1634 results about "Mobile robot" patented technology

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.

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.

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 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.

The invention discloses an autonomous positioning and navigation method and system for a ground mobile robot. The method comprises the following steps: S1, multi-source asynchronous environment perception and initial positioning; s2, carrying out adaptive fusion on multi-source data of time-space association; step S3, global pose optimization under semantic constraint; s4, decision planning driven by the dynamic risk field; and S5, automatically recovering the abnormal state and updating the map. According to the invention, through environmental response type sensor fusion, multi-modal semantic constraint modeling and entropy-driven intelligent decision-making mechanism synergistic effect, industrial pain points of positioning drift, low planning efficiency and insufficient long-term operation reliability in a complex dynamic scene are systematically solved; the centimeter-level positioning precision and the smooth motion trail are kept under the extreme conditions of severe illumination change, dense obstacle disturbance and repeated structure path, the passive operation and maintenance mode that a traditional navigation system depends on manual intervention is thoroughly changed, and a full-autonomous navigation capability closed loop is provided for industrial logistics and commercial service scenes.

The invention discloses an underwater multi-mode mobile robot equipped with a flexible bionic gripper, which integrates three operation modes of propeller propulsion, six-foot crawling and target object grabbing, and realizes'movement-attachment-operation 'integrated collaboration. The robot main body adopts a five-thruster vector layout and has a five-degree-of-freedom motion capability; and six groups of three-degree-of-freedom mechanical feet and two groups of three-degree-of-freedom mechanical arms are arranged outside, and a cruising mode, a crawling mode or a grabbing mode can be automatically switched according to tasks. A coordination dual-network hydrogel bionic sucker and a flexible gripper are integrated at the foot end and the arm end, and controllable adsorption and desorption are achieved through a diaphragm pump. The control system generates a rhythm signal based on the central mode generator, and coordinates multi-module actions of paddles, legs and arms in combination with inverse kinematics solution. The robot is suitable for tasks such as wide water area cruising, complex terrain exploration, environment maintenance and seabed sampling, has high maneuverability and high integration degree, and provides an innovative platform for underwater fine operation.

An exoskeleton system comprising at least one leg actuator unit configured to be coupled to leg of a user, the leg actuator unit including: an upper arm and a lower arm that are rotatably coupled via a joint, the joint 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, a leg-actuator-unit user interface comprising a plurality of input and feedback elements, and an actuator that extends between the upper arm and lower arms.

A system of warehouse orchestration for inventory picking and fulfilment comprising a controller configured to receive order information from warehouse management system (WMS), allocate and distribute a plurality of orders across a plurality of virtual pick zones based on real-time or near real-time demand, pick capacity and received order information. Cause first autonomous mobile robot to move to first virtual pick zone of plurality of virtual pick zones, communicate guidance instruction to first operator to guide first operator to be available at first virtual pick zone and determine pallet loading pattern indicative of distribution of plurality of inventory items in one or more cases and stacking of the one or more cases in one or more layers based on a set of criteria and communicate pick instruction to first operator to pick one or more inventory items and place onto the pallet of the first autonomous mobile robot.

The invention relates to the technical field of mobile robot autonomous navigation path planning, and discloses a complex orchard scene-oriented hybrid sampling global path planning method, which comprises the steps of adopting a Gaussian hybrid sampling strategy, switching between global uniform sampling and Gaussian offset sampling through a probability threshold, and considering global exploration and key area guidance; a dynamic node expansion strategy is adopted, the expansion step length is adaptively adjusted according to the distance between the expansion step length and a target point, and the expansion direction is optimized by fusing vectors facing a sampling point and the target point and introducing an obstacle influence factor; a layered collision detection strategy is adopted, and step-by-step filtering is carried out through rough detection based on a quadtree, secondary screening based on an axis alignment bounding box and accurate geometric judgment in sequence so as to improve the efficiency; and a dynamic iteration termination mechanism is adopted, and the minimum number of iterations and the path quality convergence state are combined as composite termination conditions. The method can quickly generate a smooth and safe path, and is especially suitable for structured scenes such as orchards.

