Improved Strategy of Two-Node Curved Beam Element Based on the Same Beam’s Nodes Information

The curved beam with a great initial curvature is the typical structure and applied widely in real engineering structures. The common practice in the current literature employs two-node straight beam elements as the elementary members for stress and displacement analysis, which needs a large number of divisions to fit the curved beam shape well and increases computational time greatly. In this paper, we develop an improved accurate two-node curved beam element (IC2) in 3D problems, combining the curved Timoshenko beam theory and the curvature information calculated from the same beam curve. The strategy of calculating the curvature information from the same bean curve in the IC2 beam element and then transferring the curvature information to the two-node straight beam element can greatly enhance the accuracy of the mechanical analysis with no extra calculation burden. We then introduce the finite element implementation of the IC2 beam element and verify by the complex curved beam analysis. By comparison with simulation results from the straight two-node beam element in the MIDAS (S2-MIDAS) and the three-node curved beam element adopted in the ANSYS (C3-ANSYS), the simulation results of the typical quarter arc examples under constant or variable curvature show that the IC2 beam element based on curved beam theory is a combination of efficiency and accuracy. And, it is a good choice for analysis of complex engineering rod structure with large initial curvature.

A topological extension of movement primitives for curvature modulation and sampling of robot motion

This paper proposes to enrich robot motion data with trajectory curvature information. To do so, we use an approximate implementation of a topological feature named writhe, which measures the curling of a closed curve around itself, and its analog feature for two closed curves, namely the linking number. Despite these features have been established for closed curves, their definition allows for a discrete calculation that is well-defined for non-closed curves and can thus provide information about how much a robot trajectory is curling around a line in space. Such lines can be predefined by a user, observed by vision or, in our case, inferred as virtual lines in space around which the robot motion is curling. We use these topological features to augment the data of a trajectory encapsulated as a Movement Primitive (MP). We propose a method to determine how many virtual segments best characterize a trajectory and then find such segments. This results in a generative model that permits modulating curvature to generate new samples, while still staying within the dataset distribution and being able to adapt to contextual variables.

The equivalence Spacetime curvature - Information wave function Entropy: a link between quantum mechanics and general relativity in Universe and Brain

Our work consists on showing that the Spacetime curvature introduced by Einstein in the Universe and also in the Brain is a result of the Information Entropy of different quantum Paths of elementary particles (leptons, bosons…) of path integrals model. We started by seeing the structure of how the incoming information is processed and then propagated in the brain and how the latter is deformed in each neuron to thus create a potential reaction response distorted or not. In quantum physics, and particularly in quantum field theory (QFT), the paths in path integrals have an equivalent role to paths between two neighboring linked neurons (synapses + neurotransmitters + dendrites). Using this modeling, we prove mathematically that the entropy of the Information coming from the paths could be equivalent to the Spacetime curvature in Universe as in Brain

A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models

The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse. The proposed method combines color and depth curvature information to create a Gaussian mixture model that can segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. This helps in overcoming the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that does not require a priori knowledge about object geometry and can be implemented online with near real-time performance. The efficacy of the proposed approach is demonstrated through simulation as well as real-world experiment.

Vehicle speed preview control with road curvature information for safety and comfort promotion

Vehicle lateral acceleration is a critical state and index for vehicle safety and ride comfort. To limit it in high speed cornering situation, a vehicle speed preview controller is proposed with the information of future road curvature, just as a human driver behavior. The future road curvature can be obtained from high definition map in intelligent vehicle control, and to implement it, model predictive control method (MPC) is implemented taking advantage of its preview nature. In this preview speed control framework, a novel kinematics model with vehicle location, speed and track curvature is established for vehicle states prediction. The control performance index of MPC is constructed with vehicle road following index and lateral acceleration index with the aiming of promoting safety and ride comfort. The controller is evaluated during cornering with different road trajectory, initial speed, preview time and road adhesion coefficient in a hardware-in-the-loop simulation platform. It is testified that vehicle slows down before cornering as human driver does to decrease lateral acceleration and steering angle with the benefit of promoting comfort and safety.

Accelerated Saddle Point Refinement Through Full Exploitation of Partial Hessian Diagonalization

Identification and refinement of first order saddle point (FOSP) structures on the potential energy surface (PES) of chemical systems is a computational bottleneck in the characterization of reaction pathways. Leading FOSP refinement strategies require calculation of the full Hessian matrix, which is not feasible for larger systems such as those encountered in heterogeneous catalysis. For these systems, the standard approach to FOSP refinement involves iterative diagonalization of the Hessian, but this comes at the cost of longer refinement trajectories due to the lack of accurate curvature information. We present a method for incorporating information obtained by an iterative diagonalization algorithm into the construction of an approximate Hessian matrix that accelerates FOSP refinement. We measure the performance of our method with two established FOSP refinement benchmarks and find a 50% reduction on average in the number of gradient evaluations required to converge to a FOSP for one benchmark, and a 25% reduction on average for the second benchmark.

Accelerated Saddle Point Refinement Through Full Exploitation of Partial Hessian Diagonalization

Identification and refinement of first order saddle point (FOSP) structures on the potential energy surface (PES) of chemical systems is a computational bottleneck in the characterization of reaction pathways. Leading FOSP refinement strategies require calculation of the full Hessian matrix, which is not feasible for larger systems such as those encountered in heterogeneous catalysis. For these systems, the standard approach to FOSP refinement involves iterative diagonalization of the Hessian, but this comes at the cost of longer refinement trajectories due to the lack of accurate curvature information. We present a method for incorporating information obtained by an iterative diagonalization algorithm into the construction of an approximate Hessian matrix that accelerates FOSP refinement. We measure the performance of our method with two established FOSP refinement benchmarks and find a 50% reduction on average in the number of gradient evaluations required to converge to a FOSP for one benchmark, and a 25% reduction on average for the second benchmark.

High-Precision Extraction of Segment Points of 2D Profiles Based on a Dynamic Grid Method

In order to improve the reconstruction precision and performance of 2D profiles, this paper presents a new high-precision extraction method of segment points of 2D profile features. Firstly, we extract the intervals which include initial segment points based on the curvature information of data points. Secondly, a grid is dynamically constructed in the interval and all the nodes of the grid are considered as candidate segment points for an optimization process. Thirdly, the optimization model of each feature curve is constructed on the basis of boundary constraints. Selection of the optimal segment point is related to two factors: the number of control points of B-spline and the total approximation error of all collected points to the fitted curves. Numerical experiments were conducted and the results demonstrate the efficacy of our method in capturing design intent and in industrial applications, compared to existing methods.