1256 results about "Robotics" patented technology

The invention discloses a manipulator grabbing method based on deep learning target detection and image segmentation, and relates to the technical field of artificial intelligence and robotics.The manipulator grabbing method comprises the following steps that a scene image to be processed is collected, the image quality is improved through the multi-light-source fusion image enhancement technology, and recognition errors caused by uneven illumination are reduced; and inputting the enhanced image to a pre-trained deep learning model, executing a target detection task, and outputting an initial bounding box and a category label of the target object. According to the method, through multi-light-source image enhancement and high-precision image segmentation, the accuracy of target recognition and contour extraction is remarkably improved, and the capture failure rate caused by image misjudgment is reduced. And meanwhile, geometric consistency verification and a multi-factor grabbing scoring mechanism are introduced, dynamic screening and collision pre-detection are conducted on the paths, the grabbing stability and safety of the mechanical arm in the complex environment are effectively guaranteed, and the intelligence and robustness of the whole system are remarkably improved.

The invention discloses a welding path planning method based on machine vision, and relates to the technical field of welding robotics.The method comprises the steps that sample data of various groove types are collected, classified and stored, a groove type database is established, and an extraction condition template is defined; groove section data are obtained through the machine vision technology, a three-dimensional model is constructed after grouping, welding seam, material and heat affected zone characteristics are analyzed, a welding sequence principle is determined, and a comprehensive welding path is generated; key parameters in the welding process are monitored in real time, an early warning mechanism is set, and the optimal path is dynamically adjusted; updating a database according to the path planning efficiency value, comprehensively analyzing candidate paths, and recommending an optimal welding path; the extraction condition template is dynamically adjusted, the precision, adaptability and efficiency of welding path planning can be improved, and the welding quality and stability are ensured; and a plurality of different cross section data are analyzed, the comprehensive welding path is determined, and various different cross section data characteristics can be met at the same time.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

Implementations relate to generation of segmentation masks for images in a zero-shot, unsupervised manner. Implementations also relate to generation of labels for the segmentation layers of the segmentation mask. Implementations use self-attention maps from a pass of the image through a generative image model to determine the segmentation mask and may use cross-attention maps generated when a prompt describing the image is provided with the image to the generative image model. Implementations aggregate maps from different resolutions to determine the mask and labels. The disclosed techniques enable accurate segmentation for any image without apriori training, facilitating applications in image processing, computer vision, extended reality applications, and robotics.

The invention discloses a control method and equipment for an intelligent robot with a body and a storage medium, and belongs to the technical field of robots. The method comprises the steps of receiving a task instruction and environment perception data, performing fusion processing on the task instruction and the environment perception data, generating task semantic information associated with the task instruction, analyzing the task semantic information through an implicit planner, generating an implicit action mark, and based on the implicit action mark, generating an implicit action. And decomposing a to-be-executed task corresponding to the task instruction into a plurality of task sub-targets layer by layer, generating an action instruction sequence based on the task sub-targets, and executing a control action corresponding to the action instruction sequence. According to the method, based on the constraint of the implicit action mark and the action instruction sequence, the robot with the body can flexibly respond to environment parameter changes such as object position deviation or new task requirements in the task execution process, the coupling degree of the action instruction of the robot with the body and the predefined scene is reduced, and high stability of task execution is ensured.

The invention belongs to the technical field of robotics and computer vision, and relates to a multi-source information fusion outdoor inspection scene three-dimensional color geometric model reconstruction method, which comprises the following steps: (1) inertial measurement unit IMU internal reference calibration initialization, (2) laser radar and inertial measurement unit IMU combined external reference calibration, (3) state estimation, (4) using an i-Octree structure to store an updated map, and (5) performing multi-source information fusion. (5) carrying out loopback detection to solve the mileage drift problem; (6) carrying out data fusion of the laser radar and the color camera; and (7) carrying out 3D global positioning. The method has the advantages that the information of the laser radar and the information of the color camera are fused, the one-to-one mapping relation between each space point in the three-dimensional point cloud and a certain pixel point in the color image is established, visual information is more completely described, and the visual information is more accurately and accurately positioned by using a 3D global positioning method. And high-precision reconstruction of the three-dimensional color geometric model is realized.

