Collision avoidance under bounded localization uncertainty

We present a multi-mobile robot collision avoidance system based on the velocity obstacle paradigm. Current positions and velocities of surrounding robots are translated to an efficient geometric representation to determine safe motions. Each robot uses on-board localization and local communication to build the velocity obstacle representation of its surroundings. Our close and error-bounded convex approximation of the localization density distribution results in collision-free paths under uncertainty. While in many algorithms the robots are approximated by circumscribed radii, we use the convex hull to minimize the overestimation in the footprint. Results show that our approach allows for safe navigation even in densely packed environments.

Authors

• Daniel Claes
• Daniel Hennes
• Karl Tuyls
• Wim Meeussen

Keywords

• Collision avoidance
• Robot sensing systems
• Uncertainty
• Approximation methods
• Navigation
• Monte Carlo methods
• Position Uncertainty
• Convex Hull
• Close Approximation
• Local Communication
• Collision-free Path
• Time Step
• Workspace
• Line Segment
• Particle Filter
• Half-plane
• Planning Algorithm
• Space Velocity
• Laser Ranging
• Convex Polygon
• Kinematic Constraints
• Static Obstacles
• Dynamic Obstacles
• Large Overestimation
• Circumcircle
• Collision Course
• Minkowski Sum
• Update Phase