AI and Robotics Updates: What to Expect in 2024
In 2024, the number of operational robots worldwide hit a record 3.9 million units. This growth is thanks to exciting tech advancements1. We’re seeing more use of Artificial Intelligence (AI) and machine learning in robots and automation.
Collaborative robots are becoming more common, helping humans and robots work together better. Mobile manipulators are making material handling tasks easier and more efficient. Digital twins are improving robot performance by simulating tasks and predicting maintenance needs.
Humanoid robots are also making progress, especially in underwater exploration and helping at home. These changes are shaping the future of work, blending industrial and service robotics.
Key Takeaways
- The global stock of operational robots reached a new high of 3.9 million units in 2024, indicating significant growth in the industry.
- AI and machine learning are becoming increasingly integrated into robotics and automation, driving new capabilities and applications.
- Collaborative robots are expanding their use cases, enabling more human-robot interaction and cooperation.
- Mobile manipulators are automating material handling tasks, enhancing efficiency and productivity.
- Digital twins are optimizing robot performance through simulations and predictive maintenance.
- Humanoid robots are gaining traction, with advancements in areas like underwater exploration and home assistance.
- The future of the AI and robotics industry looks promising, with ongoing innovations and technological breakthroughs.
Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) have changed the robotics world a lot. Generative AI is a new AI type that can make new things from what it learns. It’s making robot programming easier by using natural language instead of hard code2.
This lets people without coding skills control robots. It opens up new ways to automate jobs in many fields.
Predictive maintenance is another big AI and ML use in robotics. It uses robot performance data to guess when equipment might fail. This can save a lot of money, as each unplanned stop costs $1.3 million in car parts3.
Machine learning finds patterns in data from many robots. This makes robots work better and more reliably3.
