Low Degrees Of Freedom Research Articles

Improved A* Navigation Path-Planning Algorithm Based on Hexagonal Grid

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements.

Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.

Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

Multi-Objective Optimization of a Three Degree-of-Freedom Translational Parallel Kinematic Machine with Coplanar Rails

Several advancements in the field of parallel manipulators have taken place in recent days as they offer many advantages over serial manipulators in terms of accuracy, agility, stiffness, speed, etc. The Parallel Kinematic Machines (PKMs) with lower Degree of Freedom (DoF) joints are being explored for a variety of industrial applications and, in particular, machining applications as these offer more accuracy, high machining capability, and more stiffness. This research work focuses on the modeling, kinematics, workspace and dexterity analyses of a 3DoF Translational PKM having coplanar rails along the Cartesian axes: -X, +X, +Y. Actuation of sliders, independently along the respective rails, offer the tool platform pure translational motion. Fixed length links are used to connect the sliders and tool platform. The PKM under study is modeled in CATIA. Inverse kinematics and workspace analysis are carried out using the performance indices, namely, Workspace Volume Index (WVI) and Global Condition Index (GCI). Attempts are also made to find the optimal dimensions of the PKM through multi-objective optimization using Genetic Algorithms in MATLAB. The methodology presented is helpful to predict the PKM's performance capability while the results obtained are helpful for the development of a physical prototype necessary for further experimental investigations.

Development and improvement of a method for determining the worst-case typhoon path for storm surge deviation through Bayesian optimization

A search for the worst-case typhoon path for storm surge is conducted to facilitate storm surge disaster prevention. A widely used method today is grid search, which is an exhaustive method in which the reference paths are paralleled and rotated in small steps, and storm surge simulations are performed for each path. However, the computational cost is enormous, and adequate typhoon paths may not be considered. Therefore, our goal is to propose a search method that can find the worst-case typhoon path at a lower cost. We have proposed a search method based on Bayesian optimization that is more efficient than grid search. However, issues such as the criteria for terminating the search and the complexity level of the function that can be applied remain unaddressed. Here, we examined the conditions for terminating the search using Bayesian optimization and the conditions for its application to the shape of the function. The results showed that Bayesian optimization can stably identify more dangerous typhoon paths than that found by grid search at a lower cost. We also confirmed that the search works for a wide range of function shapes. In addition, we searched for the worst-case typhoon path in real sea under the degrees of freedom that are too large to be searched by grid search in terms of computational cost. Consequently, a path approximately 5.5% larger than that found by grid search under low degrees of freedom was found. This means that the current search method underestimates the worst-case situation. The results confirm the usefulness of the search method using Bayesian optimization developed.

Parallel Complementary Virtual Arrays Algorithm for Direction of Arrival (DOA) Estimation

This Paper discusses the challenges faced by previous method 2D Direction of Arrival (DOA) systems, such as low degrees of freedom, poor resolution, and significant estimation errors in scenarios with small snapshots. In response to these issues, the present method proposes a low-complexity 2D Direction of Arrival (DOA) estimation algorithm based on a parallel complementary virtual array.
 The algorithm utilizes two mutually parallel complementary linear arrays to generate a virtual array, addressing the limitations of traditional parallel arrays. It constructs an extended matrix with enhanced 2D angular degrees of freedom using covariance and cross-covariance matrices. The final step involves obtaining automatic matching 2D angle estimates through Singular Value Decomposition (SVD) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT).
 In comparison to traditional 2D DOA estimation methods, the proposed algorithm better exploits the information from the array's received data. It can identify more incoming signals, offering high resolution without the need for 2D linear search or angle parameter matching. Importantly, it demonstrates effective estimation even in scenarios with low Signal-to-Noise Ratio (SNR) and small snapshots. Experimental simulation results validate the effectiveness and reliability of the proposed algorithm.

Octopus-Inspired Robotic Arm Powered by Shape Memory Alloys (SMA)

Traditional rigid grippers that are used for underwater systems lack flexibility and have lower degrees of freedom. These systems might damage the underwater environment while conducting data acquisition and data sampling. Soft robotics, which is mainly focused on creating robots with extremely soft materials are more delicate for the grasping of objects underwater. These systems tend to damage the underwater ecosystem in the least possible way. In this paper, we have presented a simplified design of a soft arm inspired by the octopus arm actuated by coiled Shape Memory Alloys (SMAs) using completely flexible lightweight material. The characterization arm performance under various load and input current conditions is shown. We hope this work will serve as a basis for the future of underwater grasping utilizing soft robotics.

