618 results about "Collaborative robotics" patented technology

The invention relates to the technical field of industrial Internet of Things, in particular to an industrial Internet of Things system for collaborative robot monitoring, which comprises the following steps: a sensor calibration module, a running state modeling module, a working performance analysis module, an input parameter analysis module, a movement track detection module and a fault type analysis module. According to the method, the accuracy and reliability of collected data are ensured by calibrating the reading of the sensor, the accuracy of robot state evaluation is optimized, the ideal motion trail of the robot is predicted in real time, the actual position is monitored, the position deviation and the abnormal trail are recognized, and the accuracy of robot state evaluation is improved by combining the calibration of abnormal points and the matching of fault modes. Fault types are quickly identified, fault positions are positioned, fault detection efficiency and accuracy are improved, adaptability of the robot in a complex production environment is improved, operation efficiency and maintenance precision of the robot are enhanced, downtime is shortened, maintenance cost is reduced, and accurate production monitoring and decision support are provided for enterprises.

The invention discloses a flexible composite robot system for laboratory automation. The flexible composite robot system comprises a flexible composite robot and a central scheduling and control system, the flexible composite robot comprises an autonomous mobile platform which carries a multi-mode navigation sensor and realizes path planning and obstacle avoidance in a laboratory environment by adopting a synchronous positioning and mapping algorithm; the high-precision cooperative mechanical arm is mounted on the autonomous mobile platform and is integrated with a joint torque sensor; the end effector quick replacement system provides mechanical, electrical and pneumatic standardized interfaces; the central scheduling and control system comprises an intelligent man-machine interaction system for realizing man-machine interaction; the experiment process analyzer is used for decomposing the digital experiment scheme into an executable task sequence; the multi-agent task planning and scheduling engine is used for distributing tasks and planning a moving path; and the anomaly detection and autonomous recovery module is used for detecting an abnormal condition based on machine vision and force sensing data and triggering a recovery strategy.

The invention relates to the field of robot intelligent control and man-machine collaboration, in particular to a multi-sensing collaborative robot control method, which comprises the following steps: synchronously filtering mechanical, tactile, visual and physiological signals to generate a unified observation frame, and calculating power flow and energy in combination with a joint state to form an execution vector; extracting a topological descriptor by using persistent coherence, and outputting an interaction potential field and a human body intention by using the topological descriptor as a conditional driving diffusion model; then, power density is constructed based on the potential field gradient and intention, joint torque and impedance are generated through entropy regular optimal transmission and Jacobian and microoperator kernel mapping, and an energy Lagrange multiplier is updated in real time; and if the consistency error continuously exceeds a threshold value, estimating a Koopman lifting model on line by using a causal weight, completing model prediction control under the energy, conformal security and topology consistency constraint, outputting an update instruction and performing closed-loop feedback. The method improves the cooperation precision and safety, reduces the energy consumption, and is suitable for a high-dynamic man-machine cooperation scene.

The invention relates to the technical field of two-arm robot pipeline butt joint cooperative motion planning and control in a complex environment, in particular to a two-arm cooperative robot pipeline butt joint method based on relative Jacobi. According to the method, a double-arm kinematics equation relative to a Jacobian matrix is constructed, constraint conditions are constructed in combination with pipeline butt joint requirements, pose errors and integral information are introduced to design an anti-noise motion planning scheme, an end effector obstacle avoidance strategy is designed, the anti-noise scheme is converted into a quadratic optimization problem, and the quadratic optimization problem is solved through a numerical algorithm. According to the method, the problems that an existing scheme is insufficient in precision, inaccurate in constraint condition and poor in anti-noise and obstacle avoidance capacity are solved, the uniformity and accuracy of butt joint of the two-arm pipelines are improved, the influence of environmental noise is effectively restrained, butt joint safety is guaranteed, butt joint efficiency and reliability are improved, and the high-precision pipeline butt joint requirement under the complex environment is met.