The invention relates to the field of agricultural robots, and discloses an agricultural spraying robot system with visual navigation, and the system comprises a multi-mode sensing module, a dynamic environment modeling module, a time sequence prediction module, a multi-target path planning module, a spraying control module, and a hardware acceleration unit. The method comprises the steps that a dynamic semantic map is constructed through multi-modal perception, agricultural semantics and an attention mechanism are fused to predict an obstacle trajectory, a safe path is generated in combination with multi-objective optimization, the spraying amount is adjusted based on crop density, and real-time operation is ensured by means of hardware acceleration. According to the method, a dynamic semantic map is constructed through multi-modal perception, agricultural semantics is combined to enhance trajectory prediction, a safe path is generated by utilizing multi-objective optimization, the spraying amount is adaptively adjusted according to the crop density, and real-time control is realized by adopting heterogeneous hardware acceleration; the defects of a traditional agricultural robot in the aspects of navigation precision, obstacle avoidance safety, resource utilization and response speed are overcome, and the intelligent level of complex farmland operation is improved.

The present disclosure discloses a tennis ball-picking robot, including a chassis assembly, a ball-entering control element group, and a ball-collecting mechanism, the ball-entering control element group being disposed on a front end of the chassis assembly and the ball-collecting mechanism being disposed on a rear side of the chassis assembly. The tennis ball-picking robot incorporates multiple AI technologies such as computer vision, simultaneous localization and mapping, and robotics kinematics, enabling intelligent following, mapping and localization, and path planning. The robot can efficiently collect the tennis balls dropped on the court, and the players can also select a ball-picking area and a ball-picking speed pattern of the robot through an APP, improving the ball-picking efficiency, avoiding consuming the physical power of the players due to manual picking, improving the training effect by ultimately ensuring their focus during training, and reducing the training cost.

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.

In various examples, sets of correlated timestamps are sampled from clock sources. The sets of correlated timestamps are used to compute translation data, such as offsets and / or rates of change of the clock sources. The offsets and / or rates of change may be used to translate a timestamp to a reference time domain. The sampled clock sources may be frequency locked and the translation may be performed without using the rates of change. For example, a running average of the offsets may be used to perform the translation. The translated timestamps and corresponding sensor measurements may be provided to one or more applications for use in performing one or more operations for a machine, such as perception and / or control operations.

The invention discloses an external parameter calibration method and system for a mining laser radar and a camera, and belongs to the technical field of computer vision and robotics.The method comprises the steps that an original image and an original point cloud in a to-be-calibrated target scene are obtained; obtaining a preliminary alignment result according to the original point cloud data; optimizing the preliminary alignment result to obtain a laser radar fusion frame point cloud; performing panoramic segmentation on the original image, and constructing a 2D-3D effective target centroid corresponding relation; and based on a laser radar fusion frame point cloud and a 2D-3D effective target mass center corresponding relation, through panoramic segmentation, an effective target is extracted, an effective target panoramic information matching degree target function is constructed, the target function is optimized, and an external parameter optimal solution of the laser radar and a camera is obtained. According to the method, the automatic calibration precision of the external parameters of the mining laser radar and the camera can be effectively improved, and a research basis is provided for subsequent mining laser radar data and image fusion.

The invention discloses a snake-shaped robot control method based on reinforcement learning. The snake-shaped robot control method comprises the steps that a complex terrain simulation scene comprising walls, cubic obstacles and low channels is built; modeling is conducted on the structure, joints and kinetic parameters of the snake-shaped robot, and a snake-shaped robot simulation model is obtained; defining a state space and an action space of the snakelike robot, and designing a diversified reward function; constructing an action network and a value network by adopting a multi-layer perceptron (MLP) structure; and performing multi-round simulation training on the snakelike robot by applying a reinforcement learning algorithm, so that the robot learns a parameter set for making an optimization decision under different scenes, thereby realizing optimization control of the snakelike robot and reaching a target point. According to the method, visual information does not need to be depended on, efficient and stable motion control of the snake-shaped robot in a complex environment can be achieved through autonomous learning, and the crossing capacity of the snake-shaped robot in a low and narrow space is improved.

The invention relates to a multi-modal target identification and tracking prediction method based on a laser radar and a binocular camera, and belongs to the field of robot / unmanned system perception identification. Comprising the following steps: preprocessing a target two-dimensional image acquired by a binocular camera and target point cloud data acquired by a laser radar to obtain a preprocessed two-dimensional image and point cloud data; s2, performing two-dimensional target detection and tracking detection based on the preprocessed two-dimensional image to obtain a two-dimensional target detection and tracking result; s3, performing three-dimensional target detection based on the preprocessed point cloud data and the two-dimensional target detection and tracking result to obtain a three-dimensional detection result; and S4, based on the pixel coordinates in the two-dimensional target detection and tracking result and the pixel coordinates of the vertexes of the target point cloud boundary in the three-dimensional detection result, target fusion tracking prediction is carried out, and the target ID, the position, the category and the target point cloud boundary are obtained. And accurate identification and tracking of the target are realized.

