MIT Researchers have developed soft-bodied robots that can sense their own positions in space
Soft bodied robots are distinguished from automotive or industrial robots by the use of soft, pliable materials in their design.
Artificial intelligence (AI) and robotics are revolutionizing the way we live. AI-enabled robots would be able to see, interpret, and execute activities based on the current circumstance. Artificial intelligence is now used in a variety of areas, such as mobile apps like Google Maps, voice assistants, and so on. AI in robotics for industrial uses, on the other hand, would be a totally different scenario. It will minimize human labour to a minimum level while maximizing efficiency. Industries with artificially intelligent robots are entering a new age. To help perform tasks in an easier way, soft bodied robots, an area of robotics concerned with the construction of robots from extremely submissive materials, has recently come up.
Cobots, or collaborative robots, are currently gaining popularity. For all safety purposes, robots now operate at a distance from humans. The concept is to bring humankind and robots closer. Cobots will collaborate with humans in close quarters. They can be used in construction, as well as other sectors and human services. They may be used for monitoring, plant processing, or some other industry or societal operation.
It is now reasonable to see robotics as a viable choice for our industries. Even nowadays, we use a lot of robotics in our industries, particularly in manufacturing facilities in the automotive industry. The entire production line is crammed with automated robotic hands going about their work. They are, however, equipped for a specific role and perform it well. However, robotics presence in industries should be expanded in the future. It will result in a market climate that is both profitable and sustainable.
Traditional robots aren’t suited out for certain tasks due to their physical characteristics. Soft-bodied robots, however, could be better able to communicate with humans and fit into narrow areas. Soft robots are distinguished from automotive or industrial robots by the use of soft, pliable materials in their design. Soft robots are built differently than other types of robots, and they perform tasks that are somewhat different as well. Robots, on the other hand, need to know where all of their body parts are in order to efficiently accomplish their programmed tasks. This is a big challenge for a soft robot, which can deform in almost endless ways.
As per report of Tech Xplore, “MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design.”
It also mentioned that, “the researchers developed a novel neural network architecture that both optimizes sensor placement and learns to efficiently complete tasks. First, the researchers divided the robot’s body into regions called “particles.” Each particle’s rate of strain was provided as an input to the neural network. Through a process of trial and error, the network “learns” the most efficient sequence of movements to complete tasks, like gripping objects of different sizes. At the same time, the network keeps track of which particles are used most often, and it culls the lesser-used particles from the set of inputs for the networks’ subsequent trials.
By optimizing the most important particles, the network also suggests where sensors should be placed on the robot to ensure efficient performance. For example, in a simulated robot with a grasping hand, the algorithm might suggest that sensors be concentrated in and around the fingers, where precisely controlled interactions with the environment are vital to the robot’s ability to manipulate objects. While that may seem obvious, it turns out the algorithm vastly outperformed humans’ intuition on where to site the sensors.”