The present invention relates to a collaborative robot system used in an industrial environment where workers collaborate. The collaborative robot system comprises: a collaborative robot body having a multi-joint structure; a control unit for controlling each joint or auxiliary shaft; one or more sensors for detecting the operational state of the robot in real time; and a status diagnosis and response module for diagnosing the state of the robot on the basis of detected data and controlling the operation of the robot accordingly. Specifically, the system is configured to enable the robot to autonomously perform actions such as deceleration, stopping, and recovery in response to various state changes occurring during work, and to perform self-learning and trajectory optimization on the basis of data accumulated through repetitive tasks. Furthermore, the system includes a graphical user interface (GUI) for intuitive user interaction, which is linked to a digital twin-based virtual simulation environment, enabling presetting and modification of work paths. When multiple collaborative robots are operated together, the system enables task synchronization, path collision avoidance, and sharing of status information among the robots, and may be connected to an external control server or a cloud-based control system to allow integrated management of the entire workflow.

The invention discloses a constraint following control method of a two-arm collaborative robot based on differential homeomorphic mapping, and relates to the field of robot control. According to the method, a nominal dynamical model of a robot is established, and a geometric constraint equation is defined according to a cooperative task; establishing global differential homeomorphic mapping by constructing constraint tracking variables and complementary coordinates, and decoupling a system dynamics model into a constraint tracking subsystem and a complementary subsystem; designing a robust adaptive controller fusing sliding mode control and an adaptive law, and estimating and compensating the uncertainty of the system on line; according to the method, the constraint processing complexity is effectively simplified through differential homeomorphic mapping, the constraint tracking precision and robustness of the system under model uncertainty and external interference are remarkably improved, and high-precision and high-stability cooperative task execution of the two-arm robot in the dynamic environment is achieved.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention belongs to the technical field of intelligent and robot operation, and discloses a collaborative robot closed-loop operation method based on scene perception and thinking chain reasoning, which comprises the following steps of: firstly, generating structured semantic representation containing confidence by utilizing a visual language model of LoRA efficient fine tuning and fusing multi-view data, and providing semantic understanding for a robot; secondly, utilizing a three-dimensional diffusion model to generate explicit geometric priori under the condition of no real CAD, and correspondingly completing PnP initial pose estimation in combination with 2D-3D; then, carrying out pose optimization by adopting a tracking-refining strategy, and triggering reinitialization based on geometric prior when a residual error exceeds a threshold value; and finally, decomposing a global task by utilizing a thinking chain reasoning module, and realizing'perception-reasoning-execution 'closed-loop control based on visual servo in cooperation with an execution module. The method realizes effective combination of large model reasoning and physical execution, has strong robustness under dynamic disturbance, and is suitable for intelligent manufacturing and man-machine cooperation scenes.

The invention discloses a collaborative robot joint external force estimation method, and relates to the technical field of collaborative robot joint external force estimation, and the method comprises the steps: building a joint kinetic model, and obtaining the basic kinetic parameters of a collaborative robot joint; the angular displacement deviation between the input shaft and the output shaft of the speed reducer is obtained, and the compensated torsional angle difference is calculated according to the angular displacement deviation; a joint variable stiffness model is established, multiple sets of calibration data are obtained through experiments, and parameters in the joint variable stiffness model are identified according to the multiple sets of calibration data; based on the basic dynamic model and the joint variable stiffness model after parameter identification, a nonlinear disturbance observer is constructed in combination with introduced auxiliary variables; and system disturbance is observed and compensated through a nonlinear disturbance observer, so that an external torque estimated value acting on the tail end of the joint is calculated and output in real time. According to the method, high-precision and real-time estimation of the external torque can be effectively considered, and meanwhile, the system hardware cost and the structure complexity are greatly reduced.

