3782 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 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.

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 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 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.

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 discloses a mobile robot target following method fused with a local path planning algorithm, and the method comprises the steps: carrying out the target detection of an environment image obtained by a mobile robot through a FastDet target detection network, and generating a detection frame which is used for representing the position and size of a target in the environment image; extracting the detection frame as an initial region through a KCF visual tracking algorithm, performing feature extraction in the initial region, and outputting center pixel coordinates of the target; converting the center pixel coordinate into a three-dimensional coordinate in a world coordinate system to obtain an actual position of the target in a three-dimensional space; and generating a plurality of candidate paths by using a DWA local path planning algorithm, and selecting an optimal path based on the actual position of the target and in combination with the current pose of the mobile robot. According to the mobile robot target following method fused with the local path planning algorithm, the problem that an existing mobile robot is difficult to meet real-time target tracking in a complex environment is solved.

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 belongs to the related technical field of mobile robots, and discloses a rolling-walking composite motion control method and device for a double-wheel-foot robot, and the method comprises the steps: (1) simplifying the rolling motion of wheels of the double-wheel-foot robot into the sliding motion of a sliding block; (2) constructing a system kinetic equation of the double-wheel-foot robot based on the simplified kinetic model; constructing a constraint equation based on the contact state of the two legs and the ground; discretizing the system kinetic equation, the cost function and the constraint equation, and converting model prediction control problems corresponding to the system kinetic equation, the cost function and the constraint equation into nonlinear problems for numerical solution so as to complete leg motion planning; (3) calculating the rotation speed of the wheel based on the leg motion planning result; and (4) based on the rotation speed of the wheels and the leg motion planning result, a whole-body control algorithm is adopted to calculate and obtain moment instructions of leg joints and the wheels. According to the invention, the state space dimension is reduced, and the wheel-ground contact constraint is simplified.

The invention comprises a method and a system for conveying a mobile robot in an elevator involving the monitoring of elevator operating components and of persons surrounding the elevator and operating the elevator by selecting a floor to which the mobile robot is to move. In one aspect an inertial sensor is used to learn or determine the number of floors. The robot may also have methods implemented to assess the floor space inside the elevator or to position itself in front of the elevator in such a way that it can, for example, monitor the operation of elevator operating components without being perceived as an obstacle from the elevator users' perspective.

Disclosed in the present invention is a networked multi-robot data-driven formation control method under a preview mechanism. A leader-follower formation method is used, wherein a preview point is determined on a reference trajectory, and by means of a cross-track error between a current position and the preview point, a model-free adaptive control (MFAC) algorithm is used to control an angular velocity; each follower uses the coordinates of a leader as the preview point, and the angular velocity and a linear velocity are respectively controlled by means of the cross-track error and an along-track error between the current position and the preview point; and in the algorithm, the angular velocity is controlled by single-closed-loop MFAC, the linear velocity is controlled by dual-closed-loop MFAC, an error term is controlled by weighted collaborative errors of each robot, forward prediction is used to compensate for network communication constraints in control algorithms, and a velocity control quantity after predictive compensation is transmitted to an end-device robot by means of an edge-end network, thereby realizing stable specific-pattern coordinated formation of multiple mobile robots. The method has stronger scenario applicability, flexibility and scalability.

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 provides a path planning method based on machine learning in an easy-to-degrade environment, and relates to the fields of path planning, machine learning and the like. The method comprises the specific steps of firstly processing point cloud data through semantic segmentation and fuzzy evaluation to generate a semantic map containing risk information, then taking a global path generated by an A * algorithm as prior information of a DQN algorithm, then designing a reward function which comprehensively considers safety, efficiency and shortest path, and optimizing a local decision through the DQN algorithm. And finally, fusing the local path and the global path generated by the DQN to generate a continuous, smooth and safe optimal path. Compared with the prior art, the method not only can effectively cope with the problem of performance reduction of the laser radar in a degraded environment, but also can remarkably improve the path planning safety and robustness of the mobile robot in a complex environment, has real-time performance and expansibility, and can be widely applied to the fields of service industry, industry, transportation industry and the like.

A method implemented at an autonomous mobile robot equipped with a fork to carry a pallet. The robot transitions between a first and second piecewise flat floor segments with differing geometries. The robot uses sensor data from sensors such as LIDAR, stereo cameras, GPS, and ultrasound sensors to determine the transition between the first and second piecewise flat floor segments. The fork operates based on parameters that meet reference constraints. When the robot detects that a second floor geometry would cause these parameters to no longer meet the reference constraints, the robot determines new parameters that will satisfy the reference constraints. Control signals are then sent to adjust the fork's operation as the robot transitions to the second floor segment.

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.

A novel automatic installation robot system for raised floor, comprising an AMR (autonomous mobile robot) chassis (1), a mechanical arm body (2) and a mechanical arm tail end (3). The mechanical arm body (2) is mounted on the AMR chassis (1), and the mechanical arm tail end (3) is mounted on the mechanical arm body (2). The mechanical arm tail end (3) comprises: an installation frame part, a pneumatic suction cup module, a sensing module and a support height-adjusting module (a). One or more composite robots are used for cooperative work to automatically complete the whole process of installing the raised floor having pre-installed supports, or human-machine cooperation independently completes installation, leveling and height adjusting of the floor and the supports, thus improving the installation precision and efficiency, reducing the labor intensity of personnel, improving the installation consistency and stability, and reducing the comprehensive cost.

The invention provides a fusion positioning mapping method using a single anchor point external confidence source. The method comprises the following steps: step 1, arranging UWB anchor points; 2, initializing the mobile robot; 3, processing a front-end point cloud map; step 4, processing UWB ranging information; step 5, key frame judgment; and step 6, optimizing a back-end factor graph. The invention provides a multi-sensor synchronous positioning and mapping system integrating a 3D laser radar, an inertial measurement unit and an ultra-wideband sensor, and aims to improve the positioning precision and trajectory estimation performance of a mobile robot in a complex environment. According to the method, a robust and efficient positioning scheme is provided by combining data of three sensors through a factor graph optimization method.