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New giant drones are strong enough to perform jobs such as cleaning wind turbines, fighting fires and even carrying people to safety. Some of the dirtiest, most dangerous jobs are being taken over by artificial intelligence, robots, and drones. Machines are slowly becoming man’s best friend, helping us do tough, risky, or menial tasks. Future drones will do more than just peruse the skies, surveying and filming. New innovations in drone technology show their potential to take on much larger tasks that benefit society in new ways. New giant drones are now capable of cleaning wind turbines, fighting fires, and even carrying people to safety. An innovative startup, Aeron, has built a giant drone equipped with 28 motors and 16 batteries. The stout prototypes can lift up to 400 pounds, potentially rescuing people from burning buildings. In a series of videos, the founders demonstrate how the new drones can manoeuvre with hoses to clean and de-ice wind turbines. These large quadcopters can manoeuvre alongside tall buildings, clean the windows, or put out a potential fire.The ambitious startup, backed by Y Combinator, is already getting orders from around the world to help clean and de-ice wind turbines. The greatest challenge for these new drones is sustaining power. Relying on battery only, these drones can only carry a load for about twelve minutes. For now, the drones are better off tethered and connected to a power source from the ground. As the capability of drones expands, the implications for abuse become more real, too. A drone that can rescue people from fires can also identify, locate, and displace a person for various reasons. Large drones of this nature could be used to protect property, locating and removing threats from private areas. Who will make the rules that govern how drones can and cannot be used? A large drone that fights fires can also fight insurrection, threaten protestors, or spray down mobs. In the eyes of authority, this could be seen as a good use for drones, to curb violence; however, the power could readily be abused. Government forces could use drones in an authoritarian, intimidating manner, threatening peaceful assembly, free speech, and democracy. Drones equipped with tear gas could help riot police disperse their opposition. Large drones could easily be used as a means of force to control others. (Related: Drone makers looking to expand into civilian law enforcement market as a replacement for police helicopters.) A giant drone that can operate hoses to clean wind turbines also has the capability to operate hoses to fumigate from overhead. If a city council declared that a vector-borne disease was threatening their community, they could deploy these large drones overhead to fumigate mosquitoes and ticks in certain areas. The residents of the city will have no control over the operation or the number of nervous system toxins that are being sprayed into the air. That same drone could be used to spray disinfectants over an area that has been declared an outbreak zone. Health officials, paying no mind to the consequences of spraying people with biological agents and other chemicals, could experiment with airborne flu vaccines to combat a declared flu outbreak. As drone capabilities expand, it won’t be long before authorities begin using the technology to their advantage. It will be much easier for authorities to carry out force if they can hide behind the technology. Content gathered by BTM robotics training center, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP nagar, robotics training centers in Bannerghatta road, robotics training centers in JP nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore.
Using your body to control a drone is more effective than a joystick. If you've ever been chastised for throwing your entire body around during gaming (because physically leaning into track corners definitely helps somehow), here's a bit of science-backed vindication. Researchers in Switzerland have discovered that using your torso to control a drone is far more effective than using a joystick. The team from EPFL monitored the body movements and muscular activity of 17 people, each with 19 markers placed all over their upper bodies. The participants then followed the actions of a virtual drone through simulated landscapes, via virtual reality goggles. By observing motion patterns, the scientists found that only four markers located on the torso were needed to pilot a drone through an obstacle course and that the method outperformed joystick control in both precision and reliability. The study's lead author, Jenifer Miehlbradt of EPFL's Translational Neuroengineering Laboratory, said: "Using your torso really gives you the feeling that you are actually flying. Joysticks, on the other hand, are of simple design but mastering their use to precisely control distant objects can be challenging." The proof-of-concept system still depends on body markers and external motion detectors to work, so the team's next challenge will be making the tech wearable and completely independent. However, the range of applications for it is enormous. Being able to virtually fly while your head, limbs, hand and feet are free to perform other tasks could be a major development for gaming, drone control or even the planes of the future. Content gathered by BTM robotics training centre, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP Nagar, robotics training centres in Bannerghatta road, robotics training centres in JP Nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore,
Food delivery drones take flight in China. You don't have to wait for food delivery drones... if you live in the right part of China. Alibaba's online meal giant Ele.me has been cleared to use drones for delivering orders in Shanghai's Jinshan Industrial Park. The initiative won't deliver directly to your abode, but it will save you a lot of travel time: there are 17 routes, each of with two fixed drop-off points. Your food should arrive within 20 minutes, which isn't always possible with conventional cars slogging through traffic. Despite the automation, Ele.me believes this could be better for drivers. Humans would only need to cover about 15 percent of its routes and should lower operating costs in the process. Existing couriers could make up to five times more income, it claimed. This approach wouldn't likely work in primarily residential areas, so you could easily argue that Ele.me is taking a shortcut to put drones into service. At the same time, this underscores a few of the advantages Chinese companies have when introducing commercial delivery drones. The US is only just considering looser rules that would enable practical drone couriers, while a Made in China 2025 campaign is providing grants, investments and loans to help technological innovation in fields like this. The edge might not last for long, but it's hard not to look on with some envy as Shanghai residents receive their grub from above. Content gathered by BTM robotics training centre, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP Nagar, robotics training centres in Bannerghatta road, robotics training centres in JP Nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore.
