This was a hybrid event with in-person attendance in Wu and Chen Auditorium and virtual attendance…
Area coverage path planning is the problem of finding an efficient path that traverses the region of interest while avoiding existing obstacles. When dealing with real systems, the dynamic changes and uncertainties of the environments increase the state space exponentially and make these problems intractable. As such, exploiting the inherent geometric properties of the areas allows for redefining planning as a combinatorial optimization task. This helps to reduce the overhead complexity of the problem and break it into manageable subproblems for handling uncertainties.
In this talk, we will discuss a two-phase approach for generating a complete pipeline of robust exploration/coverage plans: (i) generate a global coverage plan and (ii) incorporate dynamic changes to adjust the plan. We will specifically discuss different coverage strategies for single and multi-robot systems that take into account obstacles and implicit geological properties of the environment and perform effective data collection suitable to the deployed sensors. We will also discuss how to incorporate uncertainties within these plans. Moreover, we will also see these methods deployed in the real world, automating scientific sampling operations.