2932 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 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 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 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.

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.

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 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.

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.

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 tunnel structure water leakage identification method for robot dog detection, and the method comprises the steps: firstly, guaranteeing the safety of an inspection robot dog in photographing a picture through constructing an environment perception algorithm based on the fusion of a laser radar point cloud and a camera laser point cloud; then, mobile shooting is carried out on the water leakage condition of the tunnel structure through inspection mobile robot dog equipment, and data preprocessing is carried out on images; and finally, constructing tunnel internal connection leakage water identification data features, and proposing an improved Yolov8 + HorNet + DASI + CBAM model to identify the missing condition of leakage water. According to the invention, the improved Yolov8 + HorNet + DASI + CBAM model is embedded into the inspection robot capable of carrying the camera, so that the problem that inspection workers are difficult to enter an underground structure is solved, leakage water occurring in an underground complex environment is accurately identified, potential safety hazards caused by leakage water occurring in the underground structure are eliminated, and the operation safety of the underground structure is guaranteed.

The invention discloses an intelligent road surface recognition method based on force perception and image analysis. The method comprises the following steps: S1, acquiring image data and contact force data in the advancing process of a mobile robot; s2, inputting the image into a two-channel feature network, and extracting structural features and fuzzy region information; s3, processing through an unwrapping model to form an image semantic vector; s4, constructing a contact time sequence, extracting behavior characteristics, and generating a contact event code; s5, performing alignment and weighted fusion on the image semantic vector and the contact code, and outputting a combined pavement state vector; s6, inputting a trafficability scoring model, identifying a ground type and generating a trafficability score; and S7, transmitting the result to a navigation module for path planning and motion control. Visual and force sense information is combined, and the complex terrain recognition accuracy and the robot traffic capacity are improved.

The invention relates to the technical field of mobile robot control, and discloses a wheeled robot trajectory tracking anti-interference control system, which comprises a nonlinear dynamics modeling module used for establishing a nonlinear dynamics model of a wheeled robot so as to describe the motion characteristics of the robot under the influence of ground friction force, sliding force and modeling errors; the disturbance estimation module is used for estimating external disturbance and system modeling errors in real time through an extended state observer and providing disturbance compensation data for the controller; and the sliding mode control module is used for realizing rapid convergence of track deviation by constructing a sliding mode surface and enhancing the anti-interference capability of the system by dynamically adjusting the sliding mode gain. According to the invention, through nonlinear dynamic modeling, disturbance estimation, sliding mode control and model prediction control fusion and real-time trajectory optimization, high-precision trajectory tracking is realized, and the anti-interference capability and the dynamic environment adaptability are enhanced.

Disclosed in the present invention is a material picking method based on a mobile robot, comprising the following steps: step S1, acquiring a real-time production plan and a bill of materials, wherein the bill of materials comprises material codes, material names, material quantity and lead time; step S2, on the basis of the material codes, dividing the bill of materials into a plurality of task units suitable for single operation of the mobile robot, and assigning corresponding picking stations; step S3, performing picking state labeling on the task units on the basis of the lead time of materials in the task units, and assigning corresponding mobile robots on the basis of the labeled picking states; and step S4, the mobile robots performing path planning on the basis of the assigned task units, and executing the assigned task units on the basis of the planned path, so that racks to which the materials in the assigned task units belong are transported to specified picking stations for operators to pick the required materials.

Proposed is a mobile robot utilizing the uncertainty of map matching poses for localization. The mobile robot includes a point cloud sensor which acquires point cloud data through scanning, a matching pose calculation part which applies a pre-registered point cloud map and the point cloud data to a pre-registered scan-map matching algorithm to calculate a map-matching pose of the mobile robot, and an uncertainty estimation part which estimates uncertainty of the map-matching pose on the basis of a probability distribution of a preset pose space around the map-matching pose. Through this, the uncertainty of the map-matching pose is estimated to evaluate the reliability of the map-matching pose, enabling more precise localization.

