Incremental micro-UAV motion replanning for exploring unknown environments

This paper describes an approach to motion generation for quadrotor micro-UAV's navigating cluttered and partially known environments. We pursue a graph search method that, despite the high dimensionality of the problem, the complex dynamics of the system and the continuously changing environment model is capable of generating dynamically feasible motions in real-time. This is enabled by leveraging the differential flatness property of the system and by developing a structured search space based on state lattice motion primitives. We suggest a greedy algorithm to generate these primitives off-line automatically, given the robot's motion model. The process samples the reachability of the system and reduces it to a set of representative, canonical motions that are compatible with the state lattice structure, which guarantees that any incremental replanning algorithm is able to produce smooth dynamically feasible motion plans while reusing previous computation between replans. Simulated and physical experimental results demonstrate real-time replanning due to the inevitable and frequent world model updates during micro-UAV motion in partially known environments.