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.

Adaptive Sliding Control of Six-DOF Flight Simulator Motion Platform

There is proposed an adaptive sliding controller in task space on the base of the linear Newton-Euler dynamic equation of motion platform in a six-DOF flight simulator. The uncertain parameters are divided into two groups： the constant and the time-varying. The controller identifies constant uncertain parameters using nonlinear adaptive controller associated with elimination of the influences of time-varying uncertain parameters and compensation of the external disturbance using sliding control. The results of numerical simulation attest to the capability of this control scheme not only to, with deadly accuracy, identify parameters of motion platform such as load, inertia moments and mass center, but also effectively improve the robustness of the system.

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 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.

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.

Dynamics and adaptive control of a dual-arm space robot with closed-loop constraints and uncertain inertial parameters

A dynamics-based adaptive control approach is proposed for a planar dual-arm space robot in the presence of closed-loop constraints and uncertain inertial parameters of the payload. The controller is capable of controlling the po- sition and attitude of both the satellite base and the payload grasped by the manipulator end effectors. The equations of motion in reduced-order form for the constrained system are derived by incorporating the constraint equations in terms of accelerations into Kane＇s equations of the unconstrained system. Model analysis shows that the resulting equations perfectly meet the requirement of adaptive controller design. Consequently, by using an indirect approach, an adaptive control scheme is proposed to accomplish position/attitude trajectory tracking control with the uncertain parameters be- ing estimated on-line. The actuator redundancy due to the closed-loop constraints is utilized to minimize a weighted norm of the joint torques. Global asymptotic stability is proven by using Lyapunov＇s method, and simulation results are also presented to demonstrate the effectiveness of the proposed approach.

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 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 neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

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.

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 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.

Simulation of a ground-layer adaptive optics system for the Kunlun Dark Universe Survey Telescope

Ground Layer Adaptive Optics （GLAO） is a recently developed technique extensively applied to ground-based telescopes, which mainly compensates for the wavefront errors induced by ground-layer turbulence to get an appropriate point spread function in a wide field of view. The compensation results mainly depend on the turbu-lence distribution. The atmospheric turbulence at Dome A in the Antarctic is mainly distributed below 15 meters, which is an ideal site for applications of GLAO. The GLAO system has been simulated for the Kunlun Dark Universe Survey Telescope, which will be set up at Dome A, and uses a rotating mirror to generate several laser guide stars and a wavefront sensor with a wide field of view to sequentially measure the wavefronts from different laser guide stars. The system is simulated on a computer and parameters of the system are given, which provide detailed information about the design of a practical GLAO system.

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.

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

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Motion connectivity-based initial video object extraction

In order to obtain the initial video objects from the video sequences, an improved initial video object extraction algorithm based on motion connectivity is proposed. Moving objects in video sequences are highly connected and structured, which makes motion connectivity an advanced feature for segmentation. Accordingly, after sharp noise elimination, the cumulated difference image, which exhibits the coherent motion of the moving object, is adaptively thresholded. Then the maximal connected region is labeled, post-processed and output as the final segmenting mask. Hence the initial video object is effectively extracted. Comparative experimental results show that the proposed algorithm extracts the initial video object automatically, promptly and properly, thereby achieving satisfactory subjective and objective performance.

CNN-LSTM车辆运动状态识别的AUKF组合导航方法

针对固定的噪声协方差难以适应车辆不同运动行为下噪声统计特性差异大的问题,提出了一种基于卷积神经网络与长短期记忆网络(CNN-LSTM)的车辆运动状态识别自适应无迹卡尔曼滤波(AUKF)组合导航方法。首先,应用CNN-LSTM网络模型进行车辆运动状态识别,解决车辆自我运动不确定性的问题;其次,将特定运动状态约束下的噪声协方差应用于UKF的时间更新与量测更新;最后,将所提方法在采集的数据集上进行验证。实验结果表明,与经典的UKF算法相比,所提方法的位置均方根误差与速度均方根误差分别下降了22.67%与2.63%,验证了所提方法的有效性。

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method.In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.

A Mode-Switching Motion Control System for Reactive Interaction and Surface Following Using Industrial Robots

This work proposes a sensor-based control system for fully automated object detection and exploration(surface following) with a redundant industrial robot. The control system utilizes both offline and online trajectory planning for reactive interaction with objects of different shapes and color using RGBD vision and proximity/contact sensors feedback where no prior knowledge of the objects is available. The RGB-D sensor is used to collect raw 3D information of the environment. The data is then processed to segment an object of interest in the scene. In order to completely explore the object, a coverage path planning technique is proposed using a dynamic 3D occupancy grid method to generate a primary(offline) trajectory. However, RGB-D sensors are very sensitive to lighting and provide only limited accuracy on the depth measurements. Therefore, the coverage path planning is then further assisted by a real-time adaptive path planning using a fuzzy self-tuning proportional integral derivative(PID)controller. The latter allows the robot to dynamically update the 3D model by a specially designed instrumented compliant wrist and adapt to the surfaces it approaches or touches. A modeswitching scheme is also proposed to efficiently integrate and smoothly switch between the interaction modes under certain conditions. Experimental results using a CRS-F3 manipulator equipped with a custom-built compliant wrist demonstrate the feasibility and performance of the proposed method.