67 results about "Mobile robot navigation" patented technology

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to a self-adaptive agent decision framework training method with dynamic environment perception capability, which comprises the following steps of: 1, collecting multi-modal sensor data in parallel, and carrying out hardware-level preprocessing on the collected data; step 2, based on the data preprocessed in the step 1, performing dynamic security boundary driven real-time feature extraction to obtain security boundary coordinates; 3, based on the safety boundary coordinates obtained in the step 2, generating an execution action strategy in a decision period; and 4, based on the action strategy generated in the step 3, constructing a feedback closed loop of the distributed execution framework, and realizing real-time closed loop optimization. According to the method, the core requirements of scenes such as mobile robot navigation and intelligent industrial control on autonomous decision-making safety and real-time performance are met.

The invention discloses an intelligent mobile robot navigation method for a dynamic environment, and relates to the technical field of computer processing, and the method comprises the following steps: S01, collecting the dynamic environment of a robot in real time through a laser radar, an RGB-D camera and a UWB module, building a robot real-time environment perception model, and carrying out the real-time environment perception of the robot; the robot real-time environment sensing model comprises parameterized description of metal reflection, dynamic obstacles and narrow terrains; s02, determining a plurality of key design parameters of navigation of the intelligent mobile robot; and S03, based on the plurality of key design parameters, constructing an environment perception accuracy objective function of the intelligent mobile robot and a real-time response and scene understanding depth objective function. According to the invention, high-precision environment perception, low-delay path re-planning and safe and efficient navigation are synchronously realized in a dynamic environment through metal reflection interference filtering, dynamic obstacle acceleration pre-judgment and narrow terrain adaptive passing strategies.

The invention relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic vision SLAM method and system based on a mixed attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules, namely a semantic perception module, a visual tracking and repositioning module, a local mapping and fusion module, a loopback detection module and a global consistency optimization module. According to the method, local and global features of an image are extracted in parallel through a target detection network, and high-precision semantic observation is output; when the tracking is lost, the dual geometric constraint of the 2D internally tangent ellipsoid-3D object ellipsoid is utilized, and the P3P algorithm and the IoU cost function are matched to realize rapid relocation. In the mapping process, Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to carry out bundle adjustment. According to the method, the problem of feature extraction failure in motion blur and weak texture scenes is effectively solved, and the construction precision of the semantic map and the survivability of the system are remarkably improved.

The invention discloses an SLAM (Simultaneous Localization and Mapping) optimization mapping method and device combining geometric verification and constraint of a reflector, relates to the technical field of industrial-grade mobile robot navigation, and solves the problem that in the prior art, a continuous constraint mechanism cannot be established in a mapping process to correct accumulative errors in motion. The method comprises the following steps: acquiring an original laser radar point cloud containing a reflector, screening out reflection points with the distance smaller than a preset distance, and carrying out clustering and quintuple geometric verification on the screened points by adopting a density clustering algorithm, so as to obtain a multi-dimensional geometric verification mechanism based on PCA (Principal Component Analysis); local coordinates of the center of the reflector are calculated and converted into global coordinates, the reflector is used as a stable Landmark depth to be fused into an SLAM back-end graph optimization framework, and optimization nodes including a timestamp, a robot pose, a reflector ID and an observation pose are constructed based on a global coordinate system to participate in graph optimization; and the matching problem caused by insufficient natural characteristics or environment change in a long corridor scene can be solved in a targeted manner.

The invention discloses a quadruped robot navigation method and system based on three-dimensional environment mapping and navigation, and belongs to the technical field of mobile robot navigation. The method comprises the following steps: acquiring a non-repeated scanning three-dimensional point cloud and a high-frequency pose to carry out pre-integration distortion removal; extracting features for joint optimization, and constructing a global three-dimensional point cloud topological map; stripping ground point cloud by using a double-track map mapping mechanism, and performing dimensionality reduction to generate a two-dimensional multi-dimensional cost navigation map; column cycle matching is carried out by using two-dimensional polar coordinate context features in a positioning degradation environment, and continuous poses are forcibly corrected; and forward sampling and evaluation are carried out based on the navigation map and the multi-dimensional trajectory evaluation cost function model, and closed-loop motion of the robot is controlled. According to the method, the problems of high computing power consumption of high-dimensional space calculation and easy failure of positioning in a degraded environment are effectively solved, and the safety navigation capability of the quadruped robot in a complex environment is remarkably improved.

