The industry and public sector are pushing for robotic solutions with high level of autonomy that can operate safely and efficiently in increasingly complex operations. And great strives have been done to realize stand-alone automated functions for robot acting (e.g., grasping, collision avoidance), as well as for high-level automated planning of missions. However, even for simple missions, the planning complexity quickly explodes, and high-level planning is often performed as an "offline" process where the world is assumed to be static. To enable real-life fully autonomous single- and multi-robot missions, we need robots that can balance long-term planning with the ability to react to immediate events. To meet this need we will develop methods in the ROBPLAN project to tightly combine planning and acting by building on techniques from symbolic AI approaches enhanced by non-symbolic AI. Moreover, we will develop methods for distributed robot decision-making during planning and acting to enable multi-robot autonomous missions with and without humans in the loop.
We will demonstrate results on I&M use cases within the oil and gas industry, but project results will also be applicable in other application domains where autonomous robots are increasingly deployed and can benefit from a higher level of autonomy in operations; agri-food, healthcare and manufacturing. The combination of new scientific results beyond the state of the art and real-life demonstration will increase impact of results both in research and industry.
The project is co-financed by the Research Council of Norway (RCN). ROBPLAN is coordinated by SINTEF Digital, with NTNU, Equinor and ScoutDI as partners.
