The dynamic stability of a quadruped robot trotting on slope was analyzed.Compared with crawl gait,trot gait can improve walking speed of quadruped robots.When a quadruped robot trots,each leg is in the alternate state of swing phase or supporting phase,and two legs in the diagonal line are in the same phase.The feet in the supporting phase form a supporting region on the ground.When a quadruped robot walks on slope,the vertical distance from zero moment point(ZMP) to the supporting diagonal line is defined as ZMP offset distance.Whether this distance is less than the maximum offset distance or not,the stability of robot trotting on slope can be judged.The foot trajectory was planned with the sinusoidal function.Based on the kinematic analysis,the ZMP offset distance of quadruped robot under different slope angles,step length and step height was calculated,then the reasonable slope angle,step length and step height for quadruped robot trotting on slope to keep dynamic stability can be determined.On the other hand,the posture angle of quadruped robot should be controlled within the desired range.Computer simulations were executed to verify the theoretical analysis.The study will provide reference for determining reasonable step parameters of the quadruped robot.
A novel wheel-track hybrid mobile robot with many movement patterns is designed.According to different environments,it can switch between the pure wheel pattern and the pure track one.According to a homogeneous coordinate transformation matrix,gravity stability and its obstacle performance are analyzed.Its gravity equation and climbing obstacle conditions are established.Experimental results show that this hybrid mobile robot could fully possess the advantages of both the wheel and the track mechanisms and achieve a good obstacle climbing capability.
In order to overcome the inherent oscillation problem of potential field methods(PFMs) for autonomous mobile robots in the presence of obstacles and in narrow passages,an enhanced potential field method that integrates Levenberg-Marquardt(L-M) algorithm and k-trajectory algorithm into the basic PFMs is proposed and simulated.At first,the mobile robot navigation function based on the basic PFMs is established by choosing Gaussian model.Then,the oscillation problem of the navigation function is investigated when a mobile robot nears obstacles and passes through a long and narrow passage,which can cause large computation cost and system instability.At last,the L-M algorithm is adopted to modify the search direction of the navigation function for alleviating the oscillation,while the k-trajectory algorithm is applied to further smooth trajectories.By a series of comparative experiments,the use of the L-M algorithm and k-trajectory algorithm can greatly improve the system performance with the advantages of reducing task completion time and achieving smooth trajectories.
Due to the interrelationship between the base placement of the manipulator and its operation object,it is significant to analyze the accessibility and workspace of manipulators for the optimization of their base location.A new method is presented to optimize the base placement of manipulators through motion planning optimization and location optimization in the feasible area for manipulators.Firstly,research problems and contents are outlined.And then the feasible area for the manipulator base installation is discussed.Next,index depended on the joint movements and used to evaluate the kinematic performance of manipulators is defined.Although the mentioned indices in last section are regarded as the cost function of the latter,rapidly-exploring random tree(RRT) and rapidly-exploring random tree*(RRT*) algorithms are analyzed.And then,the proposed optimization method of manipulator base placement is studied by means of simulation research based on kinematic performance criteria.Finally,the conclusions could be proved effective from the simulation results.
A robust visual servoing system is investigated on a humanoid robot which grasps a brush in Chinese calligraphy task.The system is implemented based on uncalibrated visual servoing controller utilizing Kalman-Bucy filter,with the help of an object detector by continuously adaptive MeanShift(CAMShift) algorithm.Under this control scheme,a humanoid robot can satisfactorily grasp a brush without system modeling.The proposed method is shown to be robust and effective through a Chinese calligraphy task on a NAO robot.
Mobile robot systems performing simultaneous localization and mapping(SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian measurement noise,a robust SLAM algorithm is proposed.It is based on the square-root cubature Kalman filter equipped with a Huber’ s generalized maximum likelihood estimator(GM-estimator).In particular,the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update,the measurement update and the new landmark initialization stages of the SLAM.Moreover,gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber’ s technique in the measurement update step.The measurement outliers are suppressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve better performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.
