Learning Minimum-Time Flight in Cluttered Environments

Abstract

We tackle the problem of minimum-time flight for a quadrotor through a sequence of waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of the quadrotor, and, thus, resulted in suboptimal solutions. Recent works can plan minimum-time trajectories; yet, the trajectories are executed with control methods that do not account for obstacles. Thus, a successful execution of such trajectories is prone to errors due to model mismatch and in-flight disturbances. To this end, we leverage deep reinforcement learning and classical topological path planning to train robust neural-network controllers for minimum-time quadrotor flight in cluttered environments. The resulting neural network controller demonstrates substantially better performance of up to 19\% over state-of-the-art methods. More importantly, the learned policy solves the planning and control problem simultaneously online to account for disturbances, thus achieving much higher robustness. As such, the presented method achieves 100% success rate of flying minimum-time policies without collision, while traditional planning and control approaches achieve only 40%. The proposed method is validated in both simulation and the real world, with quadrotor speeds of up to 42km/h and accelerations of 3.6g.

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Cited by in corpus (9)

• Autonomous Drone Racing: A Survey
• CTopPRM: Clustering Topological PRM for Planning Multiple Distinct Paths in 3D Environments
• End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight
• A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks with Multi-Robot Aerial Systems
• The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control
• SHINE: Social Homology Identification for Navigation in Crowded Environments
• Swooper: Learning High-Speed Aerial Grasping With a Simple Gripper
• RESC: A Reinforcement Learning Based Search-to-Control Framework for Quadrotor Local Planning in Dense Environments
• FlightBench: Benchmarking Learning-based Methods for Ego-vision-based Quadrotors Navigation