The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and build a navigation map of a roadway is presented. In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. An iterative equation based on Levenberg–Marquadt is derived, which can achieve the optimal estimation of the body pose. In terms of mapping, LiDAR is used to experimentally construct the grid map based on open-source algorithms, such as Gmapping, Cartographer, Karto, and Hector. A point cloud map, octree map, and compound map are experimentally constructed based on the open-source library RTAB-MAP. By setting parameters, such as the expansion radius of an obstacle and the updating frequency of the map, a cost map for the navigation of a roadheader is established. Combined with algorithms, such as Dijskra and timed-elastic-band, simulation experiments show that the combination of octree map and cost map can support global path planning and local obstacle avoidance.

The paper deals with the notch stress intensity factors (NSIFs) at a blunt V-notch tip in polymer materials using transmitted caustics. First, the governing equations of transmitted caustics which relate to the experimental measurements and the linear elastic stress fields at blunt V-shaped notches are established. Second, a series of caustic curves at the blunt V-notch tip under mode I loading are simulated. Finally, fracture experiments of polymer materials with a blunt V-shaped notch under three-point-bend loading are performed. The NSIFs obtained from caustic measurements show a good agreement with numerical results.