5030 results about "Mobile robot" patented technology

The invention provides an urban gas pipeline micro-leakage early warning method and system, and relates to the technical field of data processing, and the method comprises the steps: employing a fuzzy analytic hierarchy process to calculate a dynamic risk index based on an evaluation matrix, and triggering the three-stage linkage alarm of an audible and visual alarm, a mobile terminal and an emergency platform when the index exceeds a threshold value; for three-level linkage alarm, a mobile robot is dispatched, a target area is navigated based on thermodynamic diagram positioning and reference coordinates, gas concentration is remeasured through a methane laser detector, a pipeline surface temperature field is scanned in combination with infrared thermal imaging, and a verification result and correction data are bound through a block chain evidence storage technology. And a tamper-resistant full-link evidence chain is formed. According to the invention, through cooperation of early warning and calculation, full-process automation of gas pipeline monitoring, early warning and emergency verification is realized, and the intelligent level of safety monitoring is improved.

A three-dimensional point cloud semantic segmentation method based on graph convolution and a grouping vector attention mechanism adopts a network structure of a U-Net style, feature extraction and propagation are performed on point cloud through multi-level feature coding and decoding, each level firstly centralizes and standardizes local neighborhood features through a geometric affine module, and then the local neighborhood features are subjected to feature extraction and propagation; the capturing capability of the model on the diversity of the local region is enhanced; secondly, constructing a local neighborhood graph, and designing an asymmetric edge feature learning function to extract local topological information; deep edge features are further extracted in combination with a residual network; and finally, selectively fusing local features and global context information under the condition of low parameter quantity by using a grouping vector attention mechanism, and performing cross-level feature interpolation up-sampling to an original scale to complete point cloud segmentation. The method can significantly improve the point cloud segmentation precision, and is suitable for the perception of a mobile robot and other devices carrying a three-dimensional sensor on a complex scene.

A mobile computing device includes a user input device and a controller. The user input device includes a display, and the controller is operably connected to the user input device and configured to execute instructions to perform operations. The operations include presenting on the display, information about one or more areas that were not cleaned by an autonomous cleaning robot during a first mission. The operations further include transmitting data corresponding to a user-selected subset of the one or more areas to cause the robot to clean the user-selected subset during a second mission.

The invention provides a mobile robot operation efficiency evaluation method and system based on the industrial internet, and relates to the technical field of operation efficiency evaluation, and the method comprises the steps: collecting multi-mode operation data, selecting a transmission channel, uploading the data to an edge calculation node, carrying out the preprocessing, completing the information, and carrying out the filtering operation, generating a first semantic feature and an operation feature descriptor; selecting an optimal transmission path and a service function chain, uploading the operation characteristic descriptor to a cloud server, carrying out real-time analysis on the operation characteristic descriptor, generating an initial operation efficiency evaluation result, carrying out analysis and comparison, determining an efficiency bottleneck, and generating an initial optimization improvement suggestion; sending an initial operation efficiency evaluation result to an edge computing node, performing semantic enhancement and logical reasoning, generating a refined efficiency evaluation report, analyzing an incidence relation and an influence mechanism among different evaluation indexes, determining key efficiency bottleneck factors, generating a comprehensive optimization suggestion, collecting operation data in real time, and generating a comprehensive efficiency evaluation report.

The invention relates to the technical field of stacking equipment, in particular to a movable robot stacking system and method. The incoming material conveying line is provided with a visual detection device, and each pushing station is provided with a limiting mechanism and a pushing assembly. The feeding ends of the marshalling conveying lines are connected with the pushing stations in a one-to-one correspondence mode. Each marshalling conveying line is provided with a cache area and a grabbing area. The empty tray conveying line is provided with stacking positions, and each stacking position is provided with a steering conveying assembly. The feeding ends of the full-stack tray conveying lines are connected with the stacking positions in a one-to-one correspondence mode. The movable robot assembly comprises a transverse ground rail assembly, a robot body and a clamp, and the robot body is configured to be capable of moving to the grabbing areas of all the marshalling conveying lines to grab products and moving to all the stacking positions to stack the products to the empty trays. The full-process automation of feeding, classifying, grouping, stacking and transferring of products is achieved, and the efficiency and accuracy of stacking operation are improved.