The invention relates to the field of soft robots, in particular to a self-growing robot based on vacuum layer blocking and a variable stiffness method thereof, and the self-growing robot comprises a main body, a base, a blocking layer, an electromagnetic valve and an end cover; the end cover is fixed at the tip of the main body and consists of an outer end cover, an inner end cover and an electromagnet; the blocking layer is composed of skin flaps and a sealing bag, the skin flaps are formed by overlapping multiple layers of polyester films and packaged in the sealing bag, and the blocking layer is vacuumized to enable the layer interference skin flaps to be overlapped to increase the rigidity of the blocking layer; the base is divided into an inflation layer and an exhaust layer, the main body is stored in the inflation layer of the base after being turned inwards, the main body is fixedly connected to the base, and after the base is inflated, the tip end of the main body is driven by air pressure to be turned outwards for growth; furthermore, the blocking layer is fixed on the main body and is connected with the base air exhaust layer through an electromagnetic valve; the problem that the rigidity of a main body becomes poor after long-span extension of the self-growing robot can be solved.

The invention discloses a robot control method and system, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence technologies. The robot control method comprises the steps that a robot control instruction is received, and semantic features in the robot control instruction are extracted; acquiring a color image of an environment where the robot is located, performing image segmentation on the color image to obtain a mask corresponding to image content, and determining a semantic category of the mask; fusing the mask, the semantic category of the mask and the semantic feature to obtain a fused feature; inputting the fusion features and preset features into a multi-modal large model to obtain control information; and controlling the robot according to the control information so as to complete a task corresponding to the robot control instruction. The user intention can be accurately recognized, and the flexibility and accuracy of robot control are improved.

A method for performing one or more tasks on a surface via an unmanned aerial vehicle (UAV) or aerial robotics system (ARS) includes tethering a first UAV to a ceiling, a vertical wall, or a second UAV via a tethering cord with an extendible and retractable length, performing at least one task via the first UAV to an object during flight of the first UAV, sensing at least one parameter of the surface of the object via a task sensor, and resiliently adjusting an adjustable sensor arm of the first UAV to facilitate performance of the task(s). The adjustable sensor arm may support the task sensor and be resilient to impact forces caused by direct contact of the task sensor or the adjustable sensor arm with the surface. The tethering cord may be extended or retracted via at least one motor while the first UAV performs the task(s).

The invention relates to the technical field of robots, and discloses a waist joint driving structure of a robot, a waist driving structure of the robot, the robot and a humanoid robot. The waist joint driving structure mainly comprises a crank, a bearing, a supporting piece and a waist joint module, an output flange of the waist joint module is connected with the connecting end of the crank in an assembled mode, the supporting piece is connected to the output end of the waist joint module in an assembled mode, and the bearing with the supporting function is arranged between the supporting piece and the crank. The outer ring of the bearing abuts against the inner ring of the supporting piece, and the inner ring of the bearing abuts against the shaft sleeve on the connecting end. By means of the arrangement, the crank forms bilateral support through the bearing at the output end in the waist joint module and the bearing between the crank and the supporting piece, the problem of bias pressure can be avoided, and damage to the joint module is avoided.

The embodiment of the invention discloses a robot control method and device, electronic equipment and a storage medium, and relates to the technical field of robots and artificial intelligence, and the method comprises the steps: obtaining a target task of a robot and a corresponding performance index, state data, action data and a reward function are obtained according to the target task of the robot and the corresponding performance index, a virtual environment is constructed through a physical engine, the structure of the robot is designed according to the target task of the robot and the corresponding performance index, and state parameters of the robot are obtained; a control instruction is obtained through a Transform network, a reinforcement learning algorithm and a diagonal Gaussian strategy according to the state parameters of the robot and the environment state information of the virtual environment, and the robot is controlled to move through the control instruction. According to the method, efficient and accurate control over the robot is achieved, and the problems that in the prior art, efficiency is low, the intelligent level and universality are poor, and response is difficult to make in combination with structural information of the robot are solved.

Described herein are multi-well separation devices configured to allow a composition comprising a target agent to be separated into multiple wells, subdivided, recombined into a single well, and / or re-separated into the same or a different configuration of wells. Also described herein are reagent loading devices configured to simultaneously deliver one or more test agents to a plurality of volumes without having to individually deliver the test agents. Together, these devices allow high throughput parallel processes without repetitive pipetting or liquid handling robotics, though they may also be used separately. Also described herein are kits and systems for chemical or biological assays, as well as methods for using the multi-well separation devices and reagent loading devices described herein.

