A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance

The objective of this paper is to present a full-stack, real-time kinodynamic planning framework and demonstrate it on a quadrotor for collision avoidance. Specifically, the proposed framework utilizes an offlineonline computation paradigm, neighborhood classification through machine learning, sampling-based motion planning with an optimal control distance metric, and trajectory smoothing to achieve real-time planning for aerial vehicles. The approach is demonstrated on a quadrotor navigating obstacles in an indoor space and stands as, arguably, one of the first demonstrations of full-online kinodynamic motion planning; exhibiting execution times under 1/3 of a second. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory.