Novel Methodology for Inflection Circle based synthesis of Straight Line Crank Rocker Mechanism

Emerging fields like Compact Compliant Mechanisms have created newer/novel situations for application of straight line mechanisms. Many of these situations in Automation and Robotics are multidisciplinary in nature. Application Engineers from these domains are many times uninitiated in involved procedures of synthesis of mechanisms and related concepts of Path Curvature Theory. This paper proposes a predominantly graphical approach using properties of Inflection Circle to synthesize a crank rocker mechanism for tracing a coupler curve which includes the targeted straight line path. The generated approximate straight line path has acceptable deviation in length, orientation and extent of approximate nature well within the permissible ranges. Generation of multiple choices for the link geometry is unique to this method. To ease the selection, a trained Artificial Neural Network (ANN) is developed to indicate relative length of various options generated. Using studied unique properties of Inflection Circles a methodology for anticipating the orientation of the straight path vis-à-vis the targeted path is also included. Two straight line paths are targeted for two different crank rockers. Compared to the existing practice of selecting the mechanism with some compromise due to inherent granularity of the data in Atlases, proposed methodology helps in indicating the possibility of completing the dimensional synthesis. The case in which the solution is possible, the developed solution is well within the design specifications and is without a compromise.

Decoding Estimates of Curvilinear Self-Motion from Neural Signals in a Model of Primate MSTd

Self-motion produces characteristic patterns of optic flow on the eye of the mobile observer. Movement along linear, straight paths without eye movements yields motion that radiates from the direction of travel (heading). The observer experiences more complex motion patterns while moving along more general curvilinear (e.g. circular) paths, the appearance of which depends on the radius of the curved path (path curvature) and the direction of gaze. Neurons in brain area MSTd of primate visual cortex exhibit tuning to radial motion patterns and have been linked with linear heading perception. MSTd also contains neurons that exhibit tuning to spirals, but their function is not well understood. We investigated in a computational model whether MSTd, through its diverse pattern tuning, could support estimation of a broader range of self-motion parameters from optic flow than has been previously demonstrated. We used deep learning to decode these parameters from signals produced by neurons tuned to radial expansion, spiral, ground flow, and other patterns in a mechanistic neural model of MSTd. Specifically, we found that we could accurately decode the clockwise/counterclockwise sign of curvilinear path and the gaze direction relative to the path tangent from spiral cells; heading from radial cells; and the curvature (radius) of the curvilinear path from activation produced by both radial and spiral populations. We demonstrate accurate decoding of these linear and curvilinear self-motion parameters in both synthetic and naturalistic videos of simulated self-motion. Estimates remained stable over time, while also rapidly adapting to dynamic changes in the observer’s curvilinear self-motion. Our findings suggest that specific populations of neurons in MSTd could effectively signal important aspects of the observer’s linear and curvilinear self-motion.Author SummaryHow do we perceive our self-motion as we move through the world? Substantial evidence indicates that brain area MSTd contains neurons that signal the direction of travel during movement along straight paths. We wondered whether MSTd neurons could also estimate more general self-motion along curved paths. We tested this idea by using deep learning to decode signals produced by a neural model of MSTd. The system accurately decoded parameters that specify the observer’s self-motion along straight and curved paths in videos of synthetic and naturalistic scenes rendered in the Unreal game engine. Our findings suggest that MSTd could jointly signal self-motion along straight and curved paths.

Fat Tail in the Phytoplankton Movement Patterns and Swimming Behavior: New Insights into the Prey-Predator Interactions

Phytoplankton movement patterns and swimming behavior are important and basic topics in aquatic biology. Heavy tail distribution exists in diverse taxa and shows theoretical advantages in environments. The fat tails in the movement patterns and swimming behavior of phytoplankton in response to the food supply were studied. The log-normal distribution was used for fitting the probability density values of the movement data of Oxyrrhis marina. Results showed that obvious fat tails exist in the movement patterns of O. marina without and with positive stimulations of food supply. The algal cells tended to show a more chaotic and disorderly movement, with shorter and neat steps after adding the food source. At the same time, the randomness of turning rate, path curvature and swimming speed increased in O. marina cells with food supply. Generally, the responses of phytoplankton movement were stronger when supplied with direct prey cells rather than the cell-free filtrate. The scale-free random movements are considered to benefit the adaption of the entire phytoplankton population to varied environmental conditions. Inferentially, the movement pattern of O. marina should also have the characteristics of long-range dependence, local self-similarity and a system of fractional order.