The present application belongs to the technical field of mobile robot navigation, and particularly relates to a SLAM navigation method and system for a mobile intelligent cabinet, which comprises the following steps: acquiring a laser point cloud sequence and a grayscale image of the mobile intelligent cabinet and performing data cleaning; obtaining a geometric feature index based on the spatial jump distribution of the laser point cloud sequence; obtaining a visual texture index based on the local dispersion of the image gradient; calculating laser dynamic weight and visual dynamic weight by using the geometric feature index and the visual texture index, weighting and fusing the pose change quantity calculated by the single-line laser radar odometry and the visual odometry to obtain a fused pose quantity; and updating the global state based on the fused pose quantity and driving autonomous navigation. The present application can adjust the sensor weight in real time according to the environmental characteristics, solves the problem of positioning divergence in the long corridor of a shopping mall or a high-reflectivity environment, and improves the robustness of navigation.

A state estimation method for mobile robot navigation is provided which includes: step S1, initializing the processing module, and then controlling the field-programmable gate array to obtain measurement values of a moment k collected by the inertial measurement unit and the localization apparatus in a constant sampling period; step S2, controlling the application-specific integrated circuit to send the measurement values to the processing module; step S3, controlling the processing module to, based on the measurement values, sequentially perform vector padding, additional inertial vector acquisition, normalization processing, alignment error computation, filter measurement value computation, bias estimation value computation, velocity estimation value computation, pose estimation value computation and orthogonalization processing to obtain state estimation values of the moment k; step S4, controlling the processing module to send the state estimation values to the human-machine interface for visual display.

The invention relates to the technical field of mobile robot navigation, and discloses a robot adaptive motion control method and system, and the system comprises a step test module which is used for recording odometer data and control quantity data with a timestamp; the parameter fitting module is used for analyzing the odometer data and the control quantity data and establishing a mapping function of the control quantity and the movement speed; the delay compensation module is used for maintaining a historical control quantity queue and predicting the expected pose of the robot when the control instruction sent at the moment t is executed based on the control delay tau; the multi-objective optimization module is used for solving the optimal control quantity; and the online updating module is used for continuously collecting motion data in the motion process of the robot and dynamically updating the mapping function and the key parameters. According to the method, actual motion parameters are automatically obtained through step testing, different robot individuals and performance attenuation scenes are adapted, the problem of model mismatching is solved, and time delay compensation is controlled to reduce positioning deviation.

The application belongs to the technical field of mobile robots, and relates to a mobile robot navigation method based on multi-modal fusion environment perception, which comprises the following steps: firstly, a two-dimensional laser radar arranged on a robot chassis is used to complete the pre-construction of a static global map of an environment; then, a depth image collected by a depth camera arranged on the robot chassis and an upper computer is subjected to denoising pretreatment, and after being converted into a three-dimensional point cloud, the depth image is subjected to operations such as cutting, down-sampling, filtering and obstacle segmentation, and the point cloud after the operation is converted into two-dimensional pseudo-laser radar data consistent with the data format of the laser radar; finally, a multi-layer cost map is constructed based on the laser radar perception data, the static global map and the two-dimensional pseudo-laser radar data, and then fused, and the existing navigation function package of ROS is used to complete the fused navigation. The application can effectively avoid various irregular three-dimensional obstacles, expand the application scene of the mobile robot, and has engineering application value and low-cost platform deployment practicability.

