The self-learning robot; The future of intelligent navigation at scale?

How do you ensure robots can navigate efficiently in complex and dynamic environments without excessive costs and requiring endless fine-tuning?  To address this, Nobleans Bram Odrosslij, Birgit Plantinga and Mukunda Bharatheesha present whitepaper with a novel approach to robot navigation: self-learning motion controllers for robots based on reinforcement learning.

As a result, this article written specifically for professionals in robotics, AI and automation, offers valuable insights into a navigation method that combines affordability and scalability. Our self-learning robot Cindy™ serves as an excellent example of this approach. With impressive success rates—100% in wall maps and 91.7% in complex BARN maps—Cindy shows that advanced navigation solutions are within reach.

• Discover how current navigation methods are reaching their limits and how reinforcement learning is changing the game.
• Learn about the practical implementation of Nobleo Technology’s robot Cindy™ and the challenges that were overcome.
• Finaly, get inspired by concrete performance data and future visions that show how this technology can optimize entire robot fleets.

Read the article here (no download needed):

The self-learning robot – The future of intelligent navigation at scale

To stay ahead of tomorrow, we give our Nobleans the space and opportunity to explore and further develop new and exciting techniques, like self-learning robots. Investing in our Nobleans continuously contributes to the development of groundbreaking technologies. This research and article are an example, and we’re excited to share it with you.

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