The invention belongs to the technical field of robots, and discloses a collaborative robot intelligent feeding and discharging system, which comprises a station structure sensing module for collecting station structure information of a station where a collaborative robot is located when the collaborative robot executes feeding and discharging tasks, and performing structured coding on the station structure information to generate a station structure constraint matrix; the part task decomposition module is used for constructing a loading and unloading task queue of parts on the basis of the station structure constraint matrix, and converting the loading and unloading task queue into an action instruction set which can be executed by the collaborative robot in combination with a preset station rhythm requirement; the path deduction avoidance module projects the action instruction set to a joint space of the collaborative robot, and deduces and generates a candidate path set based on a station constraint path mapping mechanism; performing optimization selection on the candidate paths by adopting dual-target scheduling of energy consumption and process rhythm, and selecting the candidate path with the optimal performance as an execution path of the robot; and the safety of feeding and discharging operation of the collaborative robot is improved.

The invention discloses a cooperative robot joint adaptive control system and method based on multi-sensor fusion, and relates to the technical field of robot joint control. According to the invention, through an improved extended Kalman filtering and deep neural network compensation mechanism integrated by the multi-source data fusion unit, sensor noise is effectively suppressed, and multi-source data conflicts are solved; the sensor reliability evaluation unit is based on a dynamic weight distribution mechanism of Bayesian reasoning, and can reduce the fusion weight of the sensor in real time when the sensor is detected to be abnormal, and improve the contribution degree of reliable sensor data. The adaptive control unit is combined with the self-learning optimization unit to form a closed-loop optimization architecture, so that the performance limitation caused by fixed traditional PID parameters is thoroughly solved; through adaptive weighted fusion of multi-source sensor data, the joint state estimation precision is improved; and a deep neural network residual error compensation mechanism is utilized to reduce a torque control steady-state error.

Robot calibration is crucial in multi-robot cooperative systems where the inaccuracy of robots can add up and cause large errors in the final trajectory of handled parts or process tools. In this work, a two-step calibration approach is proposed based on artificial neural networks (ANNs) and definition of compensated pose for a master-slave cooperative robot system. Measuring the pose of master and slave robots at different locations in their shared workspace is required to create pairs of joint angles and output pose errors as training data. The generated data is used to train two ANN models for compensating the master-slave relative error and the master robot errors. The master-slave relative error is corrected by introducing a compensated pose for the slave robot with respect to the master robot. A neural network is then trained to predict the error parameters of the compensated pose for the joint angles of both robots as the input. The master robot is then corrected individually using another ANN model to address the absolute accuracy of the cooperative system.Measurements and simulations have been performed on a dual-robot cooperative system before and after geometric calibration. The process of cross validation is carried out to find the best network architecture for the optimal performance in correcting the robots'errors. It has been shown that even after pre-existing model-based calibration of each robot, both the absolute accuracy of the master robot and the relative tracking accuracy can be further improved by the proposed implementation of ANN calibration.

The invention relates to a cooperative robot control method based on a self-adaptive observer and a neural network. The cooperative robot control method comprises the steps of system modeling and signal collection, operator identity recognition and feature extraction, super-spiral disturbance observer, frequency domain self-adaptive admittance filter and composite control law synthesis. A kinetic model is constructed and disturbance terms are defined by collecting man-machine interaction torque and joint states in real time; extracting the characteristics of an operator, carrying out identity recognition by adopting an incremental DBSCAN algorithm, and associating personalized admittance parameters; designing a super-spiral disturbance observer to dynamically estimate disturbance; adaptively adjusting admittance parameters according to the identity of the operator and the frequency band characteristics to generate an expected trajectory; and the sliding mode control and the double-loop neural network compensation item are combined to synthesize the control torque, so that high-precision and smooth man-machine interaction control is realized. The method has the advantages of being high in anti-interference performance, good in personalized adaptability, high in tracking precision and the like.

