5896results about "Arms" patented technology

The invention discloses a double-mechanical-arm adaptive motor neural network optimization system, and relates to the technical field of mechanical arm motion control, and the system is characterized in that a feature fusion sensing module synchronizes multi-sensor data through an FPGA, combines technologies such as YOLOv5s, Poi ntNet + + and Doppler frequency shift, achieves precise extraction and fusion of obstacle 2D / 3D features, and generates a real-time environment state matrix; the kinematic chain prediction module quantifies the obstacle motion probability based on historical trajectory clustering and Bayesian prediction, constructs a collision risk field, and dynamically evaluates the collision risk in the future 2 seconds; the planning module searches a low-risk path by adopting an improved A algorithm, optimizes a joint movement track in combination with quadratic programming, embeds kinematics and dynamics constraints, and ensures the feasibility and safety of the path; in addition, a correction mechanism and an energy consumption optimization mechanism are introduced, motion parameters are dynamically adjusted according to the real-time state of the mechanical arm and environment interference, and the obstacle avoidance performance and the operation efficiency of the mechanical arm are further improved.

A humanoid robot includes a base, a robotic torso coupled to the base, at least one robotic arm, at least one robotic shoulder coupling the at least one robotic arm to the robotic torso, a robotic neck coupled to the robotic torso, and a plurality of actuators configured to move at least a portion of at least one of the robotic torso, the at least one robotic arm, the at least one robotic shoulder, and the robotic neck. Each of the robotic torso, the at least one robotic arm, the at least one robotic shoulder, and the robotic neck is defined by one or more proportions that deviates less than 25% from respective proportions of a human envelope.

The embodiment of the invention provides an unhooking robot control method based on multi-source visual fusion, which is applied to the technical field of robot control and comprises the following steps: acquiring an RGB image, a depth image, an infrared image and IMU data through a multi-source sensing system mounted at the tail end of a robot; carrying out feature fusion identification by adopting a double-branch neural network, and outputting the boundary contour of the lifting hook and the three-dimensional coordinates of the optimal grabbing point; the visual coordinates are unified to a robot base coordinate system through a registration correction mechanism; a Transform prediction model is constructed based on the visual and inertial signals, and future pose changes of the lifting hook are estimated; a feedforward control track is generated to counteract swing of the lifting hook, and track correction is carried out in combination with visual servo feedback; and a joint instruction is generated through path planning and inverse kinematics solution, and the mechanical arm is driven to complete precise unhooking operation. According to the method, the recognition precision, the anti-interference capability and the operation success rate of unhooking operation in complex illumination and dynamic environments are effectively improved.

The disclosure presents a humanoid robot with an upper region (head, torso, arms with forearm assemblies, end effectors), lower region (legs), and connecting central region. Each end effector features index, middle, ring, little finger, and thumb assemblies attached to a housing of said end effector. The housing includes interior wall extents creating spaces for tendon routing, where the first distance between first and second wall extents is less than 45% of the second distance between third and fourth wall extents. Tendons controlling finger movements pass between these wall extents. The robot incorporates a wrist assembly connecting the housing of the end effector to the forearm, actuators housed in the forearm that control the tendons, and a carpal tunnel-like structure that guides tendons from forearm to base of the housing, enabling precise hand movements without requiring actuators in the hand itself.

The invention relates to the technical field of intelligent agricultural equipment, and discloses an automatic agaricus bisporus picking method, device and equipment based on artificial intelligence and a medium. The method comprises the steps that RGB images and three-dimensional point cloud data of the agaricus bisporus growth environment are collected in real time through a binocular vision camera and a depth sensor which are installed at the tail end of a picking robot, and environment illumination parameters and cultivation bed coordinate information are obtained; inputting the RGB image into a pre-trained lightweight convolutional neural network, identifying a pileus contour, a stipe position and a maturity level of the agaricus bisporus, calculating a space coordinate, a height and a growth inclination angle of the agaricus bisporus based on the three-dimensional point cloud data, and constructing an agaricus bisporus target database; a mechanical arm picking path is generated based on the agaricus bisporus target database, and path weight parameters are optimized online through a preset reinforcement learning model; according to the maturity grade and morphological characteristics of the agaricus bisporus, the clamping force and the rotating angle of the end effector are dynamically adjusted, and automatic picking of the agaricus bisporus is completed.