Generalised two‐level nested multiple‐input multiple‐output radar direction of arrival estimation with high degrees of freedom and low mutual coupling

AbstractTo address the problems of traditional two‐level nested arrays (TTNA) with low degrees of freedom and high mutual coupling, a generalised two‐level nested MIMO radar array with high degrees of freedom and low mutual coupling for the direction of arrival (DOA) estimation is proposed. First, two coprime expansion factors are introduced in the TTNA to mitigate mutual coupling among array elements and enhance its degrees of freedom. Secondly, the range of consecutive lags and the total number of virtual array elements are derived in closed form under the structures of ‘sum–difference co‐array’. Finally, utilising the atomic norm minimisation theory, the authors address the issue of discrete holes caused by different coprime expansion factors through filling in virtual arrays at discontinuities. Furthermore, a convex optimisation model to recover equivalent received signals from these filled virtual arrays is established, which is combined with a multiple signal classification algorithm for DOA estimation. Simulation results validate the rationality of the array structure and demonstrate the effectiveness of the algorithm.

Modeling Uncertainty Quantification of NDVI of Agricultural Fields through Bayesian Linear Regression in Time Series Prediction

The current research discusses the applications of Bayesian linear regression to predict the uncertainty of remote sensing data. To predict the uncertainty, the study considered the SENTINEL-2 satellite data of agricultural fields of Uttar Pradesh state of India. Using the stratified sampling method in Google Earth Engine, the random points generated are mapped to agricultural fields. Data was collected in the form of maximum Normalized Difference Vegetation Index (NDVI) values of each agricultural field. The dynamics of the time series predictions were explored with Bayesian linear regression, a probabilistic deep learning method. The model uncertainty defined as epistemic uncertainty is evaluated with the prior and posterior probability parameters of Bayesian statistics in linear regression. The number of regression lines predicted for the same data shows evidence of uncertainty. The Bayesian linear regression models show evidence of high uncertainty for the predicted NDVI values. The variation in model uncertainty is measured by dividing the dataset into samples and it is observed that with increase in data the uncertainty is reduced. Also, with the increase in data, the posterior density becomes sharper which corresponds to a decrease in variance. Further, the study extended the concept of regression analysis with Gaussian basis functions to determine the effect of model uncertainty with an increase in data. The analysis has shown the same result in knowing the effect of uncertainty with the increase in data. Further, a nonlinear polynomial regression model with a Gaussian distribution as a basis function was developed to evaluate the marginal probabilities of the evidence function in capturing the uncertainty with varying degrees of freedom. The polynomial regression with a Gaussian distribution using Bayesian statistics has captured the uncertainty and confirmed that the uncertainty is captured at lower degrees of freedom.

The Effect of the Degree of Freedom and Weight of the Hand Exoskeleton on Joint Mobility Function

This study aims to investigate the effects of the degree of freedom (DOF) and weight of the hand exoskeleton (HE) on hand joint mobility function (ease of movement, movement range) in fine hand use activities. A three-digit passive HE prototype was built to fit each of the 12 participants. Two DOF setups (three DOF, two DOF), two digits’ weight levels (70 g, 140 g), and barehand conditions were tested. A productivity task (performed with Standardized-Nine Hole Peg Test) and motion tasks, both performing the tip pinch and tripod pinch, were conducted to measure the task completion time and the range of motion (ROM) of the digit joints, respectively, using a motion capture system. The perceived ease rating was also measured. The results showed that DOF reduction and weight addition caused a significant task completion time increase and rating drop (p < 0.05). Meanwhile, the DOF reduction increased the ROM reduction of the proximal interphalangeal joints; however, the weight addition caused a correction of the ROM reduction of several joints (p < 0.05) at the tripod pinch. In conclusion, wearing an HE reduces hand joint mobility, especially in lower DOF. However, a certain weight addition may improve joint mobility in terms of the fingers’ movement range.

A STAP clutter suppression algorithm based on sparse representation for small-array HFSWR

ABSTRACT In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter.

Generalized and symmetric coprime planar array for direction of arrival estimation

Degrees of freedom and mutual coupling are two important factors to determine the ability of direction of arrival (DOA) estimation. Although coprime planar array (CPPA) has less mutual coupling than hourglass array (HA) and ladder array (LA), its positive and negative covariance corresponding lags are discontinuous, resulting in low degrees of freedom. In this study, we propose a generalized and symmetric coprime planar array (GSCA) by employing the symmetric property of the difference co-array. By moving one subarray and introducing a supplementary subarray, we construct a connected virtual uniform rectangular array (URA). Through further optimizing the sensor number, the redundant lags are reduced and the array structure is more general. We show that the degrees of freedom are much higher than those of CPPA, HA and LA. Besides, the mutual coupling is close to CPPA, but less than HA and LA. The superiority of GSCA is verified by simulations.