The invention provides a mobile robot vibration sensing trajectory optimization method based on a physical information neural network, and the method comprises the steps: obtaining an RGB image and a depth image through a foresight camera, extracting semantic information of a topographic type and geometric information representing the topographic relief degree, and constructing an obstacle navigation cost map based on the semantic information and the geometric information; predicting the vibration influence of the terrain on the robot suspension system through a physical information neural network based on the semantic information, the geometric information and the robot state data so as to generate a vibration prediction navigation cost map; obtaining a final navigation cost map through map fusion; generating a local minimum cost trajectory on the final navigation cost map by using a trajectory planning algorithm; controlling the robot to run along the local minimum cost track; and a two-stage scheme is adopted in the training process of the physical information neural network to accelerate network training. Therefore, the hardware cost can be reduced, the physical information neural network training can be accelerated, the physical feasibility and smoothness of the trajectory can be improved, the tracking error can be reduced, and the safe and stable operation of the robot in the unstructured environment can be ensured.

The invention discloses a mobile robot autonomous search navigation method based on a visual language large model. The method comprises the steps of receiving a natural language instruction of a user, and synchronously collecting an RGB image and a laser point cloud; two-stage visual language reasoning is adopted, and target semantic analysis and existence judgment are completed in sequence; when the target does not exist, autonomous exploration is executed based on the point cloud data and is parallel to target judgment, and an exploration-judgment closed loop is formed; after determining that the target exists, switching to a visual language navigation model, generating an incremental motion instruction, and guiding the robot to approach the target; target state monitoring is continuously carried out during navigation, if the target is lost, a dynamic backtracking mechanism is triggered, the robot is driven to return to the historical state of the recently confirmed target, and navigation is restarted. The method does not need to depend on a prior map, can execute efficient and robust autonomous search and navigation tasks on edge equipment through task collaboration, model quantification and state backtracking, and is suitable for complex scenes such as industrial inspection and emergency rescue.

A multi-arm robotic harvesting apparatus is provided. In another aspect, a robotic harvesting apparatus and method automatically optically locate a fruit in a tree, move and align an arm 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, and the apparatus includes a shared optical perception system and programmable controller.

The invention relates to the field of intelligent monitoring, and discloses a clean room pollutant purification method and system based on flow field characteristics and a storage medium thereof.The method comprises the steps that a mobile robot inspection route and operation parameters are set according to the requirements of a clean room; the robot inspects according to a route, stops every time the robot moves a basic distance and monitors pollutant concentration and flow velocity; calculating the time-average concentration, the time-average flow velocity, the flow velocity gradient and the change frequency of the stagnation point pollutants; executing a monitoring strategy according to a threshold value, and starting an autonomous inspection algorithm to move along a flow velocity gradient direction; judging whether the flow velocity gradient is smaller than a convergence threshold, if so, determining a local extreme point and updating the inspection route; and if the pollutant concentration at the extreme point exceeds the standard, starting the purification device, otherwise, enabling the robot to return to the fixed route for continuous inspection. According to the method, the flow field characteristics are taken as preposition indexes, the autonomous identification and path optimization capability is improved, the workload and the detection time are reduced, and the monitoring efficiency, the accuracy and the maintenance intelligence level are improved.

The invention relates to the field of mobile robots, in particular to a four-wheel omni-directional robot chassis navigation control device and method.The four-wheel omni-directional robot chassis navigation control device comprises a chassis structure and a driving system, a radar and a mechanical arm are arranged on the chassis structure, and omni-directional wheels are installed in the centers of the four side walls of the chassis structure; the axes of the omni-directional wheels are parallel to the edge of the chassis structure, the axes of the adjacent omni-directional wheels are perpendicular to each other, the diagonal direction of the chassis structure serves as the advancing direction of the robot, the advancing direction of the omni-directional wheels and the overall advancing direction of the chassis structure form an included angle of 45 degrees, and the driving system comprises four motors with Hall encoders. The four motors independently drive the independent omnidirectional wheels, the upper computer and the lower computer conduct instruction issuing and state feedback through UART serial port communication, and the problems that at the present stage, steering is conducted by depending on steering engine driving or positioning is conducted through a single sensor, the structure is complex, the anti-interference capacity is limited, and operation is complex are solved. And high precision is difficult to maintain in a dynamic environment.

The invention discloses a storage robot cluster management system and method, and belongs to the technical field of robot scheduling. The system comprises a central management server and an autonomous mobile robot cluster. The central management server comprises a congestion prediction and global path planning module; the congestion prediction and global path planning module comprises a congestion prediction sub-module and an A * path planning sub-module with space-time cost; the autonomous mobile robot is internally provided with a multi-sensor sensing and communication module and a local decision module, and the off-line centralized training platform is used for optimizing the local decision module in each autonomous mobile robot through a centralized evaluation network with global information in a simulation environment. The invention provides a hybrid control architecture integrating global path planning, dynamic task allocation and local real-time decision based on multi-agent reinforcement learning, and efficient and smooth cluster collaborative operation can be realized.