The invention discloses a variable-stiffness continuous robot based on force locking. The variable-stiffness continuous robot comprises a flexible arm section, a variable-stiffness arm section, a rope driving mechanism and a wire driving mechanism, the flexible arm section and the variable-stiffness arm section are driven by winding a rope through a rope winding disc, so that the continuous robot generates six bending degrees of freedom. By changing the axial movement amount of the movable taper table, the lateral pressure of the rotating joint can be increased or reduced, the friction force of the joint is controlled, and therefore the rigidity of the continuous robot can be adjusted. The rigidity change of the variable-rigidity arm section can be realized, and the variable-rigidity arm section and the flexible arm section are connected in series, so that the tail end of the mechanical arm has enough flexibility while the joint rigidity is ensured, and the mechanical arm can adapt to a machining operation task in a complex and narrow space.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm and a control method thereof. The method comprises the following steps that real-time load information of all joints of the mechanical arm in the operation process is obtained, the acceleration and rotational inertia of the mechanical arm are monitored in real time, and real-time inertia force data are generated; the torque compensation amount needed by the mechanical arm is calculated according to the real-time inertia force data, and torque compensation control data are generated; performing dynamic analysis on the torque compensation control data, and performing real-time adjustment on the output torque of the servo motor based on the current load and inertia change condition to generate inertia compensation data; and the motion path and the motion speed of the mechanical arm are dynamically optimized according to the inertia compensation data, and path optimization data are obtained. Through real-time monitoring, dynamic adjustment and intelligent optimization, the operation precision, the movement efficiency, the stability and the self-adaptive capacity of the mechanical arm in the complex working environment are remarkably improved.

A selectively flexible extension tool includes a line assembly; and a plurality of sequentially arranged links, the line assembly operable with the plurality of sequentially arranged links to move the plurality of sequentially arranged links between a slacked position and a tensioned position, the plurality of sequentially arranged links spaced from one another when in the slacked position to allow the plurality of sequentially arranged links to pivotably move relative to one another, the plurality of sequentially arranged links pressed against one another when in the tensioned position to rigidly fix the plurality of sequentially arranged links to one another.

The invention discloses a terahertz-based curved surface thermal barrier coating nondestructive thickness measurement system and method, and the method comprises the steps: firstly carrying out the coordinate calibration and reverse reconstruction of a sample piece and a mechanical arm through an optical scanning and trinocular vision measurement system, and building a virtual space; then matching the virtual space with the actual space, extracting a normal vector of the curved surface of the sample piece, performing path planning on the mechanical arms to generate a control script, and controlling the double mechanical arms to carry a terahertz measuring head to scan the sample piece in a manner of being vertical to the surface of the sample piece to obtain a terahertz signal; and finally, constructing a terahertz signal analytical model, optimizing terahertz simulation signal parameters through a self-adaptive teaching and learning optimization algorithm, and fitting a detection signal until an optimal solution is obtained, namely the thickness of the thermal barrier coating. According to the invention, through cooperation of the two mechanical arms and path planning, rapid detection of a complex curved surface structure is adapted; by establishing an analytical model and combining with an optimization algorithm, the detection precision is improved, and high-precision nondestructive detection suitable for a complex curved surface structure is realized.

The invention discloses a robot visual identification, positioning and grabbing method based on RGBD point cloud, and the method comprises the following steps: 1, carrying out the camera point cloud data processing, synthesizing an RGB image and a depth image into a point cloud image, and carrying out the filtering and denoising processing of the point cloud data; step 2, target detection and capture attitude generation: performing target detection and instance segmentation on the RGB image, identifying a target object and generating a corresponding mask frame, and applying a mask to the depth image to extract depth point cloud data corresponding to the target object; obtaining the depth point cloud data, generating a plurality of groups of grabbing postures, and screening out an optimal grabbing posture; and thirdly, the mechanical arm grabbing execution module receives the optimal grabbing posture information output by the target detection and grabbing posture generation module and controls the robot to complete the grabbing action.

The invention discloses a rope-driven mechanical arm with a variable-stiffness joint module. The rope-driven mechanical arm comprises a base assembly, a shoulder assembly, a large arm assembly, an elbow assembly, a wrist static platform assembly, a wrist parallel mechanism assembly and a wrist movable platform assembly which are movably connected in sequence. The shoulder assembly comprises three variable stiffness joint modules which are orthogonally connected in sequence; the large arm assembly comprises a plurality of driving assemblies; the robot further comprises a wrist bending driving rope assembly, a wrist yawing bending driving rope assembly, a wrist rotation driving rope assembly and an elbow bending driving rope assembly. The multiple driving assemblies are connected with the wrist bending driving rope assembly, the wrist yaw bending driving rope assembly, the wrist rotation driving rope assembly and the elbow bending driving rope assembly correspondingly. The invention has the beneficial effects of inherent flexibility under the condition of low load and high rigidity under the condition of high load.