Multistep collocation method for Fredholm integral equations of the second kind

In this paper a Multistep Collocation (MC) method for the second kind Fredholm integral equations (FIEs) is proposed and analyzed. The multistep collocation method is applied to FIE with smooth kernels under uniform mesh and weakly singular kernels |s−t|−α(0<α<1) using a graded mesh then the same convergence rate as collocation method but with a lower degree of freedom is obtained. Moreover, in order to avoid the round-off errors caused by graded mesh, a Hybrid Multistep Collocation (HMC) method by combining multistep collocation and hybrid collocation method is proposed. The HMC method converges faster with lower degrees of freedom and more efficiently captures the weakly singular properties by nonpolynomial interpolation at the first subinterval. The L∞-norm convergence results are analysed and proved. Numerical examples are presented to demonstrate the efficiency of the proposed methods.

Comprehensive Investigation on the Global Mode Functions of Bidirectional Flexible Jointed-Panel Structures

Flexible jointed-panel structures play a significant role in the entire service cycle of spacecraft, but they also bring serious vibration problems to the spacecraft orbit control. The bidirectional flexible jointed-panel structure installed on the large-scale spacecraft considered in this paper may produce large-amplitude vibration. To investigate the nonlinear vibration of such a flexible structure, a nonlinear dynamic model with a lower degree of freedom should be established analytically. Thus, the global mode functions of such a complex structure should be extracted first. Taking the eigenfunctions of beams as basis functions, the Rayleigh–Ritz method is developed to extract the analytic global mode functions for bidirectional flexible jointed-panel structures. As a typical example, the cruciform flexible jointed-panel structures made up of six isotropic rectangular panels are investigated, and the dynamic model for the transverse vibration of the structure is established. Numerical results are conducted to illustrate the validation of the proposed modeling process by comparing the natural frequency and global modes obtained by the developed approach with those obtained from the finite element method. Then, the influence of parameters on the inherent properties of the structure is discussed, and an effective method to increase the fundamental frequency of the structure is proposed.

Influence of suspension parameter for derailment analysis of a full railway vehicle model cruising on a curved track

The main intention of this research work is to study the derailment response of high speed railway vehicle (HSRV) cruising on a curved track. In previous research work, lower degree of freedom (DOF) has been considered for the derailment analysis which may not give more accurate results. Hence, a 31 DOF bondgraph model of HSRV has been developed which consist of carbody, two truck frames and two selfsame wheelsets for each truck frame. Vertical, lateral, roll, yaw and pitch motion are considered for carbody and bogie and except pitch motion all the other motion are considered for wheelsets. Non-linearities in terms of heuristic nonlinear creep model and flange contact has been employed to simulate the derailment response at high speed. The effect of vehicle speed running on a curved track was investigated for derailment quotient. The main aim of present research work to evaluate derailment quotient at the speed range of 150 kmph to 600 kmph for hard and soft suspension parameter. Derailment quotient has been calculated for both linear and nonlinear creep models and it is seen that DQ for linear model has a lower value compare to non linear creep. The major advantages of the proposed model are that, the presented model can actively predict the derailment of a railway vehicle, and also precisely determine the nonlinear critical hunting speeds.

DOA Estimation of an Enhanced Generalized Nested Array with Increased Degrees of Freedom and Reduced Mutual Coupling

Aiming at low degrees of freedom (DOF) and high mutual coupling (MC) of the existing sparse arrays, an enhanced generalized nested array (EGNA) is proposed in this paper. Specifically, the proposed array adds a single antenna on the basis of generalized nested array (GNA), and the difference of coprime factors is employed as the spacing between the second subarray and the additional antenna. Then, the values of the coprime factors are analyzed in detail, which indicates that Yang-NA can be explained as a special case. Compared with the majority of the existing sparse arrays, EGNA not only has the closed-form expressions of the physical antenna locations, consecutive lags, and unique lags, but also significantly increases DOF and reduces MC. In view of the above advantages, EGNA can obtain superior performance in direction of arrival (DOA) estimation. Numerical simulation results verify the rationality and superiority of the proposed nested array.

Estimation of the thermal properties of an historic building wall by combining modal identification method and optimal experiment design

The estimation of wall thermal properties by in situ measurement enables to increase the reliability of the model predictions for building energy efficiency. Nevertheless, retrieving the unknown parameters has an important computational cost. Indeed, several computations of the heat transfer problem are required to identify these thermal properties. To handle this drawback, an innovative approach is investigated. The first step is to search the optimal experiment design among the sequence of observation of several months. A reduced sequence of observations of three days is identified which guarantees to estimate the parameter with the maximum accuracy. Moreover, the inverse problem is only solved for this short sequence. To decrease further the computational efforts, a reduced order model based on the modal identification method is employed. This a posteriori model reduction method approximates the solution with a lower degree of freedom. The whole methodology is illustrated to estimate the thermal diffusivity of an historical building that has been monitored with temperature sensors for several months. The computational efforts is cut by five. The estimated parameter improves the reliability of the predictions of the wall thermal efficiency.

