Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Mobile robotic systems are becoming increasingly popular. These systems are used in various indoor applications, raging from warehousing and manufacturing to test benches for assessment of advanced control strategies, such as artificial intelligence (AI)-based control solutions, just to name a few. Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF), and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator to expedite the learning phase and increase robustess to the simulation-to-reality gap. The paper also presents the formulation of a vehicle model along with an effective yet simple procedure for identifying tis paramters. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm. Experimental results confirm the ability of the FEKF to improve the estimate of the mobile robot's position. Furthermore, the effectiveness of the DRL path tracking strateguy is experimentally tested along manoeuvres not considered during training, showing also the ability of the AI-based solution to outpeform model-based control strategies and the demonstrator. The comparison with benchmraking controllers is quantitavely evalueted through a set of key performance indicators.

As Tesla faces a federal investigation and lawsuits over fatal accidents involving its Autopilot system, shaking public confidence in robotic cars, could a pared-down approach like the one described -- variously called "partial autonomy" or "driver assistance" systems -- be the more realistic future of hands-free driving? This type of system, more like a no-nonsense chaperone than one you would find in a fully robotic car, is a necessary component for top scores from the Insurance Institute for Highway Safety's forthcoming ratings of partial-autonomous tech; high ratings from the independent nonprofit are prized. And though General Motors is taking the lead with their Super Cruise system, they not alone; Ford, BMW and Mercedes-Benz are making similar attempts. Super Cruise combines minutely detailed, 3-D laser-scanned roadway maps with cameras, radar and onboard GPS. By the end of this year, the company intends to expand the system's network to two-way highways for the first time and double its total operational domain to 400,000 miles.

Zoom link: Posted in Slack. Please join our Slack workspace using the link below. Robotic caregivers could increase the independence of people with disabilities, improve quality of life, and help address growing societal needs, such as shortages of healthcare workers, aging populations that require care, and high healthcare costs. Using robotic technology to assist care-recipients in their daily lives may entail physically interacting with them, adapting to their preferences, and perceiving the environment for intelligent and safe assistance. These tasks can benefit from using data-driven machine learning techniques.

The vision-guided system changes the cartoning game with the integration of JLS TRAK and handles a variety of packaging with on-the-fly changeover. The high-speed, flexible Peregrine utilizes a proprietary positive carton transport (PCT) system to retain full control of the carton from forming through loading, without ever letting go.PACK EXPO Connects – November 9-13. Now more than ever, packaging and processing professionals need solutions for a rapidly changing world, and the power of the PACK EXPO brand delivers the decision makers you need to reach. Attendee registration is open now. The flexible carton loading system minimizes changeover while meeting production needs for small batches.

Moovit, an 8-year-old company based in Israel, makes an app that compiles data from public transit systems, ride-hailing services and other resources to help its 800 million users plan the best ways to get around. Intel plans to combine Moovit with Mobileye, a self-driving car specialist that Intel bought for about $15 billion in 2017.

If you had your heart set on an Apple iCar to go with your iPhone and Apple Watch, take a deep breath and prepare to be disappointed. Forbes reported Wednesday the company apparently is more interested in developing apps for self-driving cars than the cars themselves. The California Department of Motor Vehicles granted permits for Apple to test three 2015 Lexus RX 450h hybrid SUVs. Industry analysts say the fact that only three vehicles are involved suggests Apple is focusing on its CarPlay connectivity and infotainment platform, Forbes said. CarPlay already has a significant portion of the vehicle market.

After catching the world and the auto industry by surprise with its progress with self-driving cars, Google has begun the latest, most difficult phase of its project – making the vehicles smart enough to handle the chaos of city streets. But while the company describes its work with its typical tight-lipped optimism, academic experts in robotics are cautious about the prospects of fully autonomous vehicles. They estimate it will be decades until they can perform as well as human drivers in all situations – if they ever do at all. Google's cars make extensive use of detailed maps that describe not only roads and restrictions such as speed limits, but the 3-D location of stop lights and curbstones to within inches. The company is now working to make its vehicles capable of seeing and understanding the kind of unexpected obstacles that don't appear on those maps and are particularly common in urban areas, said Chris Urmson, the director of the project, last week.

In an airplane hanger on MIT's campus in Cambridge last week, a team of engineering students and researchers put the finishing touches on Talos, a Land Rover that drives itself. Talos is MIT's entry in the Defense Advanced Research Project Agency's (DARPA) robotic car race, which will take place on November 3, in Victorville, CA. Known as the Urban Challenge, the race will test the ability of robotic cars from 35 different teams to obey traffic laws and drive safely in a city-like environment without human assistance. The vehicles will need to find their way to a preprogrammed destination while paying attention to lane markers, other cars, and unexpected obstacles, such as potholes in the road. The Urban Challenge is a follow-up to DARPA's Grand Challenge race, held in 2004 and 2005, in which cars navigated an empty desert road.

A Google autonomous car was pulled over Thursday while driving near the company's headquarters in Mountain View, Calif. An officer from the Mountain View Police Department (MVPD) observed traffic slowing down on a public road and pulled the car over for driving 11 m.p.h. The Google self-driving vehicle, which is only allowed on public roads with speed limits of 35 m.p.h. or slower, was operating on a legal road, but was obstructing traffic in violation of section 22400 of the California Vehicle Code. The officer did not have a driver to ticket, but spoke with the car's operator, issuing a warning about maintaining safe and reasonable speeds. In their statement, the MVPD wrote, "The officer stopped the car and made contact with the operators to learn more about how the car was choosing speeds along certain roadways and to educate the operators about impeding traffic." Google was quick to post on its Google page, defending the cop encounter.