The invention provides a hydraulic generator inner bore inspection robot based on a dynamic behavior tree and an intelligent decision and operation method thereof, and the robot comprises an upper mechanical arm end executor, a six-axis mechanical arm, an intermediate control system, a mobile platform, a multi-physical quantity fusion detection system, and a central control platform. The method is specially designed for the complex dynamic shielding environment of the inner bore of the hydro-generator. According to the method, by constructing a three-level behavior tree architecture composed of a task decision-making layer, a scene adaptation layer and an execution control layer, inspection requirements are decomposed into environment perception, task execution and resource constraint events, and the events are dynamically mapped to preset behavior nodes. Weight factors are adjusted in real time based on a multi-dimensional evaluation system, and functions of autonomous obstacle avoidance, narrow space operation and anomaly detection are realized in combination with hardware characteristics of the robot. The problems that a traditional inspection method is poor in adaptability and low in efficiency in a dynamic shielding environment are solved, and the intelligent level and safety of hydraulic generator inner bore inspection are remarkably improved.

The invention discloses an industrial robot intelligent control system which comprises a master control terminal, the master control terminal is provided with an industrial-grade multi-core processor and is integrated with a motion control subsystem and an environment sensing subsystem, the motion control subsystem is connected with the master control terminal through a first communication interface, and the environment sensing subsystem is connected with the master control terminal through a second communication interface. According to the motion control subsystem, a distributed axis controller array and reinforcement learning strategy switching algorithm is adopted, so that the trajectory tracking precision and the energy consumption efficiency are improved; and the environment sensing subsystem realizes real-time anomaly detection through multi-mode sensor fusion and an LSTM-GAN fault prediction model. The system supports containerized deployment and edge computing collaboration and integrates AR human-computer interaction, and the adaptive capacity and safety of the industrial robot are remarkably improved.

The invention discloses a dredging mechanical arm intelligent control method and system based on DPPO reinforcement learning, and relates to the technical field of dredging engineering equipment intellectualization. The method comprises the steps that hydraulic pressure, vibration spectrum and temperature data of a dredging and desilting mechanical arm of a dredger are collected in real time and preprocessed; the preprocessed data are input into an LSTM-Attention model, feature extraction is carried out based on a long short-term memory network and an attention mechanism, and a predicted value of the remaining service life of the mechanical arm is output; a degradation-efficiency coupling model is established, and the health index and the operation efficiency of the mechanical arm are calculated based on the predicted value of the remaining service life of the mechanical arm; and a DPPO reinforcement learning controller is established, the mechanical arm health index, the operation efficiency, the energy consumption and the mechanical arm joint angle serve as a state space, an action space of a dynamic load adjusting instruction is output, a control strategy is optimized through a reward function, and the load adjusting instruction is subjected to dynamic constraint processing and then drives a mechanical arm hydraulic executing mechanism.

The invention provides a fruit stem clamping and shearing agricultural robot and a picking control method thereof.The robot comprises a control cabinet, a mechanical arm and a visual navigation system which are installed on a crawler-type chassis supporting platform, and an upper computer control system and a lower computer control system are installed in the control cabinet; and the tail end of the mechanical arm is provided with a movable end effector with an identifying and positioning system and a light supplementing device. The movable end effector can flexibly adapt to picking requirements and complex working conditions of different crops; the upper computer control system is combined with a depth camera and a laser radar to collect data, and a YOLOv8-MC model is utilized to perform identification and key point positioning on a target crop in an image, so that the crop identification precision in a complex environment is remarkably improved, the probability of missing collection and mistaken collection is reduced, and the damage to fruits is reduced; when the upper computer control system constructs a global map, depth correction and map optimization are carried out on illumination changes, and the path planning and operation accuracy of the robot is further improved.