'Blind' Cheetah 3 robot can climb stairs littered with obstacles. MIT's Cheetah 3 robot can now leap and gallop across rough terrain, climb a staircase littered with debris, and quickly recover its balance when suddenly yanked or shoved, all while essentially blind. The 90-pound mechanical beast -- about the size of a full-grown Labrador -- is intentionally designed to do all this without relying on cameras or any external environmental sensors. Instead, it nimbly "feels" its way through its surroundings in a way that engineers describe as "blind locomotion, " much like making one's way across a pitch-black room. "There are many unexpected behaviours the robot should be able to handle without relying too much on vision, " says the robot's designer, Sangbae Kim, associate professor of mechanical engineering at MIT. "Vision can be noisy, slightly inaccurate, and sometimes not available, and if you rely too much on vision, your robot has to be very accurate in position and eventually will be slow. So we want the robot to rely more on tactile information. That way, it can handle unexpected obstacles while moving fast." Researchers will present the robot's vision-free capabilities in October at the International Conference on Intelligent Robots, in Madrid. In addition to blind locomotion, the team will demonstrate the robot's improved hardware, including an expanded range of motion compared to its predecessor Cheetah 2, that allows the robot to stretch backwards and forwards, and twist from side to side, much like a cat limbering up to pounce. Within the next few years, Kim envisions the robot carrying out tasks that would otherwise be too dangerous or inaccessible for humans to take on. "Cheetah 3 is designed to do versatile tasks such as power plant inspection, which involves various terrain conditions including stairs, curbs, and obstacles on the ground, " Kim says. "I think there are countless occasions where we [would] want to send robots to do simple tasks instead of humans. Dangerous, dirty, and difficult work can be done much more safely through remotely controlled robots." Making a commitment The Cheetah 3 can blindly make its way up staircases and through unstructured terrain, and can quickly recover its balance in the face of unexpected forces, thanks to two new algorithms developed by Kim's team: a contact detection algorithm, and a model-predictive control algorithm. The contact detection algorithm helps the robot determine the best time for a given leg to switch from swinging in the air to stepping on the ground. For example, if the robot steps on a light twig versus a hard, heavy rock, how it reacts -- and whether it continues to carry through with a step, or pulls back and swings its leg instead -- can make or break its balance. "When it comes to switching from the air to the ground, the switching has to be very well-done, " Kim says. "This algorithm is really about, 'When is a safe time to commit my footstep?'" The contact detection algorithm helps the robot determine the best time to transition a leg between swing and step, by constantly calculating for each leg three probabilities: the probability of a leg making contact with the ground, the probability of the force generated once the leg hits the ground, and the probability that the leg will be in midswing. The algorithm calculates these probabilities based on data from gyroscopes, accelerometers, and joint positions of the legs, which record the leg's angle and height with respect to the ground. If, for example, the robot unexpectedly steps on a wooden block, its body will suddenly tilt, shifting the angle and height of the robot. That data will immediately feed into calculating the three probabilities for each leg, which the algorithm will combine to estimate whether each leg should commit to pushing down on the ground, or lift up and swing away in order to keep its balance -- all while the robot is virtually blind. "If humans close our eyes and make a step, we have a mental model for where the ground might be, and can prepare for it. But we also rely on the feel of touch of the ground, " Kim says. "We are sort of doing the same thing by combining multiple [sources of] information to determine the transition time." The researchers tested the algorithm in experiments with the Cheetah 3 trotting on a laboratory treadmill and climbing on a staircase. Both surfaces were littered with random objects such as wooden blocks and rolls of tape. "It doesn't know the height of each step and doesn't know there are obstacles on the stairs, but it just ploughs through without losing its balance, " Kim says. "Without that algorithm, the robot was very unstable and fell easily." Future force The robot's blind locomotion was also partly due to the