The invention discloses a rescue boat device and method based on multi-unmanned aerial vehicle assisted search, and belongs to the technical field of water rescue. The device comprises an unmanned aerial vehicle subsystem, a rescue boat main body and a cooperative communication module, the unmanned aerial vehicle subsystem comprises a plurality of rescue unmanned aerial vehicles which are provided with 4K optical zoom cameras, dual-frequency GPS / Beidou positioning modules and edge computing units (NVIDIA Jetson Xavier NX), and an intelligent parking cabin with a wireless fast charging function; the rescue unmanned aerial vehicles are provided with the 4K optical zoom cameras, the dual-frequency GPS / Beidou positioning modules and the edge computing units (NVIDIA Jetson Xavier NX); the rescue boat main body is integrated with a net-shaped lifting platform, a six-degree-of-freedom cooperative mechanical arm and a central control system; and the cooperative communication module adopts a 5G / LoRa hybrid network. The method comprises the steps that multiple unmanned aerial vehicles cooperatively search a target through an improved spiral expansion algorithm, and centimeter-level positioning is realized by adopting a multi-source fusion positioning technology; the rescue boat autonomously navigates to a target position according to the positioning information, and salvage and first-aid treatment of drowning persons are completed through the lifting platform and the mechanical arm which are intelligently controlled. The method has the technical effects that the search efficiency is improved by more than 300%, the positioning accuracy reaches + / -0.5 m, the rescue response time is shortened to be within 5 minutes, the system can stably work under the 4-level sea condition, and an efficient, accurate and full-automatic solution is provided for water rescue.

The invention discloses a full-automatic verification system of a temperature sensor, and relates to the technical field of temperature sensor verification, in the system, a main control computer is used for reading a verification scheme, controlling a collaborative robot to install a detected temperature sensor to a tool, and controlling the collaborative robot to transfer the tool provided with the detected temperature sensor into a thermostatic bath; the thermostatic bath provides a thermostatic environment through bath liquid, and the temperature of the bath liquid is monitored in real time through a temperature standard device; the acquisition system is used for acquiring an output signal of the detected temperature sensor transferred into the thermostatic bath and converting the output signal into a temperature value under the control of the main control computer; the main control computer is further used for comparing the temperature value measured by the detected temperature sensor with the standard temperature value measured by the temperature standard device and determining whether the detected temperature sensor is qualified or not according to the comparison result. According to the invention, the verification efficiency can be effectively improved, and the high-efficiency verification requirement of a large batch of temperature sensors can be met.

The invention relates to the technical field of CTP-S battery module assembly, in particular to an automatic module boxing system and method for a CTP-S battery pack. Comprising a line body integration unit, a code scanning mechanism, a material taking mechanism, a carrying truss, a PACK jacking positioning unit, a collaborative robot unit and a control system. The control system is in communication connection with the carrying truss, the material taking mechanism, the collaborative robot unit, the line body integration unit, the code scanning mechanism and the PACK jacking positioning unit. The control system obtains position compensation data generated after a plurality of preset positions in the battery pack box body are subjected to visual positioning by a visual camera of the collaborative robot unit; the control system controls the carrying truss and the material taking mechanism to act cooperatively according to the position compensation data, grabbing and placing operation is executed through the material taking mechanism, and grabbing of the module from the line body integration unit and box entering of the battery pack box on the carrying trolley are achieved. And the effects of high precision, no damage, high efficiency, wide compatibility and full-automatic assembly of the heavy CTP-S battery module are achieved.

A system for human-robot collaboration is provided herein. The system includes a robotic device having at least one mechanism for moving at least a part of the robot, one or more sensors configured to capture environmental data, a processor in communication with the robotic device and the one or more sensors, and a memory in communication with the processor. The memory has instructions that, when executed, cause the processor to capture an image of a scene, detect objects, generate manipulation options, receive a prompt indicating an overall objective, generate a list of task steps, generate a task graph, determine a probability for each task step, determine a predicted next intention step, and execute a manipulation option corresponding to the next intention step via the robotic device.

