coordinating multiple rovers with interdependent science objectives
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The MISUS system combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers. A distributed planning and scheduling approach is used to generate efficient, multi-rover coordination plans, monitor plan execution, and perform re-planning when necessary. A machine learning clustering component is used to deduce geological relationships among collected data and select new science activities. A key concept promoted by this system is the use of goal interdependency information to perform plan optimization and increase the value of collected science data. We discuss how we represent and reason about goal dependency and utility information in our planning system and explain how this information can change dynamically during system use. We show through experimental results that our approach significantly increases overall plan quality versus a standard approach that treats goal utilities independently.
planning multi-agent coordination