The application discloses a kind of laser radar calibration assembly, method, device, system and storage medium, it is related to radar positioning technical field, the laser radar calibration assembly includes: calibration platform, for carrying mobile robot travel;Target feature code, is set on calibration platform;Purpose feature code is used for the feature code sensing device on mobile robot reading;Target obstacle, is connected on calibration platform;Target obstacle is used to reflect the target laser that positioning radar on mobile robot emits.This application discloses the laser radar calibration assembly can solve when there is pose deviation between laser radar coordinate system and vehicle body coordinate system, the technical problem that AMR autonomous mobile robot navigation precision is lower.

A mobile robot navigation method based on curvature adaptation and target fault tolerance includes: acquiring environmental perception data around the mobile robot and constructing a two-dimensional cost map containing obstacle fatal thresholds and no-go thresholds; extracting local path point sets of the global planning path; determining whether there is a collision risk and whether to trigger global path reconstruction based on the local path point sets and the two-dimensional cost map; if global path reconstruction fails, performing target fault-tolerant relocalization in polar coordinates to generate an alternative endpoint and a new global path; for the new global path, constructing a curvature evaluation model with a forward-looking evaluation window and extracting the comprehensive average curvature; constructing a nonlinear exponential mapping function containing bias parameters and damping coefficients to dynamically convert the comprehensive average curvature into chassis linear velocity control gain; and simultaneously establishing a heading PD control and low-pass filtering anti-interference mechanism based on tangent fitting, outputting the finally calculated speed command to the mobile robot chassis for execution.

The application provides a navigation method and device of a mobile robot, electronic equipment and a storage medium, and relates to the technical field of robots. The method comprises the following steps: acquiring a positioning score of the mobile robot; if it is determined that the mobile robot loses positioning according to the positioning score, rotating the head of the mobile robot to construct a local map; acquiring a global map for navigation of the mobile robot, determining a navigation target point according to the global map; determining a target terminal point in the local map according to the navigation target point, and generating a local navigation path by using the target terminal point to perform local navigation. Through the fusion of head motion control and local planning navigation strategy, the robot can flexibly adjust the viewing angle, actively acquire a wider field of view and richer environmental information in a narrow space or an environment with unstable positioning signals, improve the navigation accuracy and robustness of the robot in a complex and variable scene, and effectively cope with challenges such as positioning drift.

The application discloses a kind of indoor mobile robot dense mapping and autonomous navigation method based on depth camera, belong to robot simultaneous localization and mapping, robot navigation field.The application uses depth camera and based on interframe matching to image ORB feature to realize real-time positioning to robot, by fusing color image and depth image, and in order to eliminate redundant video frame, key frame extraction method in space domain is introduced, real-time dense three-dimensional point cloud map construction is realized, and it is converted into octree map format and grid map format suitable for navigation, then by ROS Navigation function package, it is combined with existing mobile robot navigation method, realizes indoor mobile robot autonomous navigation scheme based on pure vision scheme.

This invention discloses a human-computer interaction method for service robots, involving human-computer dialogue systems and mobile robot navigation control. The biggest drawback of current human-computer dialogue systems, which primarily use sequence-to-sequence models, is that these models tend to generate high-frequency, generic responses and lack sufficient useful information, making it difficult to provide practical assistance to users. This patent introduces external knowledge to enrich the information content of the model's generated responses, improve the diversity of responses, and alleviate the problem of high-frequency generic responses. Based on this model, a human-computer interaction method for service robots is designed. This method not only has dialogue capabilities but can also parse user navigation control commands, enabling fixed-point navigation control in home or office scenarios and improving the robot's human-computer interaction capabilities.

Systems and methods are described for detecting changes at a location based on image data by a mobile robot. A system can instruct navigation of the mobile robot to a location. For example, the system can instruct navigation to the location as part of an inspection mission. The system can obtain input identifying a change detection. Based on the change detection and obtained image data associated with the location, the system can perform the change detection and detect a change associated with the location. For example, the system can perform the change detection based on one or more regions of interest of the obtained image data. Based on the detected change and a reference model, the system can determine presence of an anomaly condition in the obtained image data.

