How do programs interact with robots

His Demiurge system will select both the optimum hardware and the software modules required to complete each task. It would tap a library of low-level programmes, which it can combine on the fly. Plato used the term in the dialog Timaeus, an exposition of cosmology in which the Demiurge is the agent who takes the pre-existing materials of chaos, arranges them according to the models of eternal forms, and produces all the physical things of the world, including human bodies.

For example, one of his garbage collection robots might resemble a simple vacuum cleaner. Under a project just begun with ERC funding, Birattari and his team are developing a proof of concept system for automatically conceiving and programming robot swarms that can be demonstrated in a lab. For example, we could work with 30 to 50 simple research robots, which can push objects into one corner of the environment in which they operate.

The military is at the forefront of the development of robot swarms: there would be clear benefits to sending drones, rather than soldiers, into conflict zones. Today, military drones are remotely controlled; but in future, autonomous robots could work together to secure an area or clear land mines. And the first civilian applications, such as managing the stock in a warehouse, will be more straightforward — but no less important economically.

Amazon and Carrefour, take note. She received a diploma engineer degree in aeronautical and aerospace engineering from the Technical University of Berlin, and a doctorate in engineering from TUM. Her main research interests include cooperative, distributed and networked control with applications in human-robot interaction, multi-robot systems, and general robotics. Picture yourself infirm and elderly, and in need of some reliable, hands-on help with the basics of daily life. Now picture yourself with a robot helper.

Would you trust it? An ERC-funded research project led by Sandra Hirche, professor of control engineering at the Technical University of Munich, could help build that trust.

Hirche and her team are using artificial intelligence to develop advanced robotic systems that can work alongside humans in a safe and intuitive manner. If she is successful, robots could act as care givers to the incapacitated, support physical rehabilitation, provide mobility and manipulation aids for the elderly, and — in the workplace - collaborate with humans in manufacturing processes.

Hirche is seeking to apply mathematics to this challenge. In conventional robotics, a machine designed to grasp a moving object uses sensors and cameras to continually establish where the object is, and then compute how much its motors need to move to make contact with the object. This feedback loop, which is the essence of what experts call control engineering, is underpinned by a prediction model that estimates how the object will behave while being grasped.

But if a human is involved, the system must also predict his or her behaviour. And ideally, the robot would even adapt to the actual person it is working with. As it observes how the person moves, it would then continually adapt its statistical model. So Hirche and her team are taking advantage of recent developments in machine learning, applying models derived from a year old probability theorem developed by the English Reverend Thomas Bayes. Based on the probability theorem developed by the Reverend Thomas Bayes in the eighteenth century, Bayesian methods are being used to improve machine learning algorithms by enabling them to fill in gaps in data and extract much more information from small datasets.

Bayesian methods can also be used to estimate uncertainty in predictions, which can be very helpful in medicine, for example. When Bayesian methods are applied to deep learning, they can compress models fold, saving time and money. Monitored by psychologists, the studies demonstrated that the new control algorithms work; humans perceive the robots as helpful. The team has also performed experiments where humans and robots have just touched each other, or have moved an object through a virtual maze.

This has applications beyond robotics. The results are so much better than existing techniques. This page requires JavaScript, please enable it in your browser. How to enable JavaScript. What happens when computers program themselves? Artificial intelligence could transform our world. But first, ERC researchers are trying to answer the basic questions — about life and the universe — that AI poses.

Martin Vechev. ETH Zurich. Aude Billard. Swiss researchers visit a watch-making school, to teach robots to think like a craftsman Could robots put Swiss watchmakers out of business? Josef Urban. Czech Technical University. Sure, computers can do sums. But can they think like a mathematician? But if a sensor on, say, the right side picks up an obstacle, it will contribute a smaller vector to the sum, and the result will be a reference vector that is shifted towards the left.

The robot bounces around aimlessly, but it never collides with an obstacle, and even manages to navigate some very tight spaces:. Both perform their function admirably, but in order to successfully reach the goal in an environment full of obstacles, we need to combine them. The solution we will develop lies in a class of machines that has the supremely cool-sounding designation of hybrid automata.

A hybrid automaton is programmed with several different behaviors, or modes, as well as a supervising state machine. The supervising state machine switches from one mode to another in discrete times when goals are achieved or the environment suddenly changed too much , while each behavior uses sensors and wheels to react continuously to environment changes.

The solution was called hybrid because it evolves both in a discrete and continuous fashion. Equipped with our two handy behaviors, a simple logic suggests itself: When there is no obstacle detected, use the go-to-goal behavior. When an obstacle is detected, switch to the avoid-obstacles behavior until the obstacle is no longer detected.

As it turns out, however, this logic will produce a lot of problems. What this system will tend to do when it encounters an obstacle is to turn away from it, then as soon as it has moved away from it, turn right back around and run into it again. The result is an endless loop of rapid switching that renders the robot useless.

In the worst case, the robot may switch between behaviors with every iteration of the control loop—a state known as a Zeno condition. There are multiple solutions to this problem, and readers that are looking for deeper knowledge should check, for example, the DAMN software architecture. What we need for our simple simulated robot is an easier solution: One more behavior specialized with the task of getting around an obstacle and reaching the other side.

