Josip Ćesić

Josip Ćesić did his PhD in the autonomous technologies. He was awarded with the Silver Plaque for his particularly successful doctoral dissertation and scientific research, by the University of Zagreb in 2017.

With Gideon Brothers, his responsibilities include the development of the full autonomy stack and related technologies. He is also actively involved with strategic planning and various business and management activities as it pertains to Gideon Brothers.

Before joining the company, he was a research associate in the Laboratory for Autonomous Systems and Mobile Robotics. His main interests are in the areas of autonomous systems and robotics, as well as team leadership and product management. He is the co-author of more than 20 journal and conference papers presented at the most relevant international scientific robotics events.

Self-driving 2020 – industrial autonomy powered by visual perception

When it comes to autonomous indoor industrial transport systems, such as pallet-carriers and other vehicles used in dynamic warehouse and manufacturing facilities, existing commercial solutions have not proven adequate – the majority of products on the market are not flexible, capable or cost-effective enough for this low-margin sector. There is a clear business need for much cheaper and more flexible systems that provide greater scalability and adaptability while being easier to integrate.

Gideon Brothers addresses this need by offering a plug-and-play, self-driving solution, which does not rely on LiDAR or external guidance systems for navigation, but instead, a new vision-based technology that navigates more effectively.

Gideon Brothers’ technology relies on visual (stereo camera-based) perception paired with AI to navigate, rather than LiDAR. While visual perception is an innovation in itself (there are just two other suppliers in the world working on it), Gideon’s technology is the only solution that deeply understands its environment, the objects in it and their context. This understanding improves the robustness of localization and navigation and allows higher-level use-cases and value-added functionalities derived from recognizing interpreting, and processing data on objects in the environment.