Technology

The last time we heard from the researchers working on MIT’s robotic cheetah project, they had untethered their machine to let it bound freely across the campus lawns. Wireless and with a new spring in its step, the robot hit speeds of 10 mph (6 km/h) and could jump 13 in (33 cm) into the air. The quadrupedal robot has now been given another upgrade in the form of a LIDAR system and special algorithms, allowing it to detect and leap over obstacles in its path.

MIT’s robotic cheetah project has been in the works for a few years now. The team’s view is that the efficiency with which Earth’s fastest animal goes about its business holds many lessons for the world of robotic engineering. This line of thinking has inspired other like-minded projects, with DARPA and Boston Dynamics both working on robotic cheetahs of their own.

The MIT team says it has now trained the first four-legged robot capable of jumping over hurdles autonomously as it runs. With an onboard LIDAR system, the machine is now able use reflections from a laser to map the terrain. This data is partnered with a special algorithm to dictate the robot’s next moves.

The first part of this algorithm sees the robot identify an upcoming obstacle, and determine both its size and the distance to it. The second part of the algorithm is what enables the robot to manage its approach, determining the best position from which to jump and safely make it over the top. This sees the robot’s stride adjusted if need be, speeding up or slowing down to take off from the ideal launch point. This algorithm works in around 100 milliseconds and is run on the fly, dynamically tuning the robots approach with every step.

Right as the robot goes to leave the ground, a third part of the algorithm helps it work out the optimal jumping trajectory. This involves taking the obstacle height and speed of approach to calculate how much force is required from its electric motors to propel it up and over the hurdle.

Putting the cheetah’s new capabilities to the test, the team first set it down to run on a treadmill while tethered. Running at an average speed of 5 mph (8 km/h), the robot was able to clear obstacles up to 18 in (45 cm) with a success rate of around 70 percent. The cheetah was then unleashed onto an indoor test track, running freely with more space and longer approach times to prepare its jumps, clearing about 90 percent of obstacles.

“A running jump is a truly dynamic behavior,” says Sangbae Kim, assistant professor of mechanical engineering at MIT. “You have to manage balance and energy, and be able to handle impact after landing. Our robot is specifically designed for those highly dynamic behaviors.”

Kim and his team will now look to improve the robot further so that it can leap over obstacles on softer terrain such as grass. They will demonstrate the cheetah’s new capabilities at the DARPA Robotics Challenge in June. Gizmag will be trackside to bring you a closer look.

References:http://www.gizmag.com/

Edgar Antonio Valdés Porras has designed a software and service-oriented theoretical methodology supporting sustainability for cities, which if implemented, would increase economic impact points and infrastructure in Mexico and the Netherlands.

The Mexican specialist designs algorithms that solve the problems of communication and interaction between various economic sectors to further implement a system of software services.
Using the scrum methodology, he has developed a system based on knowing the specific problems of users or residents to create algorithms. “To create a network, points of impact are identified, the length is analyzed, the services that can be applied and then it monitors the effectiveness.”
The network of services currently being designed by Porras Valdes, facilitates the entry of government, technology and agriculture products that are interconnected. In the Netherlands, there is a network of effective communication and transport. One example is the port of Rotterdam, which is surrounded by download centers and warehouses to facilitate its function. A product, such as the one proposed by the Mexican researcher, would help in the efficacy of various production processes.
The mathematician works in Holland developing software aimed at social sustainability, cultural and agricultural programs that help solve several problems by making various tools available to the population. The improvement of social programs, for example, helps to reduce vandalism.
To implement a sustainable service, research is required to obtain a map of the location, geographical qualities, infrastructure and population attributes and generate a base of technological and social services to support the strategy to be implemented. Each of these aspects corresponds to a network node and forms a micro-network that seeks to harness all resources efficiently to create sustainable cities. The entire process takes an average of four years.
He plans to bring the system to his homeland. He states that “one of the current problems in Mexico is the centralization of resources, which are distributed incorrectly (most are located in the capital, Mexico City). We need to organize different cities to take advantage of all remedies. We need to look at microgrids and create sustainable cities that take advantage of the topology of the country.”
“We must solve the problem from the root, not with just a Band-aid. In the Netherlands, the range of possibilities is reviewed and then a decision is made. Mexico should do the same,” says Valdés Porras.

References:http://phys.org/

Following the much-hyped iOS launch back in March, Twitter’s live broadcasting app Periscope has now landed on Android. Unveiled on Tuesday, the app carries the same functionality as its iOS sibling, but with a few minor differences unique to the Android platform.When Periscope debuted earlier this year, it generated much discussion about the future of broadcasting. From that point, anybody wielding an iOS device could stream all the action live from their camera to anybody willing to tune in.

Much like Twitter itself, it quickly became a popular tool for celebrities and was adopted by everybody from Jimmy Fallon to Ringo Starr. What’s more, it raised interesting questions about piracy, with this month’s Pay-Per-View Mayweather-Pacquiao bout beamed live to the smartphones of non-paying sports fans all around the world.

Android users running version 4.4 (KitKat) can now freely download the Periscope app from Google Play. As it does on iOS, the app integrates with Twitter, offering users a list of suggested accounts to follow the first time they sign in. The home screen displays live and recent streams from people you follow, along with featured streams suggested by the app.

