Safe receding horizon control for aggressive MAV flight with limited range sensing

Micro Aerial Vehicles (MAVs) are becoming ubiquitous, but most experiments and demonstrations have been limited to slow flight except in open environments or in laboratories with motion capture systems. In this paper, we develop representations and algorithms for aggressive flight in cluttered environments. We incorporate the specific dynamics of the system and generate safe, feasible trajectories for fast navigation in real time. Specifically, we use a polyhedral decomposition of the visible free space and address the generation of safe trajectories that are within the space. Because of the limited field of view, we adopt a receding horizon control policy (RHCP) for planning over a finite time horizon, but with the guarantee that there exists a safe stopping control policy over a second time horizon from the planned state at the end of the horizon. Thus, the robot planning occurs over two horizons. While the robot executes the planned trajectory over the first time horizon, the map of obstacles is refreshed allowing the planner to generate a refined plan, once again over two time horizons. The key algorithmic contribution of the paper lies in the fast planning algorithm that is able to incorporate robot dynamics while guaranteeing safety. The algorithm is also optimal in the sense that the receding horizon control policy is based on minimizing the trajectory snap. Central to the algorithm is a novel polyhedral representation that allows us to abstract the trajectory planning problem as a problem of finding a path through a sequence of convex regions in configuration space.