The invention relates to the field of robots, and discloses a robot ordering method and system, and the method comprises the steps: obtaining a depth map and a color map of a current scene; obtaining a detection frame of a target object, and cutting out three-dimensional point cloud data of the corresponding object from the depth map according to a detection result; reasoning by taking the three-dimensional point cloud data as input of a reasoning model, and predicting candidate grabbing poses; and according to the predicted candidate grabbing poses, trajectory planning and motion execution are carried out. Reasonable action information can be generated according to information provided by the visual language model in combination with specific task requirements, and therefore control over the mechanical arm is achieved.

The invention discloses a control method, system and equipment of a remote rehabilitation robot, a medium and a product, and belongs to the technical field of robots. The method comprises the steps of obtaining multi-modal data of a patient end, obtaining a time delay prediction value through prediction of a long short-term memory network model, dynamically adjusting admittance parameters of an admittance model according to the multi-modal data and the time delay prediction value, and finally controlling the remote rehabilitation robot according to the dynamically adjusted admittance parameters. According to the method, the admittance parameters of the admittance model are dynamically adjusted in real time based on the time delay predicted value predicted by the long-short-term memory network model and the electromyographic signals of the patient in the multi-modal number, dual adaptation of the network state and the physiological features of the patient is achieved, the admittance parameters are dynamically adjusted through the electromyographic signals, and the change requirement of the complete rehabilitation cycle of the patient is met; admittance parameters are dynamically adjusted through the time delay prediction value reflecting the network time delay state, and the time delay disturbance requirement in the remote communication environment is met.

Conventionally, the road was visually inspected from patrols etc., and it was determined whether to respond to road disasters in the event of a disaster or the like by observing the actual road conditions. However, there was a problem that it took time and effort to make a judgment only by visual patrol, and it was difficult to respond quickly. 【Solution means】 An information acquisition unit that acquires information related to disasters, an analysis unit that analyzes two or more pieces of information acquired by the information acquisition unit, and a determination unit that determines the necessity of road disaster response from part or all of the results analyzed by the analysis unit, A road opening unit that registers a pre-formulated road opening plan and formulates an implementation plan for road opening, and a robotics unit in which a disaster response robot performs a road opening operation using artificial intelligence analysis and robotics technology, A road disaster response support system, program, and road management method, characterized by comprising:

The invention provides a six-degree-of-freedom flexible adaptive integrated large-scale connector automatic docking method based on vision and force feedback. According to the method, a parallel structure is used as a design basis, and the difficulty of automatic docking is solved by combining an active compliance thought of taking a force sensor as feedback and a thought of taking a passive compliance docking mechanism as mechanism passive compliance docking. A set of active and passive compliant control integrated large-scale connector automatic docking system based on combination of vision and force feedback is designed. As a research focus in the robotics field, the active compliance control technology can be deployed in numerous accurate docking application scenes depending on force feedback, the active compliance docking strategy is formulated for the design difficulty of the active compliance docking control, and the strategy is simple, fast, low in cost and high in reliability. The anti-interference performance and the reliability of the control system are greatly improved, the system is suitable for automatic docking occasions in the industrial automation field in some complex environments, and contributions are made to further research and expansion of the technology. The high-precision flexible butt joint technology is applied more and more widely nowadays with the continuous development of science and technology, compared with a traditional butt joint device, the automatic butt joint device designed by the invention is higher in assembly efficiency and precision and higher in intelligent degree, damage to all paths of plug-in devices is smaller, meanwhile, economic losses can be further reduced, and the automatic butt joint device is suitable for popularization and application. And human resource cost is saved.

Disclosed is a robotic system and method designed for the verification and validation of smart connected Human-Machine Interface (HMI) devices. The robotic system integrates a multi-finger modular robotic module, a haptic module, a finger actuator, vision cameras, vision lights, a finger rotary module, a Cartesian robotic arm, an onboard user interface, and an L-shaped door. The method involves replicating human finger interactions, simulating touch sensations on a device under test (DUT), ensuring continuous and reliable operation, utilizing vision cameras for navigation and verification, optimizing vision with controlled lighting, positioning cameras strategically, executing various tasks with a robotic arm, and facilitating user interaction through an onboard interface.

Collision detection useful in motion planning for robotics advantageously represents planned motions of each of a plurality of robots as obstacles when performing motion planning for any given robot in the plurality of robots that operate in a shared workspace, including taking into account the planned motions during collision assessment. Edges of a motion planning graph are assigned cost values, based at least in part on the collision assessment. Obstacles may be pruned as corresponding motions are completed. Motion planning requests may be queued, and some robots skipped, for example in response to an error or blocked condition.

