Path Planning

While the current part carries the title “path planning” the contributions in this section covers two topics: mapping and planning. In some sense one might argues that intelligent (autonomous) mapping actually requires path planning. While this is correct the contributions actually have a broader scope as is outlined below. A common theme to all of the presentations in this section is the adoption of hybrid representations to facilitate efficient processing in complex environments. Purely geometric models allow for accurate estimation of position and motion generation, but they scale poorly with environmental complexity while qualitative geometric models have a limited accuracy they are well suited for global estimation of trajectories/locations. Through fusion of qualitative and quantitative models it becomes possible to develop systems that have tractable complexity while maintaining geometric accuracy.