Camera Placement Optimization for a Novel Modular Robot Tracking System

The surge in industrial robots performing high-accuracy tasks like milling and spray painting necessitates effective error compensation methods. External tracker hardware has been traditionally employed, but its cost and limited scalability hinder its effectiveness as accuracy requirements change. This paper proposes a novel optical tracking system using holographic multipoint detection comprised of multiple independently placed cameras. The placement is determined using a novel optimization method that takes into account an error model of the system, the dynamic movement of the robot, and the resulting occlusion of the markers. This optimization algorithm is evaluated on a simulated two-arm robot scenario showing its capability to decrease the triangulation error.