Rigid formation of multi-robot can not flexibly adjust formation,leading to the phenomenon of chasing blind angle easily,so that the pursuers can not accurately and efficiently complete the chase.To solve this problem,an adaptive rigid structural formation algorithm is proposed.The formation center controller makes the formation center infinitely close to the target.The formation controller is designed to dynamically adjust the formation according to the location of the target.On this basis,the tracker,in accordance with the position of the target and the environmental conditions,combined with the improved rigid structure method to choose the appropriate formation adaptively,so as to complete hunting.Simulation results show that,the tracking time and energy consumption ratio of the algorithm are lower than that of the rigid formation algorithm,and the hunting blind angle can be avoided.

Aiming at the problem that the traditional micromouse maze search algorithm can not adapt to the random mazemap search,a new micromouse walking maze fusion algorithm is proposed.The algorithm uses the probability distance to divide the maze into eight regions,calibrates the probability distance features of each region and fills the algorithm to achieve efficient fusion of the probabilistic distance centripetal algorithm and the flood algorithm,improving the maze search efficiency and reducing the dependence on the high-performance CPU.Test results of 6 mazes show that,compared with the traditional centripetal and flooding algorithms,the algorithm can reduce the maze search time by 50% and the search success rate by 100%,it is an efficient maze fusion search algorithm.

The front pose estimation and back-end optimization of indoor mobile robot Simultaneous Localization and Mapping (SLAM) are susceptible to motion blur.To solve this problem,an indoor location and map building algorithm based on Inliers tracking statistics is proposed.After extracting and matching the features of RGB images and using the RANSAC algorithm to get Inliers,the fuzzy images which are affected by the motion of the camera are eliminated by tracking and statistics of the number of Inliers,and then the position of the camera is solved by the nonlinear optimization method of the Iterative Closest Point (ICP).On this basis,the trajectory and 3D dense point cloud images are obtained through closed loop detection and optimized global pose.Experimental results show that,compared with RGB-D SLAM algorithm,the proposed algorithm can effectively improve the robustness and accuracy of SLAM system map building.

Aiming at the problem that Gmapping algorithm can not be accurately located due to particle depletion in high similarity and multiple closed-loop environment,a Rao-Blackwellized Particle Filtering(RBPF) Simultaneous Localization and Mapping(SLAM) optimization algorithm based on firefly algorithm is proposed.The firefly algorithm is used to improve the estimation ability of the particle filter,and the sampled particle set is moved to the high likelihood region to improve the particle distribution while ensuring the low likelihood particle diversity to reduce the effect of particle deficiency.Simulation results under MIT and FHW datasets show that the optimization algorithm establishes more accurate raster maps in different experimental environments,and verifies its validity and feasibility.

The manual inspection methods of traditional substations are subject to many objective constraints,and there are certain problems such as false detection and missing detection.Therefore,a robot inspection method is adopted in real-time positioning of the substation,and an adaptive Monte Carlo algorithm is proposed.Using the open source robot operating system,real-time positioning is realized by combining the laser sensor and the odometer.Simulation experimental in Matlab environment results show that compared with Monte Carlo algorithm,the adaptive Monte Carlo algorithm is more accurate and more anti-interference,and it is suitable for real-time positioning of substation inspection robots.

The traditional hand-eye relationship homogeneous matrix equation solving process is cumbersome and complicated,and the accuracy is susceptible to environmental influences.Therefore,based on the idea of converting image coordinates directly into robot coordinates,using the analysis computing ability of OpenCV library function,a hand-eye relationship calibration method based on two-plane homography matrix is proposed.Using the virtual “nine squares” and a fixed reference point calibration mode,a linear camera imaging model is established,and a two-plane homography matrix is obtained,thereby guiding the manipulator positioning.Experimental results show that the method is only 38 s,and the accuracy is higher than the traditional calibration method.