The invention provides a fault detection method and system for a mechanical arm of a coffee beverage robot, and relates to the technical field of fault detection of mechanical arms. And the single-mechanical-arm detection parameters and the mechanical-arm cooperative detection parameters are randomly called from the detection rule base to carry out single-mechanical-arm special detection and three-mechanical-arm cooperative detection, track deviation analysis is carried out on single-mechanical-arm track characteristics and cooperative-mechanical-arm tracks obtained through detection according to reference mechanical-arm track characteristics, and a mechanical-arm fault alarm is output. The technical problems that in the prior art, mechanical arm fault detection of a coffee beverage robot is carried out based on a fixed period, fault detection is separated from a coffee making process, and fault missing detection and false alarm are likely to happen are solved. The technical effects that the false alarm and missing alarm phenomena of mechanical arm faults are reduced, the maintenance efficiency of the mechanical arm of the coffee beverage robot is optimized, and the high precision and stability of the automatic coffee making process are guaranteed are achieved.

The invention discloses a cooperative control method and system for a man-machine cooperative assembly robot based on digital twinning. According to the method, firstly, a digital twinborn simulation environment is constructed in a Unity engine, and mapping of a physical cooperation assembly scene and a virtual scene is achieved; personnel actions and assembled workpiece states are collected in real time through a multi-modal sensing technology, and virtual-real mapping is introduced to realize intention recognition. And then based on a reinforcement learning algorithm, performing path planning on the collaborative robot in a digital twin environment to generate an optimal collaborative path sequence. And finally, mapping the planned path to a real robot control instruction through coordinate transformation and an inverse kinematics algorithm, and dynamically optimizing a motion track by adopting an online self-adaptive strategy. Through the combination of digital twinning and reinforcement learning, the intelligent and flexible scheduling capability of a man-machine cooperation assembly system is improved, the rapid adaptability and assembly precision of the system to unstructured changes are enhanced, and the problems that task programming is tedious and the adaptability is poor in a traditional method are solved.

The invention relates to the technical field of robot collision test, and provides a human body model for collaborative robot interaction collision test and a control method thereof, and the human body model comprises a head module which comprises a head supporting part and a head model covering the outer layer of the head supporting part, and a three-dimensional force sensor is fixed on the side surface of the head module; the neck module comprises a neck skeleton rotationally connected with the head supporting part, and a six-dimensional force sensor is arranged at the joint; the trunk module comprises a trunk skeleton hinged to the neck skeleton and a thoracic cavity model covering the outer layer of the trunk skeleton, a plurality of three-dimensional force sensors are arranged on the front chest part of the trunk skeleton, and a three-dimensional force sensor and a flexible sensor are arranged on the back part; the big arm skeleton is connected with the trunk skeleton and is provided with a three-dimensional force sensor; the pose adjusting module comprises a horizontal rotating structure and a linear module, and simulates human body steering and gait movement so as to drive the head module, the neck module, the trunk module and the upper limb module to move and collide with the collaborative robot. Omnidirectional capture and analysis of the space collision force are realized.

According to the man-machine cooperation mechanical arm control method based on the multi-mode large model, assembly scene visual information, text instruction information and prompt information are combined and input into the multi-mode large model for understanding and task decomposition, and operation instructions with different function check bits and mechanical arm control codes are generated. A pixel area and coordinates of a target object are obtained by combining retrieval of a multi-modal large model or a part knowledge base with a DINO-X model and are converted into pose coordinates of a mechanical arm according to a mapping relation between a coordinate system of an end effector of the mechanical arm and a coordinate system of a visual system, and a mechanical arm control instruction is generated. The mechanical arm is controlled to move by combining operation instructions with different functional check bits and mechanical arm control codes, so that the problems of poor generalization and low precision caused by the illusion problem of a multi-mode large model are solved while the mechanical arm control instructions are accurately and efficiently generated by combining the multi-mode large model with voice instructions and visual information reasoning decisions.