The invention relates to the technical field of synchronous positioning and map construction, in particular to a geometric-semantic collaborative fusion mobile robot three-dimensional semantic map construction method, which is used for performing deep fusion on a geometric map and multi-view semantic information so as to solve the problem of difference of the two types of information in resolution, scale and dimension. The geometric map mainly provides structured information of the environment, and the semantic information describes object categories and features thereof in the environment. Direct fusion of the two types of information often causes spatial positioning errors and semantic information loss or redundancy problems. According to the method, a geometric map containing environmental structured information is constructed through an SLAM technology, and meanwhile, object classification, attribute labeling and semantic relationship analysis are performed on multi-view image data by utilizing the semantic information extraction capability of a large model. And finally, precise fusion of the geometric map and multi-view semantic information is realized, and meanwhile, the object recognition and semantic understanding capabilities of the robot in a complex scene are improved.

The invention discloses a mobile robot positioning and navigation method based on laser SLAM (Simultaneous Localization and Mapping). The method relates to the fields of machine learning, automatic driving, computer vision and the like. Aiming at the balance of real-time performance and calculation efficiency, the method comprises the following steps: firstly, tracking and estimating a motion track of a sensor by using laser radar data in real time, and fusing LiDAR and IMU; secondly, selecting a key frame and expressing the key frame by using a neural network implicit scene; adjusting parameters represented by a key frame pose and a neural scene through joint optimization to minimize a loss function; and finally, dynamically adjusting the loss function margin of laser radar rays by adopting a JS dynamic margin loss function, and converting an implicit neural representation three-dimensional scene into an explicit three-dimensional network model, thereby facilitating visualization, evaluation and use. Compared with other methods, the method has the advantages that the method has continuous and fine scene modeling capability, has real-time performance of positioning and mapping, and can be expanded to the fields of automatic driving, urban planning and the like.

A multi-arm robotic harvesting apparatus (51) is provided. In another aspect, a robotic harvesting apparatus and method automatically optically locate a fruit in a tree, move and align an arm (55) to the fruit, apply a vacuum pressure to temporarily pull the fruit against an end of the arm, rotate the arm to pick the fruit off of the tree, retract the arm and attached fruit, release the vacuum pressure to drop the fruit onto a receiving surface, and simultaneously operate another arm relative to another fruit on the same tree, while avoiding a collision between the arms. A further aspect of a mobile robotic harvesting apparatus and method applies a vacuum pressure to multiple robotically and automatically movable, fruit picking arms from a single vacuum pump (53), and the apparatus includes a shared optical perception system (67) and programmable controller (251).

Autonomous mobile robots for transporting carts are described. An example robotic cart transport system includes a cart for transport and a robotic transport. The robotic transport includes a transport cab and a load handler. The transport cab includes a drive system and a sensor array positioned over the transport cab. The load handler includes a load base supported by a directable caster wheel, a lift table positioned over the load base, a lift abutment anchor, and a lift drive. The robotic transport can autonomously position the load handler under a cart, and the lift drive can raise the lift table up and off of the top surface of the load base, to a lifted position seated against the lift abutment anchor. The lift table will contact and lift the cart in this motion, and the cart can be transported to a new location by the robotic transport.

The invention discloses an intelligent scheduling method and system for a mobile robot, and relates to the technical field of robots. Extracting task basic attributes to form a static feature layer, establishing a dynamic feature layer formed by dynamic influence factors, and generating a double-layer task feature model; establishing a self-adaptive matching model of the robot and the task, performing feature fusion on the robot state data and the task feature matrix, and calculating a matching degree scoring matrix; a task allocation scheme is generated based on the matching degree scoring matrix, potential path conflict points are predicted, an avoidance strategy is determined, and conflict prediction and avoidance are executed; dynamically adjusting a task allocation and execution sequence according to the load state of the robot; and a performance closed-loop optimization framework is established, and scheduling strategy parameters are automatically adjusted. According to the method, the double-layer task feature model and the self-adaptive matching mechanism are constructed, and track prediction and conflict avoidance technologies are combined, so that intelligent matching and dynamic adjustment of the robot and the task are realized.

The invention discloses a robot vision inertial navigation method and device and a storage medium, and belongs to the technical field of mobile robot navigation, and the navigation method comprises the following steps: measuring motion information of a mobile robot by using an IMU sensor; collecting image data of the mobile robot by using a camera; inputting the feature points and the descriptors into a LightGlue network model to obtain a local feature point matching relation of the image; calibrating the offset of the gyroscope by optimizing and recovering visual scale information, and estimating the IMU coordinate system speed, the gravitational acceleration and the offset of the IMU; introducing a visual feature re-projection error, an IMU pre-integration error and marginalized residual constraint, and then integrally converting into a target function to obtain a nonlinear optimization result; further repositioning is carried out to obtain a repositioning result; and optimizing the global pose map to obtain a final global pose map optimization result, and carrying out navigation operation according to the global pose map optimization result. The method can improve the positioning precision and operation efficiency under view angle change and illumination change, and is high in practicality.