A finite element model for static analysis of curved thin-walled beams based on the concept of equivalent layered composite cross section

In this study, an accurate 1D finite element model with low degrees of freedom (DOFs) is presented for the static extensional-shearing-bending analysis of curved thin-walled beams. In order to incorporate the nonclassical effects like transverse shear flexibility in the formulation, the concept of equivalent layered composite cross section (ELCS) is employed. Based on this new concept, the cross section of a thin-walled beam is replaced with an equivalent layered composite one. A global–local layered beam theory is employed to describe the displacement fields of the curved beams. A curved beam finite element with three nodes and 13 DOFs is developed for the approximation of the unknown variables of the displacement fields. For validation of the proposed model, the results of 1 D models of other researchers as well as the results of the 2D/3D finite element simulations (ABAQUS) are employed. Needlessness to the shear correction factor, satisfaction of zero-conditions of shear stresses on the upper and lower planes of the curved beam, high accuracy in the prediction of structural responses, and significant reduction in the time of computations are some of distinct features of the proposed finite element model.

A new hyperbolic-polynomial higher-order elasticity theory for mechanics of thick FGM beams with imperfection in the material composition

A drawback to the material composition of thick functionally graded materials (FGM) beams is checked out in this research in conjunction with a novel hyperbolic-polynomial higher-order elasticity beam theory (HPET). The proposed beam model consists of a novel shape function for the distribution of shear stress deformation in the transverse coordinate. The beam theory also incorporates the stretching effect to present an indirect effect of thickness variations. As a result of compounding the proposed beam model in linear Lagrangian strains and variational of energy, the system of equations is obtained. The Galerkin method is here expanded for several edge conditions to obtain elastic critical buckling values. First, the importance of the higher-order beam theory, as well as stretching effect, is assessed in assorted tabulated comparisons. Next, with validations based on the existing and open literature, the proposed shape function is evaluated to consider the desired accuracy. Some comparative graphs by means of well-known shape functions are plotted. These comparisons reveal a very good compliance. In the final section of the paper, based on an inappropriate mixture of the SUS304 and Si3C4 as the first type of FGM beam (Beam-I) and, Al and Al2O3 as the second type (Beam-II), the results are pictured while the beam is kept in four states, clamped–clamped (C–C), pinned–pinned (S–S), clamped-pinned (C–S) and in particular cantilever (C–F). We found that the defect impresses markedly an FGM beam with boundary conditions with lower degrees of freedom.

FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

In recent times, autonomous robots have become more relevant, aiming not only to be an extension of mobility and human performance but also allowing them to independently solve specific problems such as finding free-collision paths within some defined environments. In order to achieve this, several techniques have been developed, like action-reaction algorithms, sampling-based algorithms, and deterministic algorithms such as the Homotopy Path Planning Method (HPPM). This work presents, for the first time, a complete deterministic collision-free path planning scheme implemented in FPGA, which is mounted on a Scribbler 2 robot from Parallax. Then, an automatic algorithm of the repulsion parameter for the HPPM method is presented, using as a reference the minimum distance between the center of each obstacle with respect to the homotopic ideal path; furthermore, an algorithm is proposed for discriminating dead-end routes and collision risk trajectories, which allows us to obtain a feasible free-collision path that takes into account the robot dimensions. Besides, comparative performance tests have been carried out against other path-finding methods from the low degrees of freedom (low DoF) and sampling-based planners. Our proposal exhibits path calculation times which are 5 to 10 times faster on FPGA implementation, compared to the other methods and 10 to 100 times faster on PC implementation also compared to the rest. Similar results are obtained with regards to memory consumption, namely 20 to 200 times lower on FPGA implementation and 10 to 100 times lower on PC implementation.

New Insights into the Pre-Concentration of Alunite from a Siliceous ore using Froth Flotation

Alunite is the main non-bauxite source of aluminum. Due to the low Degrees Of Freedom (DOF) of alunite, froth flotation, as a pre-concentration method, is a viable alternative to increase selectivity in downstream processes. The current study was aimed at investigating the pre-concentration of alunite using micro-flotation. In the first step, an optical microscope studies were done to determine DOF of alunite. In the next step, flotation tests were designed. The experiments were performed based on the surface charge differential between alunite and quartz using direct and reverse flotation. In the reverse flotation, Ca+2 and Ba+2 were used to activate the quartz surface. Anionic collector was used to float the gangue minerals. The highest grade and recovery using reverse flotation were 34.96 and 81.33%, respectively. In the direct flotation, the surface of alunite was activated using Ba2+, Ca2+ and F-. The optimum condition was achieved using F- as an alunite activator for particles ranged -37+25 µm with the grade of 31.47%. The grade and recovery were 50 and 89%, respectively. All experiments were conducted in Hallimond Tube. During the experiments, several parameters were controlled.