The invention relates to shipbuilding flatness inspection equipment and a use method thereof, and belongs to the technical field of shipbuilding flatness inspection. The shipbuilding flatness inspection equipment comprises a magnetic adsorption type mobile robot platform, a high-precision vision module, an edge computing unit and a cloud data management platform; the magnetic adsorption type mobile robot platform is provided with a crawler-type walking mechanism and a self-adaptive vacuum adsorption unit. According to the invention, efficient and high-precision whole ship flatness intelligent detection can be realized through multi-sensor fusion and a self-adaptive robot platform, the system is suitable for real-time quality monitoring in ship body section construction, deck assembly and fitting-out stages, local fine scanning can be executed along a welding seam area in a ship body section detection scene, and the detection accuracy is improved. A welding deformation out-of-tolerance area is synchronously marked, a non-key plane area can be switched to a global rapid scanning mode, and in the deck assembly stage, a cloud platform can integrate data in real time and assist in carrying out an assembly dislocation correction scheme.

The invention relates to a rapid three-dimensional construction and recognition method for a complex scene, belongs to the technical field of intelligent mobile robots or unmanned vehicles, and solves the technical problems of low three-dimensional construction efficiency and low semantic recognition accuracy of the complex scene. Comprising the following steps: acquiring a point cloud registration image and an RGB-D image of a complex scene; performing multi-scale feature extraction based on the RGB-D image to obtain multi-scale image features; obtaining multi-scale scene point cloud features of the point cloud registration graph; performing multi-level feature fusion on the multi-scale image features and the multi-scale scene point cloud features to obtain a multi-scale fusion feature vector, and performing voxelization representation based on the multi-scale fusion feature vector to obtain a voxelization three-dimensional scene; and reasoning the voxelized three-dimensional scene to obtain a semantic recognition result corresponding to the complex scene. And rapid three-dimensional scene construction and category identification of a complex scene are realized.

The invention discloses a path planning method and system for a mobile robot, and relates to the technical field of path planning methods, and the method comprises the steps: determining a path planning model based on optimized point cloud data, environment complexity and the resolution of a visibility graph; in the determination of the path planning model, the optimized path is determined based on the two-way search of the starting point and the end point, the accuracy of the path planning model is ensured, rapid and accurate path planning is realized based on the two-way search of the starting point and the end point, and the accuracy of the optimized path is ensured. Collecting a plurality of path nodes, and dynamically optimizing each path node; according to the method, the obstacle features are collected according to detection of the optimized path, adaptive filtering is performed on the obstacle features, dynamic optimization of the path nodes and adaptive filtering of the obstacle features are compatible, so that the obstacles which have small influence on path planning can be adaptively filtered, and the calculation burden of path planning can be remarkably reduced.

The invention discloses an inertial vision positioning method and system for a mobile robot in a weak texture dark light environment. The method comprises the following steps: weak light image enhancement; extracting feature information for matching and performing IMU pre-integration to obtain a pose relation of adjacent key frames; visual initialization and visual inertia joint initialization are carried out, and a point-line feature and inertia error model is established; and constructing a system residual model based on a sliding window method to carry out local optimization, and outputting position and attitude information. According to the method, the problem that the feature discrimination and uniqueness of the mobile robot in the weak light environment are remarkably reduced is effectively solved, and the positioning precision and robustness in the weak texture and dark light environment are effectively improved.

The disclosure provides systems and methods for identifying salient test runs involving an autonomous vehicle system. A processor receives sets of run data, each set representative of a driving scenario. For each set, an output set is generated, the output set comprising time-indexed events generated in response to a detected behaviour of at least one challenger agent, and a sequence of decision indicators indicating whether a driving action by an ego agent would be permissible. A data retrieval component is coupled to a results database and retrieves output sets based on the time-indexed events and the sequence of decision indicators. The processor generates the sequence of decision indicators by generating a planned trajectory of the ego agent, and determining whether the predefined driving action by the ego agent would be permissible.