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

The invention relates to a scene deformation risk early warning method and device based on a mobile robot, and the method comprises the steps: obtaining perception information uploaded by at least one mobile robot, the perception information comprises real-time three-dimensional point cloud data collected by a 3D perception sensor carried by the mobile robot and a high-precision pose of the mobile robot calculated in real time based on the same frame of point cloud data; according to the pose of each mobile robot, converting the real-time three-dimensional point cloud data of at least one mobile robot into a unified global coordinate system, and performing splicing and fusion through a point cloud registration and fusion algorithm so as to incrementally construct and dynamically update a current three-dimensional morphology model; comparing the current three-dimensional shape model with a historical reference three-dimensional shape model, and calculating the deformation quantity of the target structure body in the operation scene; and when the deformation quantity exceeds a preset safety threshold value, risk early warning information is generated and output, so that the monitoring efficiency can be improved, and real-time early warning can be realized.

A method of grasping and / or placing multiple objects by a gripper of a mobile robot. The multi-grasp method includes determining one or more candidate groups of objects to grasp by the suction-based gripper of the mobile robot, each of the one or more candidate groups of objects including a plurality of objects, determining a grasp quality score for each of the one or more candidate groups of objects, and grasping, by the suction-based gripper of the mobile robot, all objects in a candidate group of objects based, at least in part, on the grasp quality score. The multi-place method includes determining an allowed width associated with the conveyor, selecting a multi-place technique based, at least in part, on the allowed width and a dimension of the multiple grasped objects, and controlling the mobile robot to place the multiple grasped objects on the conveyor based on the selected multi-place technique.

The invention relates to a digital twin-driven distributed warehousing system and method, and belongs to the technical field of intelligent logistics. The system comprises a sensing execution layer, a network transmission layer, an edge computing layer, a cloud platform layer and an application service layer. The method comprises the following steps: acquiring storage physical entity data through a perception execution layer; the data is transmitted to the edge computing layer and the cloud platform layer through the network transmission layer; in the cloud platform layer, a high-fidelity virtual warehouse is constructed and synchronized through a digital twin engine; using a multi-agent reinforcement learning algorithm to perform centralized training in a digital twin environment to generate a distributed cooperative scheduling strategy; and issuing the strategy to an edge computing layer and a sensing execution layer, and driving an autonomous mobile robot cluster to execute a cooperative task. According to the invention, step-type improvement of the warehousing system in flexibility, intelligence and cooperative capability is realized, and the problems of rigid bottleneck, non-optimal decision and low cooperative efficiency of a traditional system are solved.

An automatic cleaning method and apparatus of a mobile robot, a mobile robot, and a storage medium. The automatic cleaning method comprises collecting a current image of a scene, recognizing an object in the current image, and determining the type of the object (S101); marking the recognized object as a preliminary obstacle on a map of the scene (S102); and on the basis of the type of the preliminary obstacle, performing a corresponding cleaning operation on the basis of a wall-following sensor (S103). By means of image recognition and a wall-following sensor, objects in a scene are intelligently recognized and classified, and accordingly a precise cleaning operation is performed, thereby improving the cleaning efficiency and accuracy of mobile robots.

Systems and methods are described for combining sensor data obtained by a mobile robot. A system can obtain first sensor data from one or more first sensors of a robot. The system can determine a distance between the robot and at least a portion of the environment based on the first sensor data. For example, the distance may be a depth from a depth map. The system can obtain second sensor data from one or more second sensors of the robot. The system can combine a first portion of the second sensor data and a second portion of the second sensor data based on the distance. For example, the system can use the distance to determine a seam for combination of the first image and the second image. The system can instruct output of a user interface based on the combination.

