There are numerous tasks that traditional Rob robots can do quite well; however, there are some tasks that they can not, particularly due to their rigidity and metal components. In contrast, soft robots have the ability to interact with people more securely and can also slip into small places quite easily. However, it is necessary for robots to be aware of all their body parts if they want to accomplish their programmed duties. This comes out to be rather difficult for soft robots, which can deform in infinite ways.
Researchers have now developed an algorithm that might make things better significantly. The new technology helps engineered design soft robots that have the capability to collect useful information from their surrounding environment. It brings forward an optimized placement of sensors inside the robot’s body that results in better interaction with the environment and, in turn, efficient accomplishments of tasks given. This is a massive advancement for the Soft Robotic Technology Market as it is a significant step towards the automation of robot design. Interestingly, the system doesn’t just learn a particular task but also ponders upon the best design that would solve a problem. Sensor placements have always been a pain to solve; this solution might shed some hope on the future problem.
The team developed an unique neural network architecture that can optimize sensor placement and the other learning to complete tasks efficiently. For this, the robot's body was first divided into regions referred to as “particles.” Each particle’s rate of strain was used as an input for the neural network. The network uses the concept of trial and error so that it can learn the most efficient sequence of movements that would get the tasks completed, like gripping objects of distinct sizes. Simultaneously, the network also notes the particles that are used most often and remove the lesser-utilized particles from the inputs set for the network’s ensuing trials.
In order to test their algorithm, researchers pitted it against a series of predictions by experts. They requested roboticists to manually select the location of sensors to enable the quickie and easy accomplishment of tasks like grabbing objects. After this, the simulations were run and compared to the robots sensorized by an algorithm. It was concluded that the algorithm was easily able to outperform humans.
The researchers are optimistic that their work would be of great benefit to the automation of robot designing processes. This is not only because of the development brought to control the robot’s movements but also for automating the design of sensorized soft robots. The latter step can be considered essential for creating intelligent tools that would be needed to help people with physical tasks.
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