Challenges in Integrating Low-level Path Following and High-level Path Planning Over Polytopic Maps

Autonomous vehicle navigation and control require both planning and successful following of a desired path through an environment. This is often performed by a hierarchical framework that includes both a path planning algorithm and a path following controller. However, these two levels often operate with different modeling assumptions, constraints, and cost functions, leading to several challenges when integrated. To demonstrate the challenges that arise specifically when mapped obstacles represent regions that can be traversed but with varying traversal costs, this paper builds an intuitive control hierarchy consisting of an A* path planner, a Kalman filter to estimate traversal costs, and a model predictive controller. While each of these elements have been deeply studied individually, this paper identifies challenges that can arise from their coupled behavior and categories existing literature that addresses some of these challenges. Simulation results for a ground vehicle traversing regions of varying cost illustrate both the benefits and potential pitfalls of this hierarchical integration, motivating its further study for vehicle autonomy.