From self-driving lawn mowers for our homes to smart cocktail makers in bars, robots and automation are changing our lives in a myriad of different ways. At the forefront of this revolution we see robotics engineers innovating in every industry imaginable.
If you’re interested in changing the world or just love working with futuristic tech, then a career in robotics engineering may be for you. Read on as we explore what it is that robotics engineers do.
With the rise of machine learning (ML) and associated technologies, the demand for robotics engineers is growing each year. It’s projected that the number of jobs in the field will grow 9% between 2016 and 2026, leading to a shortage of qualified engineers. As a result, the robotics engineer salary is becoming even more competitive in order to attract top talent.
Here’s what you can expect when it comes to the salary for a robotics engineer.
Self-driving cars are no longer just a part of science fiction movies. If you visit San Francisco, odds are you will see a self-driving car from Uber or Cruise roaming the streets — with a supervisor sitting behind the wheel, of course.
Tesla has been selling cars with self-driving capabilities for years. Their Autopilot feature can “steer, accelerate and brake automatically” and even be summoned within a parking lot.
If you shop for a car in 2020, you’ll be able to choose from multiple options that can self-park. In fact, they can probably pass their parallel parking driver’s test better than you can!
Sometime back we highlighted the student story of Kush Singh, a 9 year old programmer who wants to found companies that can create technology. This week we have another young genius to blow your minds. The 11-year-old Self-Driving Car Engineer, Aaron Ma!
To say that he is anything less than the future of technology is an understatement. Aaron is already a graduate from Udacity’s Self Driving Car Engineer Nanodegree program, Deep Reinforcement Learning Nanodegree program, and AI for Trading Nanodegree program as the youngest Udacity Nanodegree graduate. Apart from these, he has successfully graduated from more than 50 certificates from various online learning platforms.
Autonomous technologies are emerging quickly. While the industry is still gearing up to deploy the benefits of autonomous tech, many in the automobile sector have already started experimenting with the advantages of this technology.
Gaurav Pokharkar’s first experience with autonomous vehicles was when he started working with Ford Motor Company as a contractor. He enrolled in Udacity’s Self-Driving Car Nanodegree program when he decided to apply for a full time role within the organization. Since then, he has also enrolled in the Sensor Fusion Nanodegree program. Here’s how the Nanodegree program helped his journey from a contractor role to becoming a full-time Advanced Driver Assistance Engineer (ADAS) Test and Dev Research Engineer at Ford Motor Company.
Every day we come across many inspiring stories of our students succeeding in various fields. Some make us happy, some make us proud, and then there are some that are so remarkable they make us realize the profound impact our students can have on society.
We came across one such story recently of Mateusz Zatylny, a recipient of the Udacity Pytorch Scholarship and the Udacity Facebook Secure and Private AI Scholarship, who is now building an autonomous technology driven wheelchair along with a group of Udacians he met during the Pytorch Scholarship program. Mateusz is a patient of generalized Dystonia, a movement disorder that is not limited to a single part of the body. But that clearly didn’t deter him from achieving great things. He can’t control his wheelchair by himself, so he decided to build an autonomous technology driven wheelchair that could help him and many more to safely maneuver through daily life.
Robots use a surprisingly simple but powerful algorithm to find out where they are on a map, a problem called localization by engineers. The algorithm known as particle filtering looks amazingly cool. In this first article, we attempt to explain the intuition behind particle filters. In part 2 we will elucidate the mathematics needed to build your own particle filters.
Every robot that can move around, whether it is a vacuum cleaning robot like a Roomba or a self-driving car like Carla, has a lifelong problem to contend with. The problem is to find out its whereabouts on a map that every robot carries with itself. But why is finding the whereabouts so challenging? A robot can just ‘look’ at its surroundings and recall which area on the map looks like the current surroundings, not very different from how we humans find where we are in a city or inside the office when woken up from a post-lunch slumber.
The challenge is the utmost precision with which robots have to localize themselves, very unlike humans. If a Roomba thinks it is in front of a door, while it actually is slightly behind a wall, a few centimeters away from where it thinks it is, it may never be able to maneuver its way out from one room to another. A self-driving car, operating under a similar misconception, may scrape another car, veer off the road or climb a curb. The reason this does not happen is because robots are able to beat humans at one of their own games.
Humans try to address the imprecision in their beliefs by filling in missing clues using logic and reasoning but they do end up tripping sometimes. However humans move around rather slowly and have enough time to recover from a bad assumption. So, while we may miss a step on a staircase and stumble, in most cases, we do not tumble down the staircase into the abyss.
For the sake of everyone’s safety robots pack much more precision into their beliefs so that they do not have to trip and recover. How do robots synchronize their beliefs with reality so precisely? Let us find out, starting with something called priors.