Adaptive Space Expansion for Fast Motion Planning

The sampling process is very inefficient for sam-pling-based motion planning algorithms that excess random sam-ples are generated in the planning space.In this paper,we pro-pose an adaptive space expansion(ASE)approach which belongs to the informed sampling category to improve the sampling effi-ciency for quickly finding a feasible path.The ASE method enlarges the search space gradually and restrains the sampling process in a sequence of small hyper-ellipsoid ring subsets to avoid exploring the unnecessary space.Specifically,for a con-structed small hyper-ellipsoid ring subset,if the algorithm cannot find a feasible path in it,then the subset is expanded.Thus,the ASE method successively does space exploring and space expan-sion until the final path has been found.Besides,we present a particular construction method of the hyper-ellipsoid ring that uniform random samples can be directly generated in it.At last,we present a feasible motion planner BiASE and an asymptoti-cally optimal motion planner BiASE*using the bidirectional exploring method and the ASE strategy.Simulations demon-strate that the computation speed is much faster than that of the state-of-the-art algorithms.The source codes are available at https://github.com/shshlei/ompl.

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm.The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory.The unmodeled dynamics of the system are considered,and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network.The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory.The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.

An Energy Efficient Control Strategy for Electric Vehicle Driven by In-Wheel-Motors Based on Discrete Adaptive Sliding Mode Control

This paper presents an energy-efficient control strategy for electric vehicles(EVs)driven by in-wheel-motors(IWMs)based on discrete adaptive sliding mode control(DASMC).The nonlinear vehicle model,tire model and IWM model are established at first to represent the operation mechanism of the whole system.Based on the modeling,two virtual control variables are used to represent the longitudinal and yaw control efforts to coordinate the vehicle motion control.Then DASMC method is applied to calculate the required total driving torque and yaw moment,which can improve the tracking performance as well as the system robustness.According to the vehicle nonlinear model,the additional yaw moment can be expressed as a function of longitudinal and lateral tire forces.For further control scheme development,a tire force estimator using an unscented Kalman filter is designed to estimate real-time tire forces.On these bases,energy efficient torque allocation method is developed to distribute the total driving torque and differential torque to each IWM,considering the motor energy consumption,the tire slip energy consumption,and the brake energy~?recovery.Simulation results of the proposed control strategy using the co-platform of Matlab/Simulink and CarSim way.

Adaptive Consistent Management to Prevent System Collapse on Shared Object Manipulation in Mixed Reality

A concurrency control mechanism for collaborative work is akey element in a mixed reality environment. However, conventional lockingmechanisms restrict potential tasks or the support of non-owners, thusincreasing the working time because of waiting to avoid conflicts. Herein, wepropose an adaptive concurrency control approach that can reduce conflictsand work time. We classify shared object manipulation in mixed reality intodetailed goals and tasks. Then, we model the relationships among goal,task, and ownership. As the collaborative work progresses, the proposedsystem adapts the different concurrency control mechanisms of shared objectmanipulation according to the modeling of goal–task–ownership. With theproposed concurrency control scheme, users can hold shared objects andmove and rotate together in a mixed reality environment similar to realindustrial sites. Additionally, this system provides MS Hololens and Myosensors to recognize inputs from a user and provides results in a mixed realityenvironment. The proposed method is applied to install an air conditioneras a case study. Experimental results and user studies show that, comparedwith the conventional approach, the proposed method reduced the number ofconflicts, waiting time, and total working time.

Adaptive deinterlacing algorithm based on motion compensation

An adaptive de-interlacing algorithm based on motion compensation is presented. It consists of the detection of motion blocks, the adaptive motion estimation with Kalman filtering, and the motion compensation for motion blocks and field repetition for static blocks. The detection of motion blocks can accurately identify the motion blocks by using successive 4-field images. The motion estimation module with Kalman filtering searches motion vectors only for motion blocks, and the search model is adaptive to motion velocity and acceleration. Two de-interlacing methods are adopted to satisfy the different requirements of motion blocks and static blocks. Compared with full search algorithm, the proposed algorithm greatly reduces the computational amount while keeping the performance approximately.

Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.