The invention discloses a BEV perception optimization method based on semantic projection compensation and edge structure enhancement, and the method comprises the steps: constructing an image edge feature pyramid under convolution perception through an MSEII module, and enhancing the boundary significance of a weak texture region; and then image semantic embedding is extracted by using a deep semantic segmentation network, geometric projection is carried out based on camera-radar external participation internal reference, image semantic features are mapped to a point cloud space, and an SPE module is constructed to complete semantic completion and structure alignment. After the image and the point cloud features are fused in a unified BEV coordinate system, a spatial context relation is further modeled through sparse point convolution, and a three-dimensional target bounding box and a semantic tag are output through a decoupling detection head. According to the method, on the basis of guaranteeing model lightweight and deployment efficiency, the long-distance target detection capability and semantic consistency in a shielding scene are remarkably improved, and the method is suitable for multi-mode BEV perception application in other resource-constrained equipment such as a mobile robot.

The invention discloses an operation analysis method and system for a mobile robot fused with an industrial mechanical arm, and the method comprises the steps: receiving a work order, then splitting a task, determining sub-task parameters, and optimizing the predicted time based on historical data; mapping and positioning are synchronized in the moving process, and the positioning precision is guaranteed through an SLAM model and prediction-correction logic; planning a global path and a residence position, and coping with a dynamic obstacle through a weighted scoring method and an MPC model; the operation position is precisely aligned to compensate the ground unevenness error; recognizing a workpiece and selecting an optimal grabbing point; the contact force and rigidity are dynamically adjusted when operation is executed, and automatic retreating and fine adjustment are performed when abnormity occurs; the grabbing quality and the advancing safety are confirmed; and data is updated after ending, and low-power automatic charging is realized. According to the method, the problems that a traditional technology path and operation planning are separated, sensing is single, robustness is poor, an operation strategy is difficult to adapt, and a standardized quick change and calibration mechanism is lacked are solved.

The invention relates to the field of mobile robot positioning, in particular to a LiDAR-inertia-vision fusion SLAM switching positioning method applied to a degraded environment. According to the method, through a multi-sensor system composed of a monocular camera, an inertial measurement unit and a laser radar which are installed on a mobile robot, original measurement data such as images, angular velocity, linear acceleration and dense space point cloud are acquired, a system state initialization model is constructed, and the multi-sensor system is used to acquire the measurement data. According to the method, the vision-inertia poses and the laser radar poses or linear interpolation of the vision-inertia poses and the laser radar poses are fused, the degradation state can be effectively recognized in real time, and multi-sensor system information switching can be carried out, so that the problem of positioning failure of the laser radar and the vision-inertia odometer module in the degradation environment can be effectively solved; the overall visual SLAM positioning precision and robustness are improved, and the long-term stable operation requirements under the severe conditions of single structure, complex illumination, sparse texture and the like are met.

A method in accordance with a particular embodiment of the present technology includes receiving, at data-processing hardware operably associated with a mobile robot, perception data corresponding to an object in an environment of the mobile robot. The method further includes generating, by the data-processing hardware and based at least partially on the perception data, a feature reference corresponding to a feature of the object. The method also includes generating, by the data-processing hardware, candidate object references based at least partially on different respective alignments between the feature reference and a model of the object. The method also includes selecting, by the data-processing hardware, one of the candidate object references based at least partially on a result of evaluating the candidate object references for accuracy. Finally, the method includes controlling, by the data-processing hardware, the mobile robot based at least partially on the selected candidate object reference.

The invention discloses a robot movement and manipulation control method based on a large model and deep reinforcement learning, and belongs to the technical field of artificial intelligence and intelligent robot systems. The problems that an existing method is difficult to process complex natural language instructions, and in a dynamic environment, the cooperation efficiency of a robot moving task and a mechanical arm control task is low are solved. According to the method, the input complex natural language instruction is processed by adopting the large language model to obtain the subtask sequence, so that the understanding of the robot on the natural language instruction can be realized; through a hierarchical reinforcement learning framework, environment semantic information is deeply fused, an interpretable risk assessment mechanism is introduced, and the system is endowed with continuous learning and generalization ability; the cooperative efficiency of autonomous navigation and manipulation tasks of the mobile robot in complex, dynamic and unknown environments can be remarkably improved, and the safety and the intelligent level of the robot are guaranteed. The method can be applied to the field of robot movement and manipulation control.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

