Dataset - Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution

Dataset - Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution Dataset for manuscripts "Unlocking Aerobatic Potential of Quadcopters: Autonomous Freestyle Flight Generation and Execution". This dataset includes the raw data for the images in the manuscript except for the schematic drawings. The source code is released as ROS packages at GitHub. Large-scale aerobatic flight Code: outdoor.mlx Data: outdoor_data/ outdoor_final.bag: This rosbag captures all essential data during outdoor flight in real-world experiments. outdoor_map.pcd: Pointcloud map file of the outdoor flight environment. [ManeuverName]View[id].mp4: Video files showcasing different maneuvers from various perspectives during rosbag playback in Rviz visualization software. Successive aerobatic maneuvers in confined spaces Code: indoor.mlx Data: indoor_data/ nokov[id].bag: The rosbag captures all essential data during indoor flight in real-world experiments. Each bag representing a single aerobatic flight. Data package representing indoor experimental flights, with each package representing a single flight. indoor_map.pcd: Pointcloud map file of the indoor flight environment. Combination of aerobatic intentions Data: intention_simulation/ [ManeuverName]_Intention.png: Input aerobatic intentions. [ManeuverName]_Action.png: Maneuvers generated based on intentions. These pictures are rendered using Blender. Ablation analyses Yaw compensation Code: ablation_yaw_comp.mlx yaw_sensitivity.mlx Data: ablation_data/YawComp/ [with/without]YCM.mp4: Slow-motion footage of the drone executing flight trajectories in the yaw-sensitive area at 1/50th of the original speed. [with/without]YCM.txt: State computation of the entire trajectory, with a time interval of 0.2 microseconds for each line of state. Trajectory optimization Code: ablation_traj_opt.mlx attitude_penalty.mlx Data: ablation_data/TrajOpt/ OptTrajRet.txt: Various evaluation metrics obtained from 90 trajectory generations. [ManeuverName]_[OptimizeCondition].png: Typical optimized trajectories under different optimization conditions for various maneuvers. Additional data in the rebuttal Matching and surpassing human pilots Code: human_single.mlx human_multi.mlx Data: human_competition/[single/multi]/[auto/manual]/[id].bag: The rosbag captures all essential data about drone flight in competition. Accurate thrust module fitting Code: thrust_fitting.mlx Data: thrust_fitting/[lidar/nokov].csv: measured calibration data of two drones. Optimization of 1000 aerobatic trajectories Code: repeat_trajopt.mlx Data: repeat_traj_opt/ opt_result.txt: final output results of the trajectory optimization problem. optBoundValue.txt: boundary values of trajectories. trajpos.txt: position, velocity, and orientation of the trajectories. Aerobatics vs. Non-Aerobatics Code: nonaero_compare.mlx Data: nonaero_compare/ aerotraj_[aero/non].txt: position and orientation of the trajectories. opt_result_[aero/non].txt: final output results of the trajectory optimization problem. optBoundValue_[aero/non].txt: boundary values of trajectories. trajpos_[aero/non].txt: position, velocity, and orientation of the trajectories.