A Hybrid Motion Compensation De-interlacing Approach

Motion compensation de interlacing is expected to be better than linear techniques; but all the block based motion compensation de interlacing methods cause block artifacts. The algorithm proposed in this paper is concerned with reducing the deficiency of motion compensated interpolation by using adaptive hybrid de interlacing methods. A spatio temporal tensor based approach is used to get more accurate motion field for de interlacing. Motion vector is assigned for each position with pixel precision; the block artifact is reduced significantly. To deal with the artifacts introduced by motion compensation when the motion estimation is incorrect, linear techniques are considered by adaptive weighting. Furthermore, directional filter is adapted to preserve details and the edge discontinuity could be eliminated greatly. Our approach is robust to incorrect motion vector estimation.

Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction

Human Adaptive Mechatronics(HAM)includes human and computer system in a closed loop.Elderly person with disabilities,normally carry out their daily routines with some assistance to move their limbs.With the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist them.The rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity potential.This paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the system.Limb characteristics extraction from EMG signal and prediction of optimal controller parameters are modeled.Time and frequency based approach of EMG signal are considered for feature extraction.The models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are validated.Based on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM system.Finally,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb movements.The proposed model of human adaptive controller predicts the limb movement by 96%accuracy.

A Neuro-inspired Adaptive Motion Detector

In this paper, a novel motion detector is proposed to perceive the weak changes in a image sequence. This is inspired by the mechanism of fixational eye movement and dynamics of vertebrate’s cortex. We realized respectively an artificial model of visual attention selection, called dual-probe adaptive model (DPAM), and an active tremor operation (ATO) approach. It is found that between them there exists a resonance phenomenon. The phenomenon is enhanced when the ATO and the DPAM are in-phase and is suppressed when they are anti-phase.?Based on this, we construct a novel motion detector combined by the ATO and the DPAM to resonate with the motion direction. This allows capturing moving edges even in the image sequences with lighting change and noisy background. Simulation and Experimental results demonstrate the effectiveness.

Compliance motion control of the hydraulic dual-arm manipulator with adaptive mass estimation of unknown object

Given the limited operating ability of a single robotic arm,dual-arm collaborative operations have become increasingly prominent.Compared with the electrically driven dual-arm manipulator,due to the unknown heavy load,difficulty in measuring contact forces,and control complexity during the closed-chain object transportation task,the hydraulic dual-arm manipulator(HDM)faces more difficulty in accurately tracking the desired motion trajectory,which may cause object deformation or even breakage.To overcome this problem,a compliance motion control method is proposed in this paper for the HDM.The mass parameter of the unknown object is obtained by using an adaptive method based on velocity error.Due to the difficulty in obtaining the actual internal force of the object,the pressure signal from the pressure sensor of the hydraulic system is used to estimate the contact force at the end-effector(EE)of two hydraulic manipulators(HMs).Further,the estimated contact force is used to calculate the actual internal force on the object.Then,a compliance motion controller is designed for HDM closed-chain collaboration.The position and internal force errors of the object are reduced by the feedback of the position,velocity,and internal force errors of the object to achieve the effect of the compliance motion of the HDM,i.e.,to reduce the motion error and internal force of the object.The required velocity and force at the EE of the two HMs,including the position and internal force errors of the object,are inputted into separate position controllers.In addition,the position controllers of the two individual HMs are designed to enable precise motion control by using the virtual decomposition control method.Finally,comparative experiments are carried out on a hydraulic dual-arm test bench.The proposed method is validated by the experimental results,which demonstrate improved object position accuracy and reduced internal force.

Robust Adaptive Neural Network Control for XY Table

This paper proposed a robust adaptive neural network control for an XY table. The XY table composes of two AC servo drives controlled independently. The neural network with radial basis function is employed for velocity and position tracking control of AC servo drives to improve the system’s dynamic performance and precision. A robust adaptive term is applied to overcome the external disturbances. The stability and the convergence of the system are proved by Lyapunov theory. The proposed controller is implemented in a DSP-based motion board. The validity and robustness of the controller are verified through experimental results.

A Tilt-correction Adaptive Optical System for the Solar Telescope of Nanjing University

A tilt-correction adaptive optical system installed on the 430 mm Solar Telescope of Nanjing University has been put in operation. It consists of a tip-tilt mirror, a correlation tracker and an imaging CCD camera. An absolute difference algorithm is used for detecting image motion in the correlation tracker. The sampling frequency of the system is 419 Hz. We give a description of the system's configuration, an analysis of its performance and a report of our observational results. A residual jitter of 0.14 arcsec has been achieved. The error rejection bandwidth of the system can be adjusted in the range 5-28 Hz according to the beacon size and the strength of atmospheric turbulence.