Experimental Investigation on the Influence of Fiber Path Curvature on the Mechanical Properties of Composites

Automated fiber placement (AFP) has been widely used as an advanced manufacturing technology for large and complex composite parts and the trajectory planning of the laying path is the primary task of AFP technology. Proposed in this paper is an experimental study on the effect of several different path planning placements on the mechanical behavior of laminated materials. The prepreg selected for the experiment was high-strength toughened epoxy resin T300 carbon fiber prepreg UH3033-150. The composite laminates with variable angles were prepared by an eight-tow seven-axis linkage laying machine. After the curing process, the composite laminates were conducted by tensile and bending test separately. The test results show that there exists an optimal planning path among these for which the tensile strength of the laminated specimens decreases slightly by only 3.889%, while the bending strength increases greatly by 16.68%. It can be found that for the specific planning path placement, the bending strength of the composite laminates is significantly improved regardless of the little difference in tensile strength, which shows the importance of path planning and this may be used as a guideline for future AFP process.

Efficient Path Tracking Control for Autonomous Driving of Tracked Emergency Rescue Robot under 6G Network

This paper proposes a path tracking control algorithm of tracked mobile robots based on Preview Linear Model Predictive Control (MPC), which is used to achieve autonomous driving in the unstructured environment under an emergency rescue scenario. It is the future trend to realize the communication and control of rescue equipment with 6G and edge cloud cooperation. In this framework, linear MPC (LMPC) is suitable for the path tracking control of rescue robots due to its advantages of less computing resources and good real-time performance. However, in such a scene, the driving environment is complex and the path curvature changes greatly. Since LMPC can only introduce linearized feedforward information, the tracking accuracy of the path with large curvature changes is low. To overcome this issue, combined with the idea of preview control, preview-linear MPC is designed in this paper. The controller is verified by MATLAB/Simulink simulation and prototype experiment. The results show that the proposed method can improve the tracking accuracy while ensuring real-time performance and has better tracking performance for the path with large curvature variation.

A New Algorithm for Calculating the Flow Path Curvature (C) from the Square-Grid Digital Elevation Model (DEM)

This paper proposes a flow-path-network-based (FPN-based) algorithm, constructed from a square-grid digital elevation model (DEM) to improve the simulation of the flow path curvature (C). First, the flow-path network model was utilized to obtain an FPN. Then, a flow-path-network-flow-path-curvature (FPN-C) algorithm was proposed to estimate C from the FPN. The experiments consisted of two sections: (1) quantitatively evaluating the accuracy using 5 m DEMs generated from the mathematical ellipsoid and Gauss models, and (2) qualitatively assessing the accuracy using a 30 m DEM of a real-world complex region. The three algorithms proposed by Evans (1980), Zevenbergen and Throne (1987), and Shary (1995) were used to validate the accuracy of the new algorithm. The results demonstrate that the C value of the proposed algorithm was generally closer to the theoretical C value derived from two mathematical surfaces. The root mean standard error (RMSE) and mean absolute error (MAE) of the new method are 0.0014 and 0.0002 m, reduced by 42% and 82% of that of the third algorithm on the ellipsoid surface, respectively. The RMSE and MAE of the presented method are 0.0043 and 0.0025 m at best, reduced by up to 35% and 14% of that of the former two algorithms on the Gauss surface, respectively. The proposed algorithm generally produces better spatial distributions of C on different terrain surfaces.

Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions

Physically interacting with kinematic constraints is commonplace in everyday actions. We report a study of humans turning a crank, a circular constraint that imposes constant hand path curvature and hence should suppress variations of hand speed due to the power-law speed-curvature relation widely reported for unconstrained motions. Remarkably, we found that, when peripheral biomechanical factors are removed, a speed-curvature relation reemerges, indicating that it is, at least in part, of neural origin.