In various examples, a technique for generating a path between a current location and a target waypoint is disclosed that includes receiving a route plan that is associated with a plurality of waypoints representing locations in a physical environment. The technique also includes identifying a search space that includes the route plan, and identifying a target waypoint of the plurality of waypoints—the target waypoint being in a portion of the search space between a current location of a mobile robot and an end waypoint of the route plan. A path between the current location of the mobile robot and the target waypoint may then be generated.

Disclosed is an outdoor mobile robot manipulator for the automatic handling of railway inter box connectors (IBC) in an intermodal rail yard. The manipulator includes a dual-mode adaptive suspension for protection of the robotic arm and end-effector against shock and vibration, a universal fingerless gripper capable of manipulating main types of rail IBCs, a blind IBC type recognition based on force sensor and close-range proximity sensors, and other protection gears enabling the manipulator to work in harsh outdoor working environments.

The invention belongs to the technical field of mechanical arm control, and discloses a mobile mechanical arm control method based on depth element imitation learning, which introduces a depth element imitation learning (DMIL) algorithm, and the algorithm combines depth element learning and imitation learning (IL) technologies to improve the adaptability and efficiency of a mobile mechanical arm in such tasks. The expert trajectory is elaborately made and carefully checked in a AppeliaSim simulation environment, and the interaction of the real world is evaluated at the same time. The position and the direction of the button lock are determined by adopting a deep learning 6D attitude estimation method, and key operation reference points are visually identified. In the whole IL stage, through integration of resistance inverse reinforcement learning (AIRL) and variable impedance control (VIC) technologies, a strategy for controlling operation of the mechanical arm is enhanced. The expert-guided trajectory and tangible force feedback data from real-world settings enhance this.

The invention provides a vehicle mobile charging control method, electronic equipment and a storage medium, and belongs to the technical field of new energy vehicle charging. The method comprises the following steps: receiving a mobile charging request, and determining a target parking space coordinate associated with the mobile charging request; determining at least one callable robot based on the first state information of each robot in the mobile robot set; determining at least one callable charging pile based on the second state information of each charging pile in the mobile charging pile set; determining a target robot and a target charging pile according to the first position information of each callable robot, the second position information of each callable charging pile and the target parking space coordinate; and the target robot is called to carry the target charging pile to the target parking space coordinate, so that a vehicle on the target parking space is charged through the target charging pile. The problems of low charging pile utilization rate and long user waiting time in the prior art can be solved.

The invention belongs to the technical field of robot testing, and discloses a mobile robot slope dynamic and static stability test system and method, and the system comprises a ramp test device which is used for providing a horizontal slope and a slope surface which is connected with the horizontal slope and can adjust different slope values; the motion capture system is used for monitoring the motion state of the mobile robot on the ramp test device in real time; and the control system is electrically connected with the ramp test device and the motion capture system, and is used for controlling the slope value of the ramp test device in real time, receiving the detection result of the motion capture system, and judging whether to adjust and control the next slope value according to the detection result. According to the invention, the climbing and slope holding capabilities and the stability of the mobile robot can be tested.

The invention relates to a display screen direction dynamic adjusting system and method based on user behaviors, and the system comprises a movable robot platform which is configured to achieve indoor autonomous movement through a bottom drive mechanism which comprises a high-precision motion assembly suitable for various ground materials; the six-degree-of-freedom mechanical arm module is vertically mounted at the top of the robot platform, and a display screen mounting interface is arranged at the tail end; the intelligent sensing module, the integrated infrared sensor array, the nine-axis inertial measurement unit and the depth vision sensor are configured to capture user biological characteristics and environment space data in real time, and through the movable robot platform and the six-degree-of-freedom mechanical arm module, autonomous movement and three-dimensional pose accurate adjustment of a display screen are achieved; and in combination with a machine learning algorithm and a sight tracking technology of the adaptive control module, highly autonomous and accurate user interaction experience is provided.

