Robots might have nailed the art of automation; nevertheless, they continue to remain ill-equipped when it comes to mastering the art of gripping and grasping. Over the past 50 years, robots have mastered the art of working in tightly controlled conditions; for example, robots are well trained to work in car assembly lines.
Since robots have limited functions, they can mostly be trained to perform a single task at a given point in time. For example, they can be used to pick up a car part, as the robot claw knows where the part needs to be carried to and fixed. Given the repetition, the hand grabbing object knows what needs to be done repetitively and does not need any frequent changes in its working patterns.
However, the reality is way different than the fixed assembly line. While humans might find it extremely easy to communicate and adapt to multiple changes in a short span of time, robots find this to be a daunting task. Such unstructured working patterns and varying work environments are pain areas for robots, especially which perform gripping tasks.
Researchers are training robots through Machine Learning, teaching them how to grip successfully. Other researchers are working on improving the robot’s hardware, thereby awarding them with robotic claws to human-like hands. Variation is the key and using data trends to train these robots is the next step in the process of gripping.
Robot Gripper Design – Learning is the key
Each year, Amazon organizes an annual competition known as the “Amazon Robotics Challenge”. In this competition, a team of researchers are required to design and build a robotic arm claw, which holds the capabilities to sort and store a customer’s order in different boxes. The items can be varied and will need to be picked from different boxes into one single box, as per the customer’s order. This competition often involves the use of grasping and identification, which should be mastered by the robotic claw design.
In 2017, Leitner’s ACRV team claimed the winner title for their invention “Cartman”. Cartman was a robot, which incorporated an aluminum frame and a robotic claw assembly. The robot claws were further equipped with two tools, often known as end effectors. These effectors include a suction cup with a vacuum pump and a gripper with two parallel plates. As the robot encounters each object, the researchers try each effector. If the function of the effector doesn’t work, the researchers can change tactics and use the other effector to solve the purpose of the challenge.
Machine learning has become one of the preferred choices to enable robots to grasp objects using claw robots. Many others are using deep learning as well to understand how robots can action simple gripping requests. The combination of a suction cup and a parallel jaw is a popular choice among robot manufacturers. While giving a robot human-like hands might seem an easy way of helping them grip objects, it is not as easy as it seems. The methodology differs between humans and robots, making the whole process of gripping extremely different and methodical.
Robotic Human Arm
A robot-human arm is attracting the interests of roboticists. There is a lot of benefit of robotic claw softness, which is not a very common feature to date. A company, Soft Robotics, in Cambridge, Massachusetts, produces air-actuated grippers that have a well-designed claw-like design. These robotic arm claws are being tested out in the factory environment, before being sold commercially. Their testing is to handle delicate produce, without inflicting any damages on the products.
The future of robotics might have just ushered in; but one can’t negate the scope of robotics, which is increasing as the days are passing. As technology continues to develop, there will be a lot of elements that will enhance the world of robotics and give them an excellent form in the long run.