The invention relates to a mobile robot navigation and positioning method based on multi-sensor data and related components, which are applied to the technical field of robot navigation and positioning, and the method comprises the following steps: acquiring environmental data and a mode conversion instruction; acquiring sensor monitoring data based on the environment data; determining a motion mode based on the sensor monitoring data, the mode conversion instruction and a preset rule; determining a positioning mode based on the environment data and the motion mode; and determining robot positioning based on the environment data, the positioning mode and the sensor monitoring data. The method has the effect of improving the navigation positioning accuracy of the mobile robot.

The application belongs to the technical field of mobile robot navigation control, and relates to a mobile robot navigation control method and system based on natural language, which comprises the following steps: collecting multi-modal perception data and encapsulating, generating a dynamic priority event stream, and obtaining a discretized intention segment set through analysis, combining historical context to construct a situational intention flow graph, and generating a predictive task instruction package; then, the modeling granularity of the map is dynamically adjusted according to the instruction package and environmental data, and a multi-resolution fusion map is generated; the segmented path dynamic optimization is carried out on the map, and the robot motion trajectory is generated; then, the trajectory is converted into a driving robot execution action, and an execution state log is generated; finally, the log is fed back to the intention flow engine, and the intention flow graph is updated and corrected in real time; the application solves the problem of insufficient logical coherence in intention understanding, multi-modal data processing and path planning of the existing mobile robot.

The invention discloses a door state identification method, related equipment and a computer program product, which are applied to the navigation process of an autonomous mobile robot, and can realize the identification of the door state under the condition of only a laser radar, thereby reducing the hardware cost. Specifically, the current position of the robot is obtained, position information of a door is searched, the first distance and the first angle of the door relative to the robot at the current moment are calculated, and a detection threshold value is dynamically adjusted according to the first distance. And acquiring laser radar data at the current moment, screening a target laser beam located in the door direction from the laser radar data according to the first angle, and determining the opening and closing state of the door at the current moment according to the size relationship between the distance of the target laser beam and the detection threshold value. The detection threshold is dynamically adjusted according to different distances between the robot and the door, and the accuracy of the detection result is improved. Only the distance of part of the target laser beams located in the door direction needs to be calculated, and the calculated amount is smaller.

The application discloses a kind of robot navigation methods of combining embedded professional knowledge LLM in restricted platform, it is applied to the differential mobile robot of carrying LiDAR, IMU and with support SIMD instruction set MCU as core, the method will embedded professional knowledge LLM and MCU end SIMD light weight DWA fusion, destination coordinates and DWA parameters are generated by LLM analysis natural language instruction, MCU successively completes continuous trajectory modeling, obstacle point cloud SIMD batch division, batch parallel collision detection, selects no collision candidate speed pair and selects optimal value output, realizes robot autonomous target arrival and whole process real-time obstacle avoidance by closed loop control.The application fully taps MCU computing power, improves the real-time of obstacle avoidance and decision efficiency, reduces hardware deployment cost, realizes the deep fusion of planning and execution, operation threshold is low, scene adaptability is strong, engineering realizable is high, and it is suitable for multi-scene differential mobile robot navigation.

The invention relates to the technical field of mobile robot navigation, and discloses a global path planning method based on an A star algorithm, which comprises the following steps: acquiring and establishing an obstacle grid map; generating a connecting line segment from the starting point to the ending point, and traversing points on the line segment according to a preset step length; obtaining a collision starting point and a collision ending point; taking the collision starting point and the collision ending point as local starting and ending points to obtain a local collision-free path; traversing from a first path point of the local collision-free path to obtain a global path list, and if the connecting line does not collide with the obstacle, updating the path point as a new starting point and exiting the traversing; repeating with the new starting point until the connecting line segment of the newest starting point and the terminal point has no collision; and adding the end point into the global path list, and carrying out collision-free path point connection optimization on the global path list. According to the invention, global search is converted into local search through the collision starting point and the collision ending point, and a single-time A star algorithm only needs to process extremely-small-scale grids around the obstacle, so that the calculation amount is reduced and the efficiency is improved.