The invention discloses an inspection method of an inspection robot, a control system, a medium and a product, and relates to the field of robots, and the method comprises the steps: obtaining first inspection data in a cable pipe gallery after the inspection robot completes a first inspection route; determining a plurality of high-heat areas and temperature anomaly severity scores corresponding to the high-heat areas according to the temperature data; determining a plurality of high-humidity areas and humidity anomaly severity scores corresponding to the plurality of high-humidity areas according to the humidity data; determining a plurality of cable surface defect areas and image defect severity scores respectively corresponding to the plurality of cable surface defect areas according to the image data; based on the temperature anomaly severity score, the humidity anomaly severity score and the image defect severity score, determining the routing inspection priority of each area; and determining a second inspection route with the shortest distance according to the plurality of high-heat areas, the plurality of high-humidity areas and the plurality of cable surface defect areas. By implementing the method, the inspection efficiency and accuracy can be improved.

The present invention generally relates to the field of robotics, and, more particularly, to a method and system for task anticipation by integrating large language models and classical planning. Conventional methods for task anticipating use data-driven deep network architectures and Large Language Models (LLMs) for task estimation but they do so at the level of high-level tasks and require a large number of training examples. Thus, embodiments of present disclosure provide a method and system for task anticipation by integrating large language models and classical planning. The disclosed method and system leverages the generic knowledge of LLMs through a small number of prompts to perform high-level task anticipation, using the anticipated tasks as joint goals in a classical planning system to compute a sequence of finer granularity actions that jointly achieve these goals.

The invention discloses an automatic foreign matter selecting system based on cooperation of vision and a manipulator, and belongs to the technical field of automatic control. Pickled vegetable products are conveyed to the unfolding platform through the lifting buckets, and a plurality of non-overlapped pickled vegetable product areas are formed; preliminary image acquisition is carried out in the primary visual area, grid division is carried out, and foreign matter confidence is calculated; a foreign matter confidence coefficient threshold value is preset to recognize a risky foreign matter pickled vegetable product area grid, and the risky foreign matter pickled vegetable product area grid is transmitted to a primary removal area; after primary foreign matter removal is completed, the pickled vegetable products are turned over and conveyed to a secondary visual area, and the secondary visual area is used for secondarily judging whether foreign matter exists in the turned pickled vegetable products or not. An intelligent judgment mechanism of visual recognition and an accurate physical execution mechanism of the mechanical arm are fused, the stability and the automation degree of system operation are improved, and the quality safety guarantee level in the food processing process is remarkably improved.

The invention provides a self-adaptive multi-modal odometer dynamic fusion method which specifically comprises the following steps: extracting edge and plane features of a laser point cloud, and generating a laser pose factor by utilizing iterative motion compensation and residual optimization; multi-scale angular point features are extracted from an original image, and visual pose factors are generated in combination with optical flow tracking and local light beam adjustment. And a dynamic factor graph is constructed, laser and visual pose factors are used as constraints, an iSAM2 engine is used for optimization solution, and a global pose is obtained. Calculating the residual mean value and variance of Lagrange factors in real time, dynamically checking the state of the sensor based on chi-square test, triggering failure response during continuous overrun, gradually removing abnormal factors, keeping historical data, aligning timestamps through SE (3) manifold interpolation, and optimizing external parameter calibration. And after the sensor is recovered, factors are re-introduced through progressive weight fusion, and a sliding window mechanism is adopted to smooth the track. The method provides a robust and high-precision fusion algorithm for the field of robots and automatic driving.

The invention belongs to the field of robots, and provides a multi-scene obstacle crossing self-adaptive climbing inspection robot and a control method thereof in order to solve the problem that an existing inspection robot is not suitable for a high-rise steel structure. The multi-scene obstacle crossing self-adaptive climbing inspection robot comprises at least three sections of moving mechanisms, and any two sections of adjacent moving mechanisms are connected through a node connecting rod; a spherical hinge type multi-degree-of-freedom steering engine driving joint is arranged at the middle section part of the node connecting rod; each section of moving mechanism at least comprises a control part, a supporting part, a sensing part and a climbing part; the climbing part at least comprises a pair of side arm connecting rods which are symmetrically arranged on the two sides of the supporting part. The tail end of each side arm connecting rod is connected with a dynamic magnetic attraction wheel through a wrist-imitating joint; a self-adaptive driven wheel is further arranged at the bottom of the supporting part, and the self-adaptive driven wheel and the dynamic magnetic attraction wheels on the two sides of the self-adaptive driven wheel form a three-point clamping structure. The method can meet the requirements of visual full-coverage path planning and space precise positioning of the high-rise steel structure.