WHITEPAPER: 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? In this whitepaper, Nobleans Bram Odrosslij, Birgit Plantinga, and Mukunda Bharatheesha present a novel approach to robot navigation: 𝘀𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿𝘀 𝗳𝗼𝗿 𝗿𝗼𝗯𝗼𝘁𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.

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.
• Get inspired by concrete performance data and future visions that show how this technology can optimize entire robot fleets.

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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