This article addresses key limitations in conventional toolpath planning for wire arc additive manufacturing (WAAM), including excessive nonproductive travel, frequent sharp turns, and inefficient coverage in multiply-connected regions. To resolve these issues, we introduce a novel approach that integrates a hybrid deformable coordinate system with a bilayer traveling salesman problem (TSP) optimization framework. The method adaptively combines Cartesian and polar coordinates using anisotropic deformation factors to align deposition paths with local geometric features, while a penalty-based distance matrix minimizes nonproductive travel across hollowed areas. Comprehensive experiments on representative benchmark models, such as a circular coin with a square hole and a star-rectangle composite structure, demonstrate that the proposed approach reduces nonproductive travel to 217.1 mm and 165.6 mm, respectively. This represents a reduction of up to 82.9% compared to conventional zigzag, Hilbert curve, contour-offset, and spiral strategies, while maintaining favorable coverage and turn count. The results validate the framework's effectiveness in balancing global motion efficiency with local deposition quality, offering a robust automated solution for path planning in WAAM applications with complex internal geometries.

To navigate and recognize where it is, a mobile robot must be able to identify its current location. In an unknown initial position, a robot needs to refer to its environment to determine its location in an external coordinate system. Even with a known initial position, drift in odometry causes the estimated position to deviate from the correct position, requiring correction. We show how to find landmarks without models. We use dense stereo data from our mobile robot's trinocular system to discover image regions that will be stable over widely differing viewpoints. We find image brightness "corners" in images and select those that do not straddle depth discontinuities in the stereo depth data. Selecting corners only in regions of nearly planar stereo data results in landmarks that can be seen in images taken from different viewpoints.

In this paper we present a novel approach to surface recovery from an image sequence of a rotating object. In this approach, the object is illuminated under a collinear light source (where the light source lies on or near the optical axis) and rotated on a controlled turntable. A wire-frame of 3D curves on the object surface is extracted by using shading and occluding contours in the image sequence. Then the whole object surface is recovered by interpolating the surface between curves on the wire-frame. The interpolation can be done by using geometric or photometric constraints. The photometric method uses shading information and is more powerful than geometric methods. The experimental results on real image sequence of matte and specular surfaces show that the technique is feasible and promising.

We describe a technique for surface recovery of a rotating object illuminated under a collinear light source (where the light source lies on or near the optical axis). We show that the surface reflectance function can be directly estimated from the image sequence without any assumption on the reflectance property of the object surface. From the image sequence, the 3D locations of some singular surface points are calculated and their brightness values are extracted for the estimation of the reflectance function. We also show that the surface can be recovered by using shading information in two images of the rotating object. Iteratively using the first-order Taylor series approximation and the estimated reflectance function, the depth and orientation of the surface can be recovered simultaneously. The experimental results on real image sequences of both matte and specular surfaces demonstrate that the technique is feasible and robust.>