Estimating curvilinear self-motion from optic flow with a biologically inspired neural system

Optic flow provides rich information about world-relative self-motion and is used by many animals to guide movement. For example, self-motion along linear, straight paths without eye movements, generates optic flow that radiates from a singularity that specifies the direction of travel (heading). Many neural models of optic flow processing contain heading detectors that are tuned to the position of the singularity, the design of which is informed by brain area MSTd of primate visual cortex that has been linked to heading perception. Such biologically inspired models could be useful for efficient self-motion estimation in robots, but existing systems are tailored to the limited scenario of linear self-motion and neglect sensitivity to self-motion along more natural curvilinear paths. The observer in this case experiences more complex motion patterns, the appearance of which depends on the radius of the curved path (path curvature) and the direction of gaze. Indeed, MSTd neurons have been shown to exhibit tuning to optic flow patterns other than radial expansion, a property that is rarely captured in neural models. We investigated in a computational model whether a population of MSTd-like sensors tuned to radial, spiral, ground, and other optic flow patterns could support the accurate estimation of parameters describing both linear and curvilinear self-motion. We used deep learning to decode self-motion parameters from the signals produced by the diverse population of MSTd-like units. We demonstrate that this system is capable of accurately estimating curvilinear path curvature, clockwise/counterclockwise sign, and gaze direction relative to the path tangent in both synthetic and naturalistic videos of simulated self-motion. Estimates remained stable over time while rapidly adapting to dynamic changes in the observer's curvilinear self-motion. Our results show that coupled biologically inspired and artificial neural network systems hold promise as a solution for robust vision-based self-motion estimation in robots.

Calculating Filament Feed in the Fused Deposition Modeling Process to Correctly Print Continuous Fiber Composites in Curved Paths

Fused deposition modeling (FDM) is a popular additive manufacturing (AM) method that has attracted the attention of various industries due to its simplicity, cheapness, ability to produce complex geometric shapes, and high production speed. One of the effective parameters in this process is the filament feed presented in the production G-code. The filament feed is calculated according to the layer height, the extrusion width, and the length of the printing path. All required motion paths and filling patterns created by commercial software are a set of straight lines or circular arcs placed next to each other at a fixed distance. In special curved paths, the distance of adjacent paths is not equal at different points, and due to the weakness of common commercial software, it is not possible to create curved paths for proper printing. The creation of a special computer code that can be used to make various functions of curved paths was investigated in this study. The filament feed parameter was also studied in detail. Next, by introducing a correction technique, the filament feed was changed on the curved path to uniformly distribute the polymer material. Variable-stiffness composite samples consisting of curved fibers can be produced with the proposed method. The high quality of the printed samples confirms the suggested code and technique.