Aiming at the problems of low recognition rate,slow speed,easy to be affected by illumination and weeds effect in the visual path recognition of agricultural robots,a visual navigation path recognition method based on mixed threshold and offset line midline is proposed.The illumination independence is used to eliminate the illumination interference.The mixed threshold method is used to segment the image,and the crop line based on the offset line midline method is given.Experimental results show that compared with the traditional agricultural robot crop line identification method,the method has a small amount of data calculation,the average time is about 200 ms,and the crop line identification accuracy rate is 98%,which can effectively determine the navigation path of the agricultural robot.

A novel binocular Visual Odometry(VO) algorithm is proposed for real-time precise localization of mobile robots.Firstly,it uses accelerated Scale Invariant Feature Transform(SIFT) operator to extract the image features on the left and right image.The sparse stereo matching is carried out after the extracting.In addition,the method of features tracking is applied between the previous and current image.Thus,the initial pose including rotation and translation matrix can be obtained with the motion estimation method based on the RANSAC strategy.Secondly,the image sequence is divided into key frames and non-key frames.In order to decrease the error of the inter-frame motion estimation,a variable sliding window is applied to optimizing the pose of adjacent key frames locally and nonlinearly.Finally,the closed-loop detection is applied by the method of bag of words.Furthermore,all the poses of key frames in the closed-loop are optimized globally to avoid the error accumulation and the drift of the trajectory.Experimental results show that the proposed algorithm has good real-time performance,while reducing the position pose error and improving the positioning accuracy.

To solve the navigation problem of mobile robot in unknown environment,based on cognitive development mechanism of biology,a cognitive development model is constructed for mobile robot autonomous navigation.The neural network which can dynamically adjust the structure is designed by autonomous inserting neuron.It is used to imitate the properties of biological development and to obtain the network that can match the application requirements.The asymptotic learning characteristic is imitated through the thermodynamic process and a cognitive learning algorithm is designed,which is proved to be convergence in theory.The results of experiments show that the proposed model can make the robot obtain knowledge automatically and accumulate experience from environment like animal,and learn the skill of autonomous navigation through cognitive development.

Aiming at the problem that the geometric parameters are not accurate and the absolute accuracy of the industrial robot is low,a geometric parameter calibration method based on Pose Modify Position Sensitive Detector(PMPSD) is proposed.Through the establishment of the error kinematics model,and the PSD device is used to sample the data,the pose correction principle is used to correct the end laser pose and joint angle,and the model constraint objective function is established.The geometric parameters errors are obtained by using the LM algorithm,and the nominal values of geometric parameter are corrected by the parameter errors.Experimental results show that the method avoids PSD feedback control,and can quickly realize geometric parameters calibration of industrial robots,the positioning mean error and standard deviation are 78.28% and 76.38% respectively,it effectively improves the positioning accuracy of the robot.

In order to make the indoor mobile robot better build the accurate map from the data of 2D laser range finder with noise,an improved algorithm based on similar triangules denoising rule is proposed.Using the splitting algorithm to extract the rough line set from the preprocessing data,the data points between the two split points are denoised by improving the similar triangules denoising rule,and the denoised data are re-split,and scanning points are fitted in the data between each two split points by least square method.Experimental results show that the proposed algorithm can reduce the number of effective points which are eliminated,and the accuracy and false positive indexes are better than similar triangles denoising method and traditional split and mergeing algorithm,at the same time,the line segment merging process is basically avoided,and the robustness and accuracy of environment modeling are enhanced.

The stability control strategy of biped robot walking process is studied,and a crus vibration control system based on the vibration acceleration of the upper body of the robot as feedback is designed.Through virtual simulation analysis software——Automatic Dynamic Analysis of Mechanical System(ADAMS),the virtual prototype of the biped robot is built and imported into Matlab.The automatic anti-disturbance control algorithm is designed to suppress the low frequency and high frequency according to active and passive vibration attenuation.Simulation results show that active and passive vibration attenuation greatly reduces the vibration of the upper body of the robot and compensates the situation that passive vibration can not suppress the low frequency vibration,and effectively improves the stability of the robot.