Monotonic curvature plays an important role in industrial design and styling of curves with aesthetic shapes, e.g., in automobile and aircraft design [1].Used in conventional parametric CAD/CAM systems, general B-splines are not adequate for aesthetic requirements.Except for the straight line and circle, monotonic curvature distribution, associated with pleasing shape, is very difficult to achieve.So Farin suggested that a fair curve has a curvature plot with relatively few regions of monotonically varying curvature.Starting from this basis, work on B-spline fairing was developed mainly in three direction: knot-removal-reinsertion methods, optimization methods based on minimizing an energy function, and filtering approaches based on B-spline wavelets.Visual curve completion (interpolating a curve segment, with continuity, to fill a gap) is a fundamental problem for human visual understanding [2].Aesthetically pleasingly shaped curves usually have monotonically varying curvature [3].While the shape of a curve is primarily defined by its curvature distribution, monotonicity of curvature is not easily achieved and controlled.To overcome this problem,

Three-dimensional (3D) tracking of rigid objects plays a very important role in many areas such as augmented reality, computer vision, and robotics. Numerous works have been done to pursue more stable, faster, and more accurate 3D tracking. Among various tracking methods, edge-based 3D tracking has been widely used owing to its many advantages. Furthermore, edge-based methods can be mainly divided into two categories, methods without and those with explicit edges, depending on whether explicit edges need to be extracted. Based on this, representative methods in both categories are introduced, analyzed, and compared in this paper. Finally, some suggestions on the choice of methods in different application scenarios and research directions in the future are given.

Shape memory alloy (SMA) actuators exhibit high strain, high energy density, and self-sensing ability, which are promising characteristics for application in soft robot systems. This paper proposes a new SMA actuator with high accuracy that is driven by antagonistic SMA and super-elastic SMA (SSMA) wires or pairs of SMA wires that is intended to be used in miniature flexible structures. A multifeedback control methodology is presented for the actuator that uses feedback from both the SMA and SSMA wires. The prestress was chosen to minimize the gap between the heating and cooling paths of the strain-to-resistance (S-R) curve for both antagonistic SMA and SSMA. A radial basis function neural network was used to establish the S-R curve model for both self-sensing feedback and SSMA feedback. To achieve better system accuracy and stability, a data-fusion algorithm (based on the support function) was used to combine the multifeedback data. Accurate and stable motion control is demonstrated through multistep response, sinusoidal tracking, and force-disturbance tests. The experimental results show that the combination model using all the feedback has superior accuracy compared to the models that use self-sensing feedback or SSMA feedback. In addition, the maximum standard deviation within the SMA-SSMA antagonistic platform and the paired-SMA antagonistic platform can be reduced to 0.036% and 0.026%, respectively. A soft joint with two degrees of freedom is illustrated to demonstrate how the current research concept could potentially be implemented in bionic soft robots.

Abstract Real‐time rendering of large‐scale engineering computer‐aided design (CAD) models has been recognized as a challenging task. Because of the constraints of limited graphics processing unit (GPU) memory size and computation capacity, a massive model with hundreds of millions of triangles cannot be loaded and rendered in real‐time using most of modern GPUs. In this paper, an efficient GPU out‐of‐core framework is proposed for interactively visualizing large‐scale CAD models. To improve efficiency of data fetching from CPU host memory to GPU device memory, a parallel offline geometry compression scheme is introduced to minimize the storage cost of each primitive by compressing the levels of detail (LOD) geometries into a highly compact format. At the rendering stage, occlusion culling and LOD processing algorithms are integrated and implemented with an efficient GPU‐based approach to determine a minimal scale of primitives to be transferred for each frame. A prototype software system is developed to preprocess and render massive CAD models with the proposed framework. Experimental results show that users can walkthrough massive CAD models with hundreds of millions of triangles at high frame rates using our framework. Copyright © 2016 John Wiley & Sons, Ltd.