The invention discloses a navigation safety system for a mobile robot in an unknown environment, and belongs to the technical field of robot control. The navigation safety system comprises a searchlighting module used for acquiring searchlighting images, containing light beams, of the mobile robot in different directions; the path optimization module is used for analyzing dust sectional distribution conditions in different directions based on the lamplight searchlighting image and generating a navigation path optimization strategy; and the autonomous navigation module is used for executing basic moving path planning and dynamically adjusting navigation parameters in combination with a navigation route optimization strategy. According to the invention, based on the multidirectional light path image, region segmentation processing is carried out, the dust concentration of different sections is analyzed, exploration scenes of special environments such as collapse tunnels and pits are adapted, targeted technical support is provided for path decision of the mobile robot in a high-dust unknown region, and the navigation safety and task execution reliability of the mobile robot are improved.

The invention discloses a robot multi-mode environment feature modeling and recognition positioning method and system, and belongs to the technical field of robot navigation. The method comprises the following steps: acquiring an RGB image, a depth image and laser radar point cloud data, and constructing an environment feature model containing spatial geometric features and trafficability parameters; and performing target-level semantic recognition and region-level semantic segmentation on the multi-modal data to generate semantic regions and relationships thereof. And generating a navigation region sequence based on the environment feature model and the semantic feature model, wherein the navigation region sequence represents a region-level path from the current position to the target position. The robot generates a local path according to the real-time environment data in the moving process and outputs a motion control instruction; and when the robot approaches the target position, acquiring multi-view observation data, and calculating the three-dimensional position of the target through feature association and a camera imaging model. According to the method, the modeling precision, the path planning stability and the target positioning reliability in a complex environment are improved, and the method is suitable for various mobile robot navigation and positioning scenes.

The application relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic visual SLAM method and system based on a hybrid attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules of semantic perception, visual tracking and repositioning, local mapping and fusion, loop detection and global consistency optimization. The application extracts local and global features of an image in parallel through a target detection network, and outputs high-precision semantic observations; when tracking is lost, a 2D inscribed ellipse-3D object ellipsoid dual geometric constraint is used to realize fast repositioning in cooperation with a P3P algorithm and an IoU cost function. In the mapping process, a Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to perform bundle adjustment. The application effectively solves the problem of feature extraction failure in motion blur and weak texture scenes, and significantly improves the construction accuracy of a semantic map and the survival ability of the system.

The invention belongs to the technical field of intelligent mobile robot navigation, and particularly relates to a speed smooth control method and system of a mobile charging robot. The method comprises the steps of receiving an upper-layer original speed instruction, obtaining real-time state information of a robot, and judging whether speed intervention is needed or not; if the short-distance obstacle exists, anti-collision triggering or sudden stop triggering is carried out, and zero speed is output; and otherwise, according to the difference between the current speed and the target speed, performing smooth adjustment at a fixed step length, and continuously issuing an instruction after smoothing to the actuator until feedback is received. The system comprises a data acquisition module, a speed decision module, an instruction issuing module and an ROS topic communication assembly, and realizes data interaction and closed-loop control. According to the method, the speed is dynamically adjusted by fusing the environment and state information, the problems that traditional control is poor in smoothness and insufficient in safety are solved in cooperation with speed smooth processing, and the motion stability, the instruction execution efficiency and the scene adaptability of the robot are improved.

The application relates to a loose-coupling seamless switching navigation method, system, application and computer readable storage medium based on multi-dimensional scene perception, and belongs to the technical field of autonomous mobile robot navigation. The method comprises the following steps: real-time perception of environment characteristics, task semantics, sensor health degree and system load; strategy decision based on a layered state machine, automatic selection of a loose-coupling or tight-coupling fusion mode; adoption of a state inheritance and gradual fusion mechanism to realize mode smooth switching; and triggering of hierarchical safety recovery under abnormal conditions. The system comprises a sensor module, a state perception module, a strategy decision module, a fusion switching module and a safety recovery module. The loose-coupling seamless switching navigation method, system, application and computer readable storage medium based on multi-dimensional scene perception have the advantages that complex dynamic scenes can be adapted, navigation precision, real-time performance and robustness are considered, the method is suitable for application scenes such as park inspection, warehouse transportation and unmanned driving, and has remarkable practical value and popularization prospect.

