Predictive Angular Potential Field-based Obstacle Avoidance for Dynamic UAV Flights

In recent years, unmanned aerial vehicles (UAVs) are used for numerous inspection and video capture tasks. Manually controlling UAVs in the vicinity of obstacles is challenging, however, and poses a high risk of collisions. Even for autonomous flight, global navigation planning might be too slow to react to newly perceived obstacles. Disturbances such as wind might lead to deviations from the planned trajectories. In this work, we present a fast predictive obstacle avoidance method that does not depend on higher-level localization or mapping and maintains the dynamic flight capabilities of UAVs. It directly operates on LiDAR range images in real time and adjusts the current flight direction by computing angular potential fields within the range image. The velocity magnitude is subsequently determined based on a trajectory prediction and time-to-contact estimation. Our method is evaluated using Hardware-in-the-Loop simulations. It keeps the UAV at a safe distance to obstacles, while allowing higher flight velocities than previous reactive obstacle avoidance methods that directly operate on sensor data.

Authors

• Daniel Schleich
• Sven Behnke

Keywords

• Location awareness
• Laser radar
• Three-dimensional displays
• Estimation
• Autonomous aerial vehicles
• Robot sensing systems
• Trajectory
• Unmanned Aerial Vehicles
• Obstacle Avoidance
• Potential Obstacles
• Unmanned Aerial Vehicle Flight
• High Velocity
• Sensor Data
• Autonomous Vehicles
• Trajectory Prediction
• Collision Risk
• Flight Direction
• Angular Field
• Flight Dynamics
• Repulsive Forces
• Current Image
• Future Trajectories
• Classical Field
• Low-level Control
• Safety Threshold
• Smooth Trajectory
• Global Localization
• Velocity Commands
• Current Scan
• Pushing Force
• Unmanned Aerial Vehicle Position
• Collision-free Trajectory
• LiDAR Scans
• 3D LiDAR
• Angular Components
• Narrow Corridor