The invention discloses a quick-change heterogeneous end effector integrated on a collaborative robot and a control method of the quick-change heterogeneous end effector, and relates to the technical field of rehabilitation physiotherapy. By adopting the combination of the six-dimensional force sensor and the stereoscopic vision depth camera, rapid switching of multiple physiotherapy modes is realized, and the accuracy and diversity of the physiotherapy process are improved. The probe design refers to finger pressing, palm pressing, hand pinching and other actions of professional physiotherapy doctors, contact force stability is ensured, the accuracy and comfort of the physiotherapy process are ensured, meanwhile, real-time monitoring is conducted, and physiotherapy parameters are automatically adjusted according to the physical state and requirements of a patient. And by adopting the six-dimensional force sensor and the calibration zero setting technology, the accuracy of force feedback and the signal stability are remarkably improved, and the physical therapy effect is prevented from being influenced by external interference. And rapid replacement and efficient operation of the probe are achieved, the multifunctionality of the collaborative robot is improved, and the service life of the collaborative robot is prolonged. The physical therapy effect is guaranteed, meanwhile, the working intensity of physical therapy doctors is effectively relieved, and wide application prospects and clinical value are achieved.

The invention relates to the technical field of collaborative robot welding, in particular to a collaborative robot welding system suitable for ship narrow cabin moving operation, which comprises a ship bottom plate, the ship bottom plate comprises an outer bottom plate, an inner bottom plate, a rib plate and a girder, the outer bottom plate, the inner bottom plate, the rib plate and the girder form a cabin, and a plurality of pipelines are arranged at the top of the cabin. A movable U-shaped magnetic attraction rail is arranged at the center of the top of the ship bottom plate, a six-degree-of-freedom small collaborative robot is arranged at the top of the movable U-shaped magnetic attraction rail, and an auxiliary rail is arranged between every two adjacent cabins. The robot system is compact in structure, the technical problems that an existing welding robot system is inconvenient to move, poor in obstacle avoidance capacity, insufficient in welding accessibility, low in path planning efficiency and the like in narrow cabin environments such as ship cubicles are solved, welding blind areas are effectively reduced, efficient and stable movement and welding in the dense obstacle environment are achieved, and the welding efficiency is improved. And the working efficiency and the quality consistency are obviously improved.

The invention relates to a mobile robot laser inner hole cladding machine, and relates to the technical field of laser cladding. The mobile robot laser inner hole cladding machine comprises a laser device, a powder feeding mechanism and a laser cladding head, the powder feeding mechanism is used for continuously conveying metal powder to the laser cladding head, the mobile robot laser inner hole cladding machine further comprises a control machine base and a moving base, the laser device and the powder feeding mechanism are both installed on the control machine base, a collaborative robot is further arranged on the moving base, and the collaborative robot is connected with the laser cladding head. The laser cladding head is installed on the collaborative robot so that the collaborative robot can drive the laser cladding head to move, and the control machine base and the movable base are both used for moving on the ground. The device has the effect of conveniently repairing the internal parts of the large machine.

The invention provides a man-machine cooperation assembly track generation method, device and equipment based on a digital human, and relates to the technical field of man-machine cooperation, and the method comprises the steps: constructing a digital human model, a cooperation robot model and an assembly environment based on musculoskeletal on a target simulation platform, defining the attributes of each model, and forming a man-machine cooperation assembly simulation platform; taking the man-machine cooperation assembly simulation platform as an interaction environment, training a digital human control strategy network based on a deep reinforcement learning algorithm, and generating a digital human assembly track in an assembly process; introducing the trained digital human control strategy network into a man-machine collaborative assembly simulation platform, taking a dynamic assembly environment as a training environment of a collaborative robot, training the obstacle avoidance capability of the collaborative robot to avoid dynamic actions of a digital human through a deep reinforcement learning algorithm, generating a robot obstacle avoidance strategy network, and performing robot obstacle avoidance simulation on the robot. And outputting a robot assembly track based on the robot obstacle avoidance strategy network. By means of the method, man-machine safety cooperation assembly can be achieved.