The invention relates to the field of indoor mapping, discloses a mobile robot indoor mapping method based on multi-sensor fusion, and relates to the technical field of mobile robot navigation. The method comprises the following steps: an upper computer processing module collects data of a wheel type odometer, inertia measurement and a laser radar and carries out time alignment; calculating a state prediction value based on a kinematic model, and generating a posterior state estimation value by using extended Kalman filtering and fusing multi-source observation data; the current active local sub-graph is obtained by taking the current active local sub-graph as an initial value, a continuous probability map model based on bicubic interpolation is constructed, and an optimal matching pose is solved by utilizing a nonlinear optimization algorithm; and updating the probability grid map based on the optimal pose, and generating an indoor map through multi-resolution loopback detection and global pose map optimization introducing a robust loss function. According to the method, slipping and zero offset errors are effectively compensated, the problem that gradient optimization cannot be carried out on a discrete map is solved, and the robustness and precision of indoor mapping are improved.

This application provides a mobile robot navigation method, apparatus, control unit, and mobile robot, relating to the field of mobile robot navigation technology. The method includes: acquiring time-synchronized RGB images and sparse depth data; performing semantic segmentation and monocular depth estimation on the RGB images to obtain a semantic mask and a monocular depth map; performing scale correction on the monocular depth map based on the sparse depth data to obtain a corrected depth map, and generating a 3D point set based on the semantic mask and the corrected depth map; tracking dynamic obstacles in the 3D point set and generating a dynamic risk score based on the relative motion of the tracked target; fusing static obstacle cost, semantic cost, and dynamic risk score into a local cost map; and generating speed commands based on the local cost map and the global path to drive the mobile robot chassis movement. This improves upon the insufficient fusion of obstacle semantics, depth scale, and navigation cost, thereby enhancing the smoothness and safety of navigation.

This invention relates to the field of mobile robot navigation and positioning technology, specifically disclosing a mobile robot navigation and positioning adaptive switching system. The positioning decision layer identifies under-constrained environmental areas in real time and uses a lightweight time-series prediction model to predict the reliability degradation risk of RTK and LiDAR in advance. It dynamically generates a unique optimal positioning mode command by integrating environmental feature scores, prediction results, and the current positioning status. The pose calculation layer activates the corresponding module according to the command, performs fusion using an extended Kalman filter in multi-sensor fusion mode, and outputs a six-DOF pose, covariance matrix, and positioning health score. When the positioning health score continuously falls below a safety threshold, the system generates a safe docking command and outputs a hardware alarm signal through a GPIO pin. This invention eliminates the need for pre-dividing the positioning area, achieving seamless and continuous output of positioning services, avoiding pose jumps, and significantly improving the operational continuity and environmental adaptability of the mobile robot.

The invention belongs to the technical field of mobile robot navigation control, and relates to a mobile robot navigation control method and system based on a natural language, and the method comprises the following steps: collecting and packaging multi-modal perception data, generating a dynamic priority event stream, obtaining a discretized intention fragment set through analysis, and carrying out the analysis of the discretized intention fragment set; constructing a contextualized intention flow graph in combination with historical contexts, and generating a predictive task instruction packet; dynamically adjusting the map modeling granularity according to the instruction packet and the environment data, and generating a multi-resolution fusion map; performing sectional path dynamic optimization on the map to generate a robot motion track; the track is converted into a driving robot execution action, and an execution state log is generated; finally, the log is fed back to the intention flow engine, and the intention flow graph is updated and corrected in real time. The problem that an existing mobile robot is insufficient in logic continuity in the aspects of intention understanding, multi-modal data processing and path planning is solved.