The invention discloses a fork type mobile robot power distribution unit dynamic distribution control method and system, and relates to the technical field of intelligent robots, and the method comprises the steps: arranging sensors at a robot PDU input end and each output channel, collecting an operation data set in real time, and carrying out the preprocessing to form a dynamic feature data set; the main control microcontroller carries out power response analysis and evaluates a channel response state based on the dynamic characteristic data set, generates a preliminary power distribution instruction, calculates a target current MI during preliminary power distribution, carries out thermal stress analysis, and constructs a thermal stress identification index Htt to carry out thermal accumulation risk evaluation; when a heat accumulation risk exists, calculating a channel target current MII when the secondary power supply is distributed, and adjusting the duty ratio of an MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) switch to carry out heat suppression regulation and control; and after the distribution is completed, calculating an energy utilization rate Nsys and executing energy efficiency state evaluation. According to the method, adaptive distribution and safe operation management of the multi-channel power supply of the robot are realized.

The invention relates generally to a mobile robot, e.g. a humanoid robot, configured to provide reality capture and metrology grade geometric measurement, e.g. to generally support infrastructure surveillance and / or to support workflows in the field of metrology. Aspects of the mobile robot, inter alia, relate to providing increased accuracy of metrology grade devices to overcome deficiencies in mobile reality capture. On the other hand, benefits of mobility provided by mobile robots are transformed to the field of metrology while maintaining metrology grade accuracy.

The invention discloses a laser wireless energy transfer dynamic MPPT control method based on an RL-PSO hybrid algorithm and a reconfigurable array system, and aims to solve the problems of power oscillation and mismatch loss caused by light spot offset of a photovoltaic array under laser dynamic irradiation. According to the method, reinforcement learning (RL) and particle swarm optimization (PSO) are fused, dynamic parameters such as the light spot moving speed and irradiance are sensed in real time through the RL, the inertia weight and learning factors of the PSO are adjusted in a self-adaptive mode, and collaborative optimization of global search and local tracking is achieved. According to the system level, a reconfigurable photovoltaic array is designed, and according to data of a light spot position sensor, battery series-parallel topology is dynamically adjusted through a switch matrix, and current matching is optimized. According to the method, double-loop control is adopted, wherein an outer-layer RL decision module dynamically switches PSO working modes according to the environment, and an inner-layer PSO module optimizes the working voltage. The system efficiency in a dynamic scene can be improved by 15%-30%, mismatch loss is reduced by 40% or above, and the method is suitable for unsteady-state energy supply scenes such as unmanned aerial vehicles and mobile robots.

An exoskeleton system comprising a leg actuator unit that is configured to be coupled to a leg of a user. The leg actuator unit includes: an upper arm and a lower arm that are rotatably coupled via a rotatable joint, the rotatable joint configured to be positioned at a knee of the user with the upper arm coupled about an upper-leg portion of the user above the knee and with the lower arm coupled about a lower-leg portion of the user below the knee. The upper arm is configured to be coupled to the upper-leg portion above the knee via a first set of couplers that includes a first upper-leg coupler, the lower arm is configured to be coupled to the lower-leg portion below the knee via a second set of couplers that includes one or more lower-leg couplers associated with a lower-leg brace, and an actuator extends between the upper arm and lower arm, the actuator configurable to move the upper arm and lower arm.

The invention belongs to the technical field of mobile robots, and particularly relates to an amphibious hexapod robot which comprises a robot body, six walking feet, a protection box, propellers, a multi-sensor system and a central control unit. The six walking feet are arranged on the two sides of the robot body, and a waterproof driving joint is arranged at the tail end of each foot. The protection box is arranged at the root of the walking foot and internally integrated with propellers; the sensor system comprises a water depth sensor, an attitude sensor and a grounding detection module; the central control unit is used for collecting sensor data in real time, judging whether the current environment is a land or a water area, automatically switching a walking mode or a water propulsion mode, and meanwhile adaptively adjusting gaits and the rotating speed of propellers according to the water depth and the terrain. The robot has environment perception, mode decision, gait generation, grounding prevention protection and energy optimization strategies, full-autonomous and high-adaptability movement in the land and water environment is achieved, and the working capacity and safety of the robot in the complex environment are improved.