A Robust Docking Strategy for a Mobile Robot using Flow Field Divergence

We present a robust strategy for docking a mobile robot in close proximity with an upright surface using optical flow field divergence. Unlike previous approaches, we achieve this without the need for explicit segmentation of the surface in the image, and using complete optical estimation (i. e. no affine models) in the control loop. A simple proportional control law is used to regulate the vehicle's velocity, using only the raw, unfiltered flow divergence as input. Central to the robustness of our approach is the derivation of a time-to-contact estimator that accounts for small rotations of the robot during ego-motion. We present both analytical and experimental results showing that through tracking of the focus of expansion to a looming surface, we may compensate for such rotations, thereby significantly improving the robustness of the time-to-contact estimate. This is demonstrated using an off-board natural image sequence, and in closed-loop control of a mobile robot

Authors

• Chris McCarthy
• Nick Barnes

Keywords

• Robustness
• Mobile robots
• Image motion analysis
• Optical sensors
• Biomedical optical imaging
• Image segmentation
• Motion estimation
• Australia
• Focusing
• Velocity control
• Flow Field
• Mobile Robot
• Docking Strategy
• Close Proximity
• Optimal Control
• Electromagnetic Field
• Optical Flow
• Simple Control
• Robot Control
• Simple Law
• Flow Divergence
• Control Strategy
• Partial Differential
• Angular Velocity
• Constant Velocity
• Optical Axis
• Direction Of Motion
• Imaging Center
• Ground Plane
• Image Patches
• Forward Velocity
• Effect Of Rotation
• Stopping Distance
• Image Point
• Single Camera
• Axis Of Motion
• Angle Of Approach
• Use Of Tracking
• Translational Velocity
• Obstacle Avoidance