The invention relates to the technical field of robot control, in particular to a control method and system for a mechanical arm of a collaborative robot. The method comprises the following steps: collecting multi-modal observation data such as vision, force and moment, touch, joint state and the like, and outputting an operation intention, a task stage and an uncertainty index after time sequence alignment and feature fusion; calculating security constraints according to the information, constructing a feasible region, adaptively updating impedance parameters, generating a control instruction, and issuing and executing the control instruction after constraint projection; meanwhile, an online self-calibration mechanism triggered by a residual error is introduced, and parameters such as sensing zero offset, friction and TCP are updated. According to the method, the flexibility, the operation precision and the safety of man-machine cooperation can be considered, the risks of impact, collision and clamping stagnation are reduced, and the long-term operation stability of the mechanical arm is improved.

The invention discloses a kinetic parameter identification method, device and equipment of a collaborative robot considering uncertain factors, and relates to the technical field of robots, the method comprises the following steps: constructing an improved kinetic friction model considering uncertain factors and multi-factor coupling, and obtaining friction torques borne by joints according to the model; constructing a kinetic equation; solving the kinetic equation to obtain a recursive expression; the collaborative robot is driven according to the excitation track, an output parameter matrix is obtained, the joint angular velocity of the collaborative robot is calculated according to the output parameter matrix and the recursion expression, and high-order terms of the joint angular velocity are calculated step by step; when the error of the expansion values of two adjacent orders is smaller than an iteration convergence coefficient, outputting the angular velocity; and judging a motion stage to which the angular velocity belongs according to the velocity judgment coefficient, and dividing a parameter identification stage according to a judgment result to realize segmented identification of the kinetic parameters of the collaborative robot. According to the method, the identification precision and calculation efficiency of the kinetic model are improved.

This invention discloses an airport FOD (Foreign Object Debris) detection and removal system and method based on multi-source sensing fusion. The system includes a mobile transport chassis, a multi-source heterogeneous sensing unit, a six-degree-of-freedom collaborative robotic arm, a composite cleaning end effector, and an edge computing platform. During operation, environmental data is first collected using LiDAR, millimeter-wave radar, and a binocular camera. Spatiotemporal fusion and state estimation algorithms are used to achieve all-weather, high-precision positioning and attribute calculation of FOD. The composite cleaning end effector integrates a high-negative-pressure suction port, a ring-shaped electromagnetic adsorption array, and a flexible mechanical claw. Subsequently, the system adaptively switches between magnetic suction, suction pickup, or gripping modes based on the material, volume, and shape characteristics of the FOD, and combines a visual closed-loop feedback mechanism to automatically upgrade and verify the cleaning strategy. This invention effectively solves the problems of poor perception robustness under complex weather conditions and insufficient adaptability of single cleaning methods, significantly improving the intelligence level and operational efficiency of airport pavement foreign object removal.

To provide a control device, a bending processing system, a robot control method, and a robot control program that enable use of a cooperative robot in a reduced space while securing user safety.SOLUTION: A control device includes a control part capable of controlling a multi-joint cooperative robot that conveys a workpiece with respect to a carrying target. The control part is configured to cause the multi-joint cooperative robot to operate in prior to the workpiece such that at least a part of the multi-joint cooperative robot is located in front of the workpiece in a travel direction, in at least one of carrying-out operation of carrying out the workpiece from the carrying target and carrying-in operation of carrying in the workpiece toward the carrying target.SELECTED DRAWING: Figure 7

The present invention provides a collaborative robot that allows for easy attachment and detachment of the fifth-axis motor and torque converter from the upper arm. [Solution] The output shaft 512 of the fifth-axis motor 51, which rotates the support arm 16 of the collaborative robot 1 around the fifth axis A5, is positioned along the fourth axis A4. Inside the upper arm 15 of the collaborative robot 1, the fifth-axis motor 51 and a torque converter 53 that converts the torque of the output shaft 512 into torque around the fifth axis A5 are housed. The upper arm 15 is a cylindrical housing and has an arm body 151 with an opening 2 for mounting the fifth-axis motor 51 and the torque converter 53, and a cover 152 that covers the opening 2 and is attached to the arm body 151. When viewed from a direction along the fifth axis A5, the fifth-axis motor 51 and the torque converter 53 are housed in the internal region within the opening edge 20 of the opening 2.