LANSCE Diagnostic Robot Localization

Abstract

Los Alamos Neutron Science Center (LANSCE) operates a linear particle accelerator (LINAC) that is used for a number of scientific research projects. Due to the presence of harmful radiation, researchers are not allowed in the beam tunnel during operation. Since the beam tunnel is inaccessible, an autonomous mobile robot is to be developed and deployed in the tunnel, monitoring the accelerator during operation. The robot will present real time data for operators and scientists from sensors such as a video camera, thermal camera, microphones, and muon detectors; allowing for beam diagnostics previously unavailable. Localization is a fundamental step in autonomous navigation of mobile robots, answering the “where am I” question. For this project, an Extended Kalman Filter (EKF) algorithm is implemented as a positional estimator. The EKF relies on an odometric motion model to predict the robot’s position and LIDAR measurement data to update the robot’s position. The measurement model is based on v features extracted from the LIDAR point cluster using the Split-and-Merge algorithm, and the nearest neighbor algorithm associates the features to an a priori feature map of the beam tunnel. The predicted and measured positions are then joined using a weighted value by means of the Kalman gain. The effectiveness of this localization technique is demonstrated using the LabVIEW robotics simulator.