Robot Interaction Force Estimation Using an Adaptive Sliding Mode Observer

In constrained motion control of a robot,the interaction force is an important variable,which directly describes the state of interaction.It is required in a number of algorithms for interaction control.Desirably,the interaction force has to be measured by force sensors.However,there are inherent limitations with force sensors,such as the cost,sensing noise,limited bandwidth,and the difficulty of physical location at the required place,which is dynamic.In the present paper,the interaction force is estimated by using high order sliding mode observers.An adaptive version of a high order sliding mode observer is developed to robustly reconstruct the interaction force.Experimental results are given to show the effectiveness of the developed algorithms.

BLOCK ADAPTIVE RECURSIVE ALGORITHM FOR VIDEO CONFERENCE CODING

This paper presents a new motion estimation algorithm for video conference signal coding. This type of algorithm is called block adaptive recursive algorithm (BARA). Simulation results show that this new algorithm has better performance than conventional ones.

A SELF－ADAPTIVE FINITE ELEMENT METHOD FOR SOLVING 2－D EULER EQUATIONS ON THE UNSTRUCTURED MESHES

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High-elevation Adaptation of Motion Visual Display Modifications in the Toad-Headed Agamid Lizards(Phrynocephalus)

Understanding the process of adaptation is a key mission in modern evolutionary biology.Animals living at high elevations face challenges in energy meta bolism due to several environmental constraints(e.g., oxygen supply, food availa bility,and movement time). Animal behavioral processes are intimately related to energy meta bolism, and therefore, behavioral modifica tions are expected to be an important mechanism for high-elevation adaptation. We tested this behavioral adaptation hypothesis using va ria tions of motion visual displays in toad-headed agamid lizards of the genus Phr ynocephalus. We predicted tha t complexity of visual motion displays would decrease with the increase of elevation, because motion visual displays are energetically costly. Displays of 12 Phr ynocephalus species were collected with elevations ranging from sea level to 4600 m. We quantified display complexity using the number of display components, display duration, pathways of display components, as well as display speed for each species. Association between display complexity and elevation was analyzed using the phylogenetic generalized least squares(PGLS)model. We found that both the number of display components and the average value of tail coil speed were negatively correlated with elevation, suggesting that toad-headed lizards living at high-elevation areas reduced their display complexity to cope with the environmental constraints. Our research provides direct evidence for high-elevation adaptation from a behavioral aspect and illustrates the potential impacts of environment heterogeneity on motion visual display diversification.

A Restricted, Adaptive Threshold Segmentation Approach for Processing High-Speed Image Sequences of the Glottis

In this paper, we propose a restricted, adaptive threshold approach for the segmentation of images of the glottis acquired from high speed video-endoscopy (HSV). The approach involves first, identifying a region of interest (ROI) that encloses the vocal-fold motion extent for each image frame as estimated by the different image sequences. This procedure is then followed by threshold segmentation restricted within the identified ROI for each image frame of the original image sequences, or referred to as sub-image sequences. The threshold value is adapted for each sub-image frame and determined by respective minimum gray-scale value that typically corresponds to a spatial location within the glottis. The proposed approach is practical and highly efficient for segmenting a vast amount of image frames since simple threshold method is adapted. Results obtained from the segmentation of representative clinical image sequences are presented to verify the proposed method.

Adaptive Fuzzy Control of Constrained Flexible-Link Manipulators

In this paper, a novel adaptive fuzzy control scheme is presented. The controller is constructed by using a table lookup scheme and self tuning techniques, which includes the identification block, the fuzzification, the updating rule base, the defuzzification, and the crisp controller (sub controller), etc. The adaptive fuzzy controller is designed in detail by means of a triangular membership function and the center of gravity method. The control scheme addressed here is implemented to control the motion of the end effector of a two link constrained flexible manipulator. Computer simulation results show that the novel adaptive fuzzy control scheme works quite well.

Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences

In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.

Adaptive DT Mesh Points Tracking——A Novel Sequential Image Inter-frame Predictive Scheme

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