Vehicles are the worst polluter of the atmosphere with emissions clogging up the air around us in almost all big cities. Part of the problem is that they use fossil fuels that are not quite as efficient in burning. Another part is that vehicles are heavy mechanical beasts that need tremendous power to be pulled.

At the Indian Institute of Technology a research academy scholar Abhishek Tripathi has begun an experiment to make vehicles lighter. By reengineering the vehicles he hopes to make them more efficient in burning fuel. He has turned to magnesium instead of aluminum to make the frame of his vehicle lighter.

Magnesium is half the weight of aluminum and would work well in theory. However it is also a very brittle metal and can’t always be molded into the shapes required for automobile parts. Instead of using conventional processing techniques such as rolling, forging and extrusion, Tripathi is experimenting with a new technique called Friction Stir Processing or FSP.

FSP is a shorter solid-state processing technique which will be able to make structural changes quickly and in a single step. FSP will be able to address refinement and densification homogenously and fast. This is one science project that could have a major impact on the way vehicles are designed in the future.

First responders have to often risk their lives in order to rescue others. With robots designed to help first responders in their rescue missions the element of risk can be controlled. One of the main reasons why robot rescuers have not become quite as popular as earlier envisioned is because they are extremely energy hungry. They discharge their batteries super fast and soon become useless in areas where they can not be recharged quickly and efficiently.

Researchers at Sandia National Laboratories are tackling this issue of energy efficiency in robots in the hope of creating true robot rescuers who can function just as well as their human counterparts and for just as long as it takes. They are developing technology that will really improve the endurance of biped robots, allowing them operate for extra periods of time while working in the types of locomotion most relevant to disaster response scenarios.

The graduate students are planning to showcase their new technological advances in the DARPA Robotics Challenge Finals in June 2015. The goal of this science project here is not to make a big, bad ass robot, but rather an efficient one that can be used in emergency situations as a major tool. It will be interesting to see how these robots develop.

The Earth is covered by water and land constitutes just one fourth of the surface, yet human beings are no where near uncovering the secrets of the deep oceans than a couple of hundred years ago. The human body is much too fragile to endure the harsh environment of the underwater regions for long stretches of time. This limits the ability of human researchers to explore the depths of the ocean first hand in a detailed manner.

For the past few decades there has been considerable work done in the field of oceanology, but scientific research has received a real boost when the field or underwater robotics began to flourish. Researchers at the National University of Singapore are closer to creating underwater robotic creatures with a high degree of artificial intelligence.

The robotic turtles which were designed by NUS are among the most maneuverable robots that have been made available for ocean research. Soon they will be able to produce a whole swarm that can work autonomously together enabling ocean researchers to conduct science experiments that they could not even have dreamed about a decade ago. Perhaps this new breed of underwater robots will finally be able to patrol the ocean the way human beings have never been able to.

Seeing something and knowing what it is and how you can use it can take a human being less than a fraction of a second to compute. However when it comes to a robot, the whole process is overseen by a complex algorithm that takes in input from a camera and then categorizes what is shown.

Robot vision is not so much true vision as a program that gets an image and tells the robot what the object in the image is. Then it also lets the robot know the uses that the object that has been sighted. This is a skill that all robots that hope to work in households in the future must master. At the Massachusetts Institute of Technology researchers are working on making this easier for the robots to do.

Lawson Wong, a graduate student in electrical engineering and computer science has been working on a new algorithm that can make robot vision for future household robots better. After several attempts and improvements the researcher has come up a new algorithm that shows a lot of promise. The objects that a robot is commonly likely to encounter are sorted into specific groups to aid faster recognition of the object. The tests of the science project have been promising.

YouTube has many educational videos available and has often been used as a supplement by many homeschooling families. However the scope of video teaching has now been expanded to include artificial intelligence. Yes, robots are now using YouTube videos to learn how to perform simple tasks.

Researchers at the University of Maryland have come up with a robotic system that are allowing robots to learn the intricacies of grasping and manipulation of cooking utensils to cook using online videos. The robots are taught a certain set of movements and then they have to “think” about which motion is best suited to the task that they want to perform.

The computer scientist Yiannis Aloimonos, is hoping to enable the robots to observe and identify objects in order for them to generate new behaviour based on these observations. So a robot can learn how to make an omelet by watching the relevant YouTube video and deciding how to manipulate the ingredients available.

Now this is a science project that brings breakfast in bed presented by a robot one step closer to reality. As per Yiannnis the one key challenge was devising a way for the robots to parse individual steps appropriately, while gathering information from videos that varied in quality and consistency.