Control-driven mapping and planning

Layered hybrid controllers typically include a planner at the top level with reactive control at the lower levels. The planner considers the state of the robot in a global context. The low-level controllers consider only the local environment of the robot and are able to operate at a high frequency to ensure the safety of the robot. Also, it is often the case that the low-level controllers consider more aspects of the robot's state (e.g. kinematic constraints) than the planner. The consideration of such constraints at the planning level would prohibitively increase the state space the planner must consider and, accordingly, its running time and complexity. In this paper, we investigate how we can take advantage at the planning level of domain knowledge encapsulated in the lower level controllers, and we introduce a feedback mechanism that enables low-level controllers to influence the high-level planner.