Advances in machine learning and artificial intelligence in the future could improve the functionality of cobots.

In the manufacturing & industrial sector, technology is constantly evolving. The industry is introducing the use of robots in the workplace, with recent advances in automation. And so far, by substituting or supporting human staff on assembly lines, they have shown tremendous potential for accelerating and automating a variety of manufacturing processes. There are many fears that robots will essentially take over production, leaving people without jobs and transforming the industry as a whole, since the technology is new. Another problem is that robots for development should be both effective and reasonably affordable in order to be implemented on a wide scale.

According to Tech Xplore, "Researchers at Wuhan University of Science and Technology and University of Leicester have recently developed an optimization technique that could help to optimize the cost and efficiency of multiple robots set to operate in assembly lines. This technique, presented in a paper published in Springer Link's Neural Computing and Applications journal, is based on a metaheuristic algorithm known as migrating bird optimization algorithm, which is ideal for solving optimization problems due to its simplicity and flexibility in adapting to the nature of a problem."

They also mentioned that, "The overreaching objective of the recent study by Janardhanan and his colleagues was to optimize assembly lines in which robots and human workers collaborate, ensuring that they can work both effectively and safely. To do this, they developed a multi-objective mixed-integer programming model and used a metaheuristic algorithm. They then tested it on several scenarios in which different types of robots are expected to work together to assemble goods.

The algorithm can minimize an assembly line's overall cycle time and decrease the total purchasing cost of a team of robots. The algorithm's design is inspired by the V- flight formation of birds. The algorithm selects an optimal solution (i.e., a solution that optimizes the total cost and reduces the overall cycle time) among a set of possibilities and replaces outdated solutions identified earlier.

Overall, the migrating bird optimization algorithm was found to achieve remarkable results, which were either better or similar to those achieved by the state-of-the-art techniques it was tested against. In the future, it could be used by manufacturers worldwide to optimize the cost and efficiency of assembly lines, employing a team of robots to support human workers."

Future Possibilities

Advances in machine learning and artificial intelligence in the future could improve the functionality of cobots. When cobots become smarter, they will be likely to function more complex tasks and recall prior work to assist them in the future. In order to complete work more effectively, machine learning can also indicate that cobots would be able to identify themselves and solve any technical problems.

According to the Robotic Industries Association, "Collaborative robots feature technological innovations that solve some of the most pressing challenges in robotic automation. With proven ability to provide ROI, a reputation for high productivity, and low initial costs, collaborative robots are growing in demand.

As collaborative robots become a sought-after technology, they'll quickly grab a significant share of all robot sales, bringing cutting-edge capabilities to a wider range of industries and applications while driving the long-term market growth."