A shiny red button at the bottom right of screen can be hit to begin a broadcast of your own, which users can choose to be public or a private broadcast streamed only to followers they select. Give the broadcast a title, tag the location if you wish and you’re away, bringing a summary of your lunch or a fire in Brooklyn live to the mobile screens of anybody who is interested.

In a blog post, Periscope’s developers note a few differences between the Android and iOS versions. Further to an interface inspired by Material design, Google’s visual language, Android users can configure the app to push notifications when somebody they follow on Twitter broadcasts for the first time and also if somebody they are following shares somebody else’s broadcast. Another added feature is the ability to resume watching broadcasts from where you left off, should you be interrupted by a phone call or message.

References:http://www.gizmag.com/

Today’s industrial robots are remarkably efficient—as long as they’re in a controlled environment where everything is exactly where they expect it to be.

But put them in an unfamiliar setting, where they have to think for themselves, and their efficiency plummets. And the difficulty of on-the-fly motion planning increases exponentially with the number of robots involved. For even a simple collaborative task, a team of, say, three autonomous robots might have to think for several hours to come up with a plan of attack.
This week, at the Institute for Electrical and Electronics Engineers’ International Conference on Robotics and Automation, a group of MIT researchers were nominated for two best-paper awards for a new algorithm that can significantly reduce robot teams’ planning time. The plan the algorithm produces may not be perfectly efficient, but in many cases, the savings in planning time will more than offset the added execution time.
The researchers also tested the viability of their algorithm by using it to guide a crew of three robots in the assembly of a chair.
“We’re really excited about the idea of using robots in more extensive ways in manufacturing,” says Daniela Rus, the Andrew and Erna Viterbi Professor in MIT’s Department of Electrical Engineering and Computer Science, whose group developed the new algorithm. “For this, we need robots that can figure things out for themselves more than current robots do. We see this algorithm as a step in that direction.”
Rus is joined on the paper by three researchers in her lab—first author Mehmet Dogar, a postdoc, and Andrew Spielberg and Stuart Baker, both graduate students in electrical engineering and computer science.

Grasping consequences

The problem the researchers address is one in which a group of robots must perform an assembly operation that has a series of discrete steps, some of which require multirobot collaboration. At the outset, none of the robots knows which parts of the operation it will be assigned: Everything’s determined on the fly.
Computationally, the problem is already complex enough, given that at any stage of the operation, any of the robots could perform any of the actions, and during the collaborative phases, they have to avoid colliding with each other. But what makes planning really time-consuming is determining the optimal way for each robot to grasp each object it’s manipulating, so that it can successfully complete not only the immediate task, but also those that follow it.
“Sometimes, the grasp configuration may be valid for the current step but problematic for the next step because another robot or sensor is needed,” Rus says. “The current grasping formation may not allow room for a new robot or sensor to join the team. So our solution considers a multiple-step assembly operation and optimizes how the robots place themselves in a way that takes into account the entire process, not just the current step.”
The key to the researchers’ algorithm is that it defers its most difficult decisions about grasp position until it’s made all the easier ones. That way, it can be interrupted at any time, and it will still have a workable assembly plan. If it hasn’t had time to compute the optimal solution, the robots may on occasion have to drop and regrasp the objects they’re holding. But in many cases, the extra time that takes will be trivial compared to the time required to compute a comprehensive solution.
Principled procrastination
The algorithm begins by devising a plan that completely ignores the grasping problem. This is the equivalent of a plan in which all the robots would drop everything after every stage of the assembly operation, then approach the next stage as if it were a freestanding task.
Then the algorithm considers the transition from one stage of the operation to the next from the perspective of a single robot and a single part of the object being assembled. If it can find a grasp position for that robot and that part that will work in both stages of the operation, but which won’t require any modification of any of the other robots’ behavior, it will add that grasp to the plan. Otherwise, it postpones its decision.
Once it’s handled all the easy grasp decisions, it revisits the ones it’s postponed. Now, it broadens its scope slightly, revising the behavior of one or two other robots at one or two points in the operation, if necessary, to effect a smooth transition between stages. But again, if even that expanded scope proves too limited, it defers its decision.
If the algorithm were permitted to run to completion, its last few grasp decisions might require the modification of every robot’s behavior at every step of the assembly process, which can be a hugely complex task. It will often be more efficient to just let the robots drop what they’re holding a few times rather than to compute the optimal solution.
In addition to their experiments with real robots, the researchers also ran a host of simulations involving more complex assembly operations. In some, they found that their algorithm could, in minutes, produce a workable plan that involved just a few drops, where the optimal solution took hours to compute. In others, the optimal solution was intractable—it would have taken millennia to compute. But their algorithm could still produce a workable plan.
“With an elegant heuristic approach to a complex planning problem, Rus’s group has shown an important step forward in multirobot cooperation by demonstrating how three mobile arms can figure out how to assemble a chair,” says Bradley Nelson, the Professor of Robotics and Intelligent Systems at Swiss Federal Institute of Technology in Zurich. “My biggest concern about their work is that it will ruin one of the things I like most about Ikea furniture: assembling it myself at home.”

References:http://phys.org/