The invention discloses an automatic driving planning method, device and equipment for a two-wheeled mobile robot and a storage medium, and the method comprises the steps: collecting environment data and vehicle body posture information through a multi-source sensor, and constructing a space-time joint map under a dynamic aerial view coordinate system; a hierarchical trajectory planning architecture is designed based on the four-dimensional state space, an initial path is generated by adopting an improved algorithm, and space-time joint optimization is performed through quadratic programming; a smooth trajectory meeting dynamic constraints is generated in combination with a differential flatness parameterization method, and accurate execution of attitude and motion instructions is realized based on feedforward-feedback cooperative control. According to the method, the problem that the trajectory is not feasible due to neglect of the inclination angle in the traditional automatic driving planning of the two-wheeled vehicle is solved, and the motion stability and safety in a dynamic scene are improved.

The present invention relates to a Livox laser radar-based dynamic object filtering method for a three-dimensional point cloud map. The method comprises: saving point clouds and corresponding poses of all frames of a mobile robot during mapping, setting the saved point cloud of each frame as a query frame, and converting point cloud data of the query frames and of a point cloud map into a fan-shaped grid cluster point cloud descriptor represented by local height extreme value difference information; on the basis of the fan-shaped grid cluster point cloud descriptor, by means of a region-adaptive point cloud ground extraction method, segmenting to obtain a ground point cloud estimation result; and obtaining a potential dynamic region by means of a descriptor comparison method, and on the basis of the ground point cloud estimation result, replacing a dynamic point cloud in an original point cloud map with a ground point cloud segmented in a two-dimensional grid in the query frames. The present invention can eliminate smears generated by dynamic objects in existing three-dimensional point cloud maps, and obtain a static point cloud map allowing for robot positioning and autonomous navigation.

The invention discloses a curve tunnel deformation monitoring data fusion processing method based on a multi-station robot, and belongs to the technical field of tunnel measurement, and the method comprises the steps: obtaining tunnel original point cloud data collected by a plurality of mobile robots and corresponding initial pose data, and constructing a multi-station dynamic unified coordinate system; performing preliminary registration based on a tunnel structure feature point set in the extracted tunnel original point cloud data to generate a preliminary fusion point cloud; performing weighted fusion on the preliminary fusion point cloud according to the fusion error distribution parameter to generate a final fusion point cloud; performing geometric modeling on the final fusion point cloud to generate a deformation monitoring reference model; performing comparative analysis on the deformation monitoring reference model and the tunnel design model to obtain a deformation distribution diagram; and generating a deformation trend prediction report. A unified coordinate system is constructed, multi-source point clouds are subjected to weighted fusion, and time sequence prediction is carried out, so that high-precision three-dimensional modeling and dynamic deformation trend early warning of the curve tunnel can be realized.

The invention provides a mobile robot active positioning method and device for an indoor dynamic scene, and the method comprises the steps: collecting indoor environment data, and constructing an indoor global map which comprises the geometric structure and semantic information of an environment; sensing data collected by the mobile robot in real time are matched with an indoor global map, the pose of the mobile robot in the three-dimensional space is estimated in real time, and the uncertainty index of pose estimation is calculated; evaluating information gains of different potential observation points based on uncertainty indexes of pose estimation, and selecting the observation point with the maximum information gain as a candidate target point; planning a path to the candidate target point; the mobile robot collects sensing data at the candidate target points, the sensing data are matched with the indoor global map again, and the pose of the mobile robot is updated; the indoor environment change is monitored in real time, the indoor global map is dynamically updated, and the indoor global map is regularly corrected and optimized.

The invention discloses a multi-source tight coupling SLAM method based on global information in a dynamic scene, and belongs to the field of mobile robots. According to the method, efficient positioning and mapping are realized through multi-source sensor data fusion and algorithm optimization. The method comprises the following steps of: firstly, eliminating invalid point cloud and data distortion by combining a binocular camera, a single-line laser radar, an IMU (Inertial Measurement Unit) and odometer data and utilizing point cloud filtering, dynamic target detection and motion distortion correction technologies, and fusing observation data of the camera and the laser radar; secondly, IMU and odometer data are fused through unscented Kalman filtering (UKF), and high-precision initial pose estimation is output; and finally, improving a Gmapping algorithm, optimizing a grid map updating module by adopting a subgraph fusion and breadth-first search strategy, and enhancing the environmental feature retention capability of the SLAM module. In a dynamic and complex scene, the method has better robustness, the constructed map better conforms to a real environment, and meanwhile, the pseudo obstacle characteristics in the map can be effectively reduced.

