Efficient Trajectory Planning for High Speed Flight in Unknown Environments
There has been considerable recent work in motion planning for UAVs to enable aggressive, highly dynamic flight in known environments with motion capture systems. However, these existing planners have not been shown to enable the same kind of flight in unknown, outdoor environments.
We are developing an architecture that enables the fast replanning necessary for reactive obstacle avoidance by combining three techniques. First, we developed a technique that couples computationally efficient, closed-form trajectory generation methods with spatial partitioning data structures to reason about the geometry of the environment in real-time. Second, we extended the technique to maintain safety margins during fast flight in unknown environments by planning velocities according to obstacle density. Third, we showed how our receding horizon, sampling-based motion planner uses minimum-jerk trajectories and closed-loop tracking to enable smooth, robust, high-speed flight with the low angular rates necessary for accurate visual-inertial navigation. The data show fast flight in complex, urban environments.
Published in ICRA 2019 —
Ryll, M., Ware, J., Carter, J. and Roy, N. “Efficient Trajectory Planning for High Speed Flight in Unknown Environments.” Proceedings of the International Conference on Robotics and Automation (ICRA). IEEE, 2019.
http://groups.csail.mit.edu/rrg/papers/ryll19a.pdf
