108 results about "Mobile robot navigation" patented technology

The invention provides a robot social adaptive navigation knowledge learning and migration method and system, and relates to the field of mobile robot navigation. Aiming at the problems that an existing path planner lacks time sequence memory and neglects pedestrian social intent, a man-machine co-fusion scene is constructed, a training set containing an expert demonstration path is made, and a recursive generation model is input; designing a recurrent neural network embedded RRT, generating an RNN-RRT planner, and fusing historical information and pedestrian convergence probability in training; new scene loading training parameters are finely adjusted to realize knowledge migration, loss convergence or output RNN final parameters after reaching a preset round number. According to the method, the path anthropomorphism and generalization ability are improved, and the method is suitable for complex human-computer interaction scenes.

The invention discloses a closed denial environment adaptive path planning algorithm, and belongs to the technical field of mobile robot navigation. According to the algorithm, aiming at the characteristics of dense obstacles and many dynamic interferences in a closed denial environment, an artificial potential field method is innovatively combined with a sampling-based RRT algorithm and a dynamic window method, so that organic fusion of global and local path planning is realized. According to the algorithm, an artificial potential field gravitational field is introduced into an RRT sampling process, so that new node generation always deviates to a target direction, and adaptive change is realized by dynamically adjusting a sampling sector angle; in local path planning, a global artificial potential field gravitational field is utilized to optimize a DWA evaluation algorithm, and a better dynamic obstacle avoidance effect is achieved. The algorithm is suitable for path planning requirements of unmanned aerial vehicles, specialized robots and the like in a closed denial environment.

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 is applicable to the technical field of mobile robot navigation, perception and environment mapping, and provides an indoor mobile robot obstacle detection and map construction method, which comprises the following steps of: synchronously acquiring three-dimensional laser and images, carrying out preprocessing such as illumination enhancement, distortion correction and point cloud filtering, extracting multi-modal features by using SwinTransform and SPVCNN, and constructing a map by using an indoor mobile robot. After fusion through a cross-modal attention module CMAM, inputting the 3D-RCNN to realize obstacle detection; and constructing an occupied grid map in combination with SLAM data, mapping barrier semantic tags, performing dynamic error correction through a Bayesian mechanism, and finally generating a global path based on algorithms such as A * and the like. The method integrates the advantages of multi-modal data, improves the complex obstacle recognition rate and map stability, improves the obstacle detection and path planning success rate, and is suitable for robot autonomous navigation scenes such as service and storage.

The invention discloses a mobile robot path planning method combining kinematics constraint and variable density sampling, which comprises the following steps: setting a starting point and a target point, adding the starting point into a vertex set, adding the target point into a sampling set, initializing a node priority queue and an edge priority queue, and establishing kinematics constraint and environment parameters at the same time; searching a main circulation path; if the vertex cost is better, performing vertex expansion and generating candidate edges, and if the edge cost is better, performing feasibility verification and accessing the path tree; when the search is stagnated or trapped in local optimum, an escape mechanism is triggered, and the exploration capability is enhanced through radius expansion and supplementary sampling; an improved heuristic function and a local cost function are established, search convergence is guided, and track smoothness and performability are improved; and when a termination condition is reached, outputting a path planning result. According to the method, the sampling efficiency and the searching speed are improved while the path feasibility is ensured, so that the navigation requirement of the mobile robot in a complex dynamic environment is met.

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.

A computer-implemented method and apparatus to generate an image-based localization model for mobile robot navigation. The method includes performing data collection at a plurality of different service locations, to which a fleet of mobile robots is deployable, to generate collected data, performing a data retention operation with respect to the collected data based on a data retention policy, generating a first image-based localization model and a second image-based localization model for a first and respectively, second service location of the plurality of different service locations, using the collected data. The method further includes deploying the first image-based localization model and the second image-based localization model to a first and, respectively, second mobile robot of the fleet of mobile robots, the first image-based localization model and the second image-based localization model being used to navigate the first and, respectively, the second service location of the plurality of different service locations.