The invention relates to the technical field of industrial equipment automatic detection and fault diagnosis, in particular to a fault detection method and equipment for driving equipment and a medium. And the mobile robot and the cloud server perform cooperative execution. The method comprises the following steps: generating a global detection path based on mobile robot electric quantity, communication load and detection precision mapping; the mobile robot sequentially moves to each detection point; identifying the identification code and uploading the identification code to a cloud; the cloud dynamically generates a detection instruction set based on equipment historical data and issues the detection instruction set to the mobile robot; the mobile robot executes the instruction to acquire equipment state data and uploads the equipment state data; and the cloud end compares the data with a normal threshold value, and instructs the mobile robot to detect again or continue the task according to a result. According to the invention, through combination of intelligent path planning and cloud dynamic decision, high efficiency, accuracy and adaptive capability of driving equipment fault detection in a complex environment are realized.

The invention relates to the technical field of mobile robot control, in particular to a multi-modal sensing and decision-making integrated system for a mobile robot, and provides the following scheme: performing joint smooth state estimation on multi-source sensing data to construct a state version diagram containing a time evolution relationship and a version evolution relationship; and under the constraint of an action locking window, establishing an action prefix index structure in combination with a historical action sequence, executing limited search on the state version diagram, and generating an updated action sequence with the minimum difference from the historical action structure. According to the method, the unstable change of event triggering and state machine transition can be inhibited under the condition that the smooth state estimation result is subjected to backtracking correction, the causal consistency and reproducibility of the action sequence are kept, and the operation stability and decision reliability of the mobile robot under the complex working condition are improved.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

The invention discloses a wind power cabin inspection robot, and relates to the technical field of operation and maintenance of wind power generation equipment. Comprising a walking mechanism, an installation mechanism and a detection mechanism, the walking mechanism comprises a left installation frame, a right installation frame is arranged on one side of the left installation frame, a notch is formed in one side of the right installation frame, and an insertion strip is inserted into the notch. Through the arrangement of the walking mechanism, the mounting mechanism and the detection mechanism, in the use process, after the detection mechanism moves to a target area, a second motor drives a third gear to drive a second gear to rotate, so that a rotating rod rotates around a connecting frame, the transverse detection range is expanded, and meanwhile, the third motor drives a rotating shaft to rotate; the device can cover most of equipment and dead angles in a cabin, and the visual angle can be adjusted without moving the robot body.

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

The invention relates to the technical field of robots, in particular to a mobile robot remote operation system and method based on mixed reality, and the method comprises an operator end and a robot end, the operator end completes a complex remote operation task based on visual feedback provided by a man-machine cooperation interaction interface, and the robot end completes a remote operation task based on visual feedback provided by the man-machine cooperation interaction interface. The virtual reality controller integrates a motion state and an operation mode of the mobile robot, and the mixed reality server generates visual feedback according to visual information of a remote operation visual angle of the mobile robot; the robot end comprises a mobile robot, a collection device on the mobile robot and a robot server, and after the mobile robot receives a remote control instruction sent by the virtual reality controller to the robot end, posture and button information of the mobile robot are input into the robot server to drive the mobile robot to move. Therefore, the problems of low real-time performance and accuracy of remote control, poor operation efficiency and flexibility and the like caused by limitation of bandwidth and system time delay in related technologies are solved.

The invention relates to the technical field of robots, and discloses an obstacle sensing method and a storage medium, and the method comprises the steps: obtaining image data of an environment in front of a mobile robot and three-dimensional point cloud data generated through laser radar scanning; pixel-level semantic segmentation is carried out on the image data, and a semantic segmentation map with each pixel having a semantic tag is generated; projecting each three-dimensional point in the three-dimensional point cloud data to a semantic segmentation map based on a calibration relationship between a camera and a laser radar, and generating a semantic point cloud with a semantic tag; for a detection blind area of the laser radar in front of the robot, generating a virtual three-dimensional point cloud in the detection blind area by using obstacle semantic information corresponding to an image area of the detection blind area in the semantic segmentation map; and determining the obstacle point cloud based on the three-dimensional point cloud of which the semantic label is the obstacle category in the semantic point cloud and the virtual three-dimensional point cloud. According to the invention, the obstacle detection precision in a complex environment can be improved.

A computing system may be configured as a fleet controller for autonomous mobile robots operating within a physical environment. The system may include a communication interface receiving sensor data from the robots including image data captured by visible light cameras located on the robots, an environment mapper determining a scene graph representing the environment and identifying navigable regions of the environment, a workflow coordinator determining a workflow including tasks to be performed within the environment by one or more of the robots in cooperation with a human, and a route planner configured to determine routing information for a robot, which may autonomously navigate the environment to execute the tasks based on the routing information.

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