tasks.

The Future of Robot Platforms:
The future of robot platforms is bright. As computation continues to become cheaper and more powerful, robots will become even more capable and versatile. Robots will play an increasingly important role in our lives, helping us with tasks ranging from household chores to space exploration.

ROS Penetration in Autonomous Driving and Industrial Robots:
ROS is gaining traction in autonomous driving systems due to its origins in universities and its penetration into the industrial robot market. ROS is also making inroads into the high-end consumer market.

Unix Integration and Commercial Failure:
Universal Robots, a Danish company owned by Teradyne, uses ROS running on Unix for its industrial robots. Rodney Brooks’ company, which later failed, also ran ROS on Ubuntu.

Rethink Robotics’ Marketing Video:
Rethink Robotics created a marketing video showcasing the automatic construction of behavior trees using teach by demonstration. The video demonstrated the control of various robots, including KUKA robots, using ROS running on Unix.

Modern User Interface and Force Sensing:
ROS offers a modern user interface with Node.js and JavaScript. ROS enables real-time force sensing, allowing robots to perceive forces exerted on their joints.

Safe Interaction with Children:
ROS-based robots are safe for interaction with children, as demonstrated in a video where children were able to program and operate the robots without prior instruction.

Real-time Guarantees in ROS:
Despite Ubuntu not providing real-time guarantees, ROS achieves real-time performance by running on a separate computer with a real-time operating system.

Multi-Core Use:
Multi-core processors are used in robots to separate real-time and non-real-time tasks. Certain threads are locked down on specific cores for real-time tasks, while other tasks run on other cores. This approach provides real-time comfort, not a guarantee, but it is generally sufficient for most applications.

Real-Time Control:
Position and force control loops run at high frequencies (kilohertz or even 30 kilohertz) on embedded processes near the motors. These loops handle tasks such as motor commutation and current control. This separation of real-time and non-real-time tasks allows for efficient and reliable control of the robot’s movements.

Unix Operating Systems in Robotics:
Unix-based operating systems are becoming increasingly popular in robots, especially high-end models. Linux is now used in many robots, including the higher-end Roombas, self-driving cars, and some drones. The increased computational power of multi-core processors enables the use of Unix-based operating systems in robots. Unix provides a stable and reliable platform for running complex robotic applications.

Exceptions:
Drones, due to their mass and power requirements, often use fully embedded flight software stacks instead of Unix. Some applications, particularly those with low cost, low power, or ultra-high-performance requirements, may not be suitable for Unix-based operating systems.

Conclusion:
The use of multi-core processors, real-time control loops, and Unix-based operating systems is shaping the future of robotics. These technologies enable the development of more capable, reliable, and versatile robots for a wide range of applications.