model-predictive control algorithm, which predicts how much force a given leg should apply once it has committed to a step. "The contact detection algorithm will tell you, 'this is the time to apply forces on the ground, '" Kim says. "But once you're on the ground, now you need to calculate what kind of forces to apply so you can move the body in the right way." The model-predictive control algorithm calculates the multiplicative positions of the robot's body and legs a half-second into the future if a certain force is applied by any given leg as it makes contact with the ground. "Say someone kicks the robot sideways, " Kim says. "When the foot is already on the ground, the algorithm decides, 'How should I specify the forces on the foot? Because I have an undesirable velocity on the left, so I want to apply a force in the opposite direction to kill that velocity. If I apply 100 newtons’s in this opposite direction, what will happen a half second later?" The algorithm is designed to make these calculations for each leg every 50 milliseconds, or 20 times per second. In experiments, researchers introduced unexpected forces by kicking and shoving the robot as it trotted on a treadmill, and yanking it by the leash as it climbed up an obstacle-laden staircase. They found that the model-predictive algorithm enabled the robot to quickly produce counter-forces to regain its balance and keep moving forward, without tipping too far in the opposite direction. "It's thanks to that predictive control that can apply the right forces on the ground, combined with this contact transition algorithm that makes each contact very quick and secure, " Kim says. The team had already added cameras to the robot to give it visual feedback of its surroundings. This will help in mapping the general environment and will give the robot a visual heads-up on larger obstacles such as doors and walls. But for now, the team is working to further improve the robot's blind locomotion "We want a very good controller without vision first, " Kim says. "And when we do add vision, even if it might give you the wrong information, the leg should be able to handle (obstacles). Because what if it steps on something that a camera can't see? What will it do? That's where blind locomotion can help. We don't want to trust our vision too much." This research was supported, in part, by Naver, Toyota Research Institute, Foxconn, and Air Force Office of Scientific Research. 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Experimental drone uses AI to spot violence in crowds. Whether or not it works well in practice is another story. Drone-based surveillance still makes many people uncomfortable, but that isn't stopping research into more effective airborne watchdogs. Scientists have developed an experimental drone system that uses AI to detect violent actions in crowds. The team trained their machine learning algorithm to recognize a handful of typical violent motions (punching, kicking, shooting and stabbing) and flag them when they appear in a drone's camera view. The technology could theoretically detect a brawl that on-the-ground officers might miss, or pinpoint the source of a gunshot. As The Verge warned, the technology definitely isn't ready for real-world use. The researchers used volunteers in relatively ideal conditions (open ground, generous spacing and dramatic movements). The AI is 94 percent effective at its best, but that drops down to an unacceptable 79 percent when there are ten people in the scene. As-is, this system might struggle to find an assailant on a jam-packed street -- what if it mistakes an innocent gesture for an attack? The creators expect to fly their drone system over two festivals in India as a test, but it's not something you'd want to rely on just yet. There's a larger problem surrounding the ethical implications. There are questions about abuses of power and reliability for facial recognition systems. Governments may be tempted to use this as an excuse to record aerial footage of people in public spaces, and could track the gestures of political dissidents (say, people holding protest signs or flashing peace symbols). It could easily combine with other surveillance methods to create a complete picture of a person's movements. This might only find acceptance in limited scenarios where organizations both make it clear that people are on camera and with reassurances that a handshake won't lead to police at their door. Content gathered by BTM robotics training center, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP nagar, robotics training centers in Bannerghatta road, robotics training centers in JP nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore.
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