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 provides a visual navigation method and system based on reinforcement learning, and relates to the field of machine vision, and the method comprises the following steps: constructing and training a visual navigation decision model based on a deep Q network; constructing and training an image enhancement model; acquiring a target position; based on the target position, the mobile robot is navigated, in the navigation process, images in different directions are obtained through a plurality of visual sensors carried on the mobile robot, the images in different directions are processed through an image enhancement model, obstacle features are extracted from the processed images in different directions, and the obstacle features are obtained; the mobile robot is controlled to complete obstacle avoidance navigation based on the obstacle characteristics through the visual navigation decision model, and the method has the advantage of improving the intelligent level of navigation of the mobile robot.

The invention provides a mobile robot deep reinforcement learning motion planning method based on collision probability, and belongs to the field of mobile robot navigation planning. According to the method, firstly, in order to evaluate the risk of collision between the robot and an obstacle in a complex dynamic environment, a collision probability estimation function is designed based on the relative distance and speed between the robot and the obstacle and is used for marking key obstacles around the robot; secondly, considering a key obstacle and a global motion planning target, and designing a combination form state observation space; and finally, designing a reward function for guiding the robot to avoid the key obstacle aiming at the key obstacle, bringing the information of the key obstacle into a state space and designing a reward function in a corresponding form, thereby realizing effective obstacle avoidance of the high-risk obstacle and reducing the time cost of motion planning. Meanwhile, it is verified that the algorithm improves the safe and rapid motion planning performance of the mobile robot in a complex dynamic environment in a test environment.

The invention provides an internal constraint mobile robot navigation method fusing visual perception and speed optimization, and aims to realize autonomous navigation of a mobile robot in a scene with a U-shaped obstacle through multi-modal perception. Firstly, a laser radar is used to construct a two-dimensional map; secondly, based on a global path and two-stage speed optimization window filtering algorithm, a speed gradient thought and a dynamic self-adaptive internal constraint TEB navigation algorithm, on the premise that the system is kept stable, the navigation speed of the mobile robot is increased, and the mobile robot can smoothly pass through the interior of the U-shaped obstacle; and finally, combining a post-processing rule in a visual perception part, and proposing an orientation sensitive feature enhancement mechanism to obtain a passable signal, so as to improve the perception capability in a special environment. Experimental verification shows that the method has remarkable advantages in the aspects of feasibility and stability, the task success rate is increased by 50% compared with a traditional method, the average navigation time consumption is reduced by 20%, and the all-weather operation requirement of the mobile robot in a dynamic environment can be effectively met.

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.

Facilitating navigation localization for a mobile robot operating autonomously can include a deployment of a retractable landmark in a work area in response to an operational threshold not being met for a mobile robot to autonomously operate traversing the work area. The operational threshold not being met can be based on a determination of when landmarks in the work area for the mobile robot to use as perimeter markers do not meet the operational threshold.

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.

Training and / or using both a high-level policy model and a low-level policy model for mobile robot navigation. High-level output generated using the high-level policy model at each iteration indicates a corresponding high-level action for robot movement in navigating to the navigation target. The low-level output generated at each iteration is based on the determined corresponding high-level action for that iteration, and is based on observation(s) for that iteration. The low-level policy model is trained to generate low-level output that defines low-level action(s) that define robot movement more granularly than the high-level action—and to generate low-level action(s) that avoid obstacles and / or that are efficient (e.g., distance and / or time efficiency).

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 present invention discloses an active auditory localization method for mapless navigation. The steps include: 1) training a mobile robot navigation model on a simulation platform through a reinforcement learning method; 2) the mobile robot collects the ranging information of the lidar at the current moment, the auditory orientation information obtained based on the target position, and the pose information of the mobile robot odometer according to a set time step; wherein, the lidar is mounted on the mobile robot; 3) inputting the ranging information, the auditory orientation information and the pose information into the mobile robot navigation model trained in step 1) to infer the speed command at the current moment, and the mobile robot navigates to the target position according to the speed command. The present invention adopts a more reliable and effective target localization method, which has high application value for mapless navigation in real scenarios.

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.