Then, simply set our reference vector to be parallel to this surface. Keep following this wall until A the obstacle is no longer between us and the goal, and B we are closer to the goal than we were when we started. Then we can be certain we have navigated the obstacle properly. To make up our minds, we select the direction that will move us closer to the goal immediately. To figure out which way that is, we need to know the reference vectors of the go-to-goal behavior and the avoid-obstacle behavior, as well as both of the possible follow-wall reference vectors.

Here is an illustration of how the final decision is made in this case, the robot will choose to go left :. Determining the follow-wall reference vectors turns out to be a bit more involved than either the avoid-obstacle or go-to-goal reference vectors. The final control design uses the follow-wall behavior for almost all encounters with obstacles.

However, if the robot finds itself in a tight spot, dangerously close to a collision, it will switch to pure avoid-obstacles mode until it is a safer distance away, and then return to follow-wall. Once obstacles have been successfully negotiated, the robot switches to go-to-goal. An additional feature of the state machine that you can try to implement is a way to avoid circular obstacles by switching to go-to-goal as soon as possible instead of following the obstacle border until the end which does not exist for circular objects!

The control scheme that comes with Sobot Rimulator is very finely tuned. It took many hours of tweaking one little variable here, and another equation there, to get it to work in a way I was satisfied with.

Robotics programming often involves a great deal of plain old trial-and-error. I encourage you to play with the control variables in Sobot Rimulator and observe and attempt to interpret the results. Sometimes it drives itself directly into tight corners and collides. Sometimes it just oscillates back and forth endlessly on the wrong side of an obstacle. Occasionally it is legitimately imprisoned with no possible path to the goal. Many of the failure cases it encounters could be overcome by adding some more advanced software to the mix.

Robots are already doing so much for us, and they are only going to be doing more in the future. While even basic robotics programming is a tough field of study requiring great patience, it is also a fascinating and immensely rewarding one.

In this tutorial, we learned how to develop reactive control software for a robot using the high-level programming language Python. But there are many more advanced concepts that can be learned and tested quickly with a Python robot framework similar to the one we prototyped here.

I hope you will consider getting involved in the shaping of things to come! Acknowledgement: I would like to thank Dr. Magnus Egerstedt and Jean-Pierre de la Croix of the Georgia Institute of Technology for teaching me all this stuff, and for their enthusiasm for my work on Sobot Rimulator.

A robot is a machine with sensors and mechanical components connected to and controlled by electronic boards or CPUs. They process information and apply changes to the physical world.

Robots are mostly autonomous and replace or help humans in everything from daily routines to very dangerous tasks. Robots are used in factories and farms to do heavy or repetitive tasks. They are used to explore planets and oceans, clean houses, and help elderly people. Researchers and engineers are also trying to use robots in disaster situations, medical analysis, and surgery. Self-driving cars are also robots! The creation of a robot requires multiple steps: the mechanical layout of the parts, the design of the sensors and drivers, and the development of the robot's software.

Usually, the raw body is built in factories and the software is developed and tested on the first batch of working prototypes.

There are three steps involved. First, you get motors and sensors running using off-the-shelf drivers. Then you develop basic building blocks so that you can move the robot and read its sensors. The Raspberry Pi 3, Model B. The Raspberry Pi is like a normal PC but much smaller. This Raspberry Pi 3 has a 1. The Arduino and Raspberry Pi are both useful for robotics projects but have some important differences.

An Arduino is a microcontroller, which is like a simple computer but which runs and loops a single program that you have written on a PC. This program is compiled and downloaded to the microcontroller as machine code. The Arduino is well suited to low-level robot control and has features like analogue-to-digital conversion for connecting analogue sensors.

Raspberry Pi. A Raspberry Pi RPi is just like a normal PC and so is more versatile than an Arduino but lacks features like analogue-to-digital conversion. The RPi runs a Linux operating system usually Raspian. You can connect a keyboard, mouse and monitor to a RPi, along with peripherals like a camera — very useful for robotics.

What programming language would you like to learn, and are you tempted have a go with an Arduino to learn C, or a Raspberry Pi to learn Python or both?

Want to keep learning? This content is taken from The University of Sheffield online course. See other articles from this course. This article is from the online course:. News categories. Other top stories on FutureLearn. Category: General. In hospital teams for emergency resuscitation of patients, team interaction and communication are crucial.

Between people and robots there are even more challenges — like making sure they share understandings of how words are used or what appropriate responses are to questions. Human-dog teams do fine without the use of natural language.

Navy SEALs can work together at highly effective levels without uttering a word. Bees communicate location of resources with a dance. Communication does not have to involve words; it could include sound signals and visual cues. If a robot was tending the patient when their heart stopped, it could indicate what happened on a monitor that all resuscitation team members could see.

Interpersonal trust is important in human teams. The best robot teammates will be trustworthy and reliable — and any breaches in reliability need to be explained. But even with an explanation, technology that is chronically unreliable is likely to be rejected by human teammates.