An autonomous mobile robot control method and apparatus, a device and a readable storage medium. An autonomous mobile robot determines a sound source direction according to a voice signal from a user, and determines moving objects around the autonomous mobile robot itself. The autonomous mobile robot determines, from the moving objects, a target object located in the sound source direction, determines a working area according to the target object, and moves to the working area, and executes a task.

A mobile robot tool for use in testing balance of a wheel has a pressure plate assembly having a pressure plate member rotatable about the first attachment element. The pressure plate member is driven along a tool operational axis in one direction to cause the pressure plate member to bear against the wheel rim flat. A cone assembly has a cone block rotatable about the second attachment element. The cone block is driven along the axis in the opposite direction to drive it against the rim center bore on the other side of the wheel. The first and the second attachment elements are screwed together to clamp the rim between the core block and the pressure plate ready for conducting a wheel balance test.

A computer-implemented method of evaluating the performance of a trajectory planner for a mobile robot in a scenario, in which the trajectory planner is used to control the mobile robot responsive to at least one other agent of the scenario the method comprising: determining a scenario parameter set for the scenario and a likelihood of the scenario parameter set; computing an impact score for a failure event or near failure event between the mobile robot and the other agent occurring in the scenario instance, the impact score quantifying severity of the failure event or near failure event; and computing a risk score for the instance of the scenario based on the impact score and the likelihood of the scenario parameter set.

The invention discloses an autonomous mobile robot for large-load assembly in narrow and small space, which comprises a bearing vehicle body walking along with a mobile trolley, a plane adjusting module is mounted on the bearing vehicle body, and a middle connecting plate for plane position adjusting output is mounted at the top of the plane adjusting module. A space adjusting module is installed on the middle connecting plate, and a movable platform for space position adjusting output is installed on the top of the space adjusting module. The invention discloses an autonomous mobile robot for narrow and small space and heavy load assembly, a plane adjusting module of the autonomous mobile robot has three degrees of freedom of X-direction translation, Y-direction translation and rotation around Z, and a space adjusting module has three degrees of freedom of Z-direction translation, rotation around X and rotation around Y. And the two parts jointly form space six-degree-of-freedom motion. The invention has the advantages of high rigidity of the whole machine, good flexibility, large working space and low gravity center, and can realize large-load assembly application in a narrow space.

The invention relates to a dynamic scene vision SLAM method and system based on point-line complementation, and belongs to the field of simultaneous mapping and positioning of mobile robots. According to the method, target prior information monitoring is carried out on an image through a semantic module, and dynamic feature points inconsistent with camera motion are identified; visual feature extraction is carried out on the image through a tracking module, and the visual feature extraction comprises point feature extraction and line feature extraction; removing the point features preliminarily extracted by the tracking module according to the dynamic feature points identified by the semantic module, fusing the obtained static point features and line features, expanding the static point features according to the fused line feature constraints, and screening out key frames according to the point and line features; and finally, completing map construction and optimization updating through a local mapping module and a loopback detection and global BA optimization module. According to the method, mapping limitation and deviation possibly caused by single scene texture information can be overcome, and the real-time performance, the mapping precision and the robustness of mapping can be improved.

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

An unmanned aerial vehicle includes a body. The body has an upper side and a lower side opposite the upper side. At least one rotor is coupled to the body to generate lift in an upward direction. A mechanism is mounted to the lower side of the body. The mechanism includes a landing gear, a storage system, a robotic arm housing, a counterweight housing, a robotic arm, an actuator, and an end effector. The actuator is coupled to the robotic arm to move the robotic arm between a retracted position and an extended position. The end effector is coupled to the distal end of the robotic arm. The end effector includes a gripper with a pair of opposing compressible elements for gripping an object between the pair of compressible elements.

The invention discloses a robot dynamic environment autonomous navigation method based on deep reinforcement learning. The method comprises the following steps: setting an observation space and an action space of a mobile robot; environmental data are acquired and preprocessed, feature extraction is performed on the data of the laser radar and the depth camera, and a mobile robot observation space feature map is generated; acquiring a target position of the robot, generating a path from the current position to a global target by using an A * algorithm, and generating sub-target points according to the current position and the global path; constructing a PPO deep reinforcement learning network, taking the observation space feature map and the sub-target points as the input of the network, and outputting to obtain the state value, the forward speed and the steering speed of the mobile robot to realize autonomous navigation; designing a reward function, establishing a simulation environment, and training a deep reinforcement learning network based on the reward function for autonomous navigation in a real-time complex dynamic environment. The mobile robot is actively prevented from colliding with pedestrians and obstacles while keeping advancing.