In order to improve the scheduling efficiency of photolithography,bottleneck process of wafer fabrications in the semiconductor industry,an effective estimation of distribution algorithm is proposed for scheduling problems of parallel litho machines with reticle constraints,where multiple reticles are available for each reticle type.First,the scheduling problem domain of parallel litho machines is described with reticle constraints and mathematical programming formulations are put forward with the objective of minimizing total weighted completion time.Second,estimation of distribution algorithm is developed with a decoding scheme specially designed to deal with the reticle constraints.Third,an insert-based local search with the first move strategy is introduced to enhance the local exploitation ability of the algorithm.Finally,simulation experiments and analysis demonstrate the effectiveness of the proposed algorithm.更多还原
Stope mining design is a very important and complicated task in daily production design and technical management of an underground mine.Based on workface technology and human-computer interaction technology,this study introduces a method of 3D parametric design for the irregular structure of stope bottoms,and focuses on solving technical problems in surface modeling of stope bottom structure.Optimization of the minimum span length algorithm(MSLA) and the shortest path search algorithm(SPSA) is conducted to solve the problem of contour-line based instant modeling of stope bottom structures,which makes possible the 3D parametric design for irregular structure of stope bottom.Implementation process and relevant methods of the proposed algorithms are also presented.Feasibility and reliability of the proposed modeling method are testified in a case study.In practice,the proposed 3 D parameterization design method for irregular structure stope bottom proves to be very helpful to precise 3D parametric design.This method is capable of contributing to improved efficiency and precision of stope design,and is worthy of promotion.
Intensity of cavitation is significant in ultrasonic wastewater treatment,but is complicated to measure.A time difference based method of ultrasonic cavitation measurement is proposed.The time differences at different powers of 495 kHz ultrasonic are measured in experiment in comparison with conductimetric method.Simulation results show that time difference and electrical conductivity are both approximately positive proportional to the ultrasonic power.The degradation of PNP solution verifies the availability in wastewater treatment by using ultrasonic.
Video sensors and agricultural IoT(internet of things) have been widely used in the informationalized orchards.In order to realize intelligent-unattended early warning for disease-pest,this paper presents convolutional neural network(CNN) early warning for apple skin lesion image,which is real-time acquired by infrared video sensor.More specifically,as to skin lesion image,a suite of processing methods is devised to simulate the disturbance of variable orientation and light condition which occurs in orchards.It designs a method to recognize apple pathologic images based on CNN,and formulates a self-adaptive momentum rule to update CNN parameters.For example,a series of experiments are carried out on the recognition of fruit lesion image of apple trees for early warning.The results demonstrate that compared with the shallow learning algorithms and other involved,wellknown deep learning methods,the recognition accuracy of the proposal is up to 96.08%,with a fairly quick convergence,and it also presents satisfying smoothness and stableness after convergence.In addition,statistics on different benchmark datasets prove that it is fairly effective to other image patterns concerned.
Electromechanical dynamics analysis and simulation on a rollforming equipment with both sides variable cross-section are discussed in this study.The system includes mechanical parts and electromagnetism parts,and it is a strongly coupled electromechanical system.Based on a virtual work principle and given power,generalized forces of this system are obtained.By using Lagrange-Maxwell equations,a model of electromechanical dynamics is established.Differential equations of two-phase winding on d-q axis are obtained by Park transformation,which comes from three-phase winding equations on the A-B-C axis.This system is solved with the 4th order Runge-Kutta’s method,and discrete solutions of all variables are obtained.Finally,by using Matlab language,the system is simulated.The results show that the proposed method works very well.
Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals’ correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.
Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.
Massive MIMO systems have got extraordinary spectral efficiency using a large number of base station antennas,but it is in the challenge of pilot contamination using the aligned pilots.To address this issue,a selective transmission is proposed using time-shifted pilots with cell grouping,where the strong interfering users in downlink transmission cells are temporally stopped during the pilots transmission in uplink cells.Based on the spatial characteristics of physical channel models,the strong interfering users are selected to minimize the inter-cell interference and the cell grouping is designed to have less temporally stopped users within a smaller area.Furthermore,a Kalman estimator is proposed to reduce the unexpected effect of residual interferences in channel estimation,which exploits both the spatial-time correlation of channels and the share of the interference information.The numerical results show that our scheme significantly improves the channel estimation accuracy and the data rates.
Due to more tag-collisions result in failed transmissions,tag anti-collision is a very vital issue in the radio frequency identification(RFID) system.However,so far decreases in communication time and increases in throughput are very limited.In order to solve these problems,this paper presents a novel tag anti-collision scheme,namely adaptive hybrid search tree(AHST),by combining two algorithms of the adaptive binary-tree disassembly(ABD) and the combination query tree(CQT),in which ABD has superior tag identification velocity and CQT has optimum performance in system throughput and search timeslots.From the theoretical analysis and numerical simulations,the proposed algorithm can colligate the advantages of above algorithms,improve the system throughput and reduce the searching timeslots dramatically.