The invention discloses a force / position hybrid control method for a dexterous arm-hand robot, and aims to improve the tracking precision and flexibility of a high-degree-of-freedom robot in a complex interaction task. The method comprises the following steps: constructing a complete kinematics and dynamics model; designing a self-adaptive sliding mode impedance controller, converting an impedance equation into a system taking interaction force as a state, and introducing a sliding mode variable to realize stable tracking of expected force; a preset performance boundary function and a synchronization error are introduced to realize cooperative control and performance constraint; and finally, a force / position hybrid control structure is constructed, the outer ring adjusts the expected trajectory, the inner ring achieves accurate tracking, and therefore smooth interaction between the robot and the complex environment is achieved. The method has good robustness and adaptability, and is suitable for tasks such as grabbing and operation.

The invention discloses an intelligent mechanical arm control system, and relates to the technical field of intelligent mechanical arms, the system comprises a sensor data acquisition module, a multi-modal data fusion module, an environment and target prediction module, an intelligent decision and control module and a control execution and feedback module; through a cooperative working mechanism of the environment and target prediction module and the intelligent decision-making and control module, future state change of a target object can be accurately predicted, possibility is provided for pre-judgment operation of the mechanical arm, then a decision-making optimization algorithm and a multi-target optimization function are applied, an optimal motion strategy is planned for the mechanical arm, and the optimal motion strategy is provided for the mechanical arm. The system further has the real-time feedback and adjustment capacity, it is ensured that the mechanical arm can automatically adapt to changes in the complex dynamic environment, the operation precision and efficiency of the mechanical arm are improved, the capacity of the mechanical arm for adapting to the complex environment and dealing with emergencies is enhanced, and the production efficiency and safety are improved.

The invention relates to the technical field of intelligent mechanical arm control, and discloses a mechanical arm track optimization method and system based on deep learning and a fuzzy algorithm, and the method comprises the steps: building a kinematic model of a mechanical arm, determining the working space of the mechanical arm, carrying out the high-density random sampling, and generating a three-dimensional point cloud picture of a reachable region at the tail end of the mechanical arm; constructing a path planning model, designing a state space and an action space, and constructing a reward function; time-impact double-target optimization is carried out on the tail end path point sequence, a smooth joint trajectory is constructed, and balance between the shortest trajectory execution time and the minimum joint impact is achieved on the premise that speed and acceleration constraints are met; and tracking control is carried out on the trajectory, external disturbance and unmodeled dynamics are estimated and compensated in real time, a parameter adaptive law is designed, and the trajectory tracking precision of the system in a complex environment is improved. The autonomy, the accuracy and the anti-interference capability of the hot-line work mechanical arm in a complex environment are improved.

The invention provides a trajectory planning method for a mechanical arm to pass through a space passing point based on an improved dynamic motion primitive, and belongs to the technical field of robots. Firstly, the mechanical arm obtains a teaching trajectory, an improved dynamic motion primitive algorithm is used for learning the preprocessed teaching trajectory, and the teaching trajectory is obtained; the DMP can generate a motion mode similar to the teaching track; through a depth camera installed around the mechanical arm or at a fixed position, information, shapes and motion states of passing points are obtained in real time, the detected information of the passing points serves as input, and attraction steering force and regression steering force are quantified; the DMP is dynamically updated for real-time adjustment, based on the Lyapunov stability theory, the stability of the improved DMP algorithm is proved, and it is ensured that the motion trail of the mechanical arm in the dynamic environment is converged to a target point; and finally, carrying out an experiment to verify the dynamic passing point crossing capability of the mechanical arm. Under the condition that the starting point and the end point are not changed, an additional learning process is not needed, and the practicability and the efficiency of the system are improved.

A robot includes a first link, a second link aligned with the first link along a rotation axis and extending away from the rotation axis, and an actuator accommodated in the first link to rotate the second link around the rotation axis with respect to the first link. The second link includes a first sub-link and a second sub-link that sandwich the actuator along the rotation axis, the first sub-link is attached to the actuator, and the second sub-link is attached to the first sub-link in a state where the second sub-link is not constrained from the first link in a direction crossing the rotation axis.

Disclosed are a method and system for controlling a robotic arm pressing force based on a pulse wave signal. The method comprises: acquiring a pulse wave signal of a target; acquiring an interaction force between an end of a robotic arm and the target, and determining an expected pressing force according to a mapping relation between a pressing effect and the pulse wave signal; establishing an admittance control model to convert the expected pressing force into an expected trajectory of the robotic arm; and determining, on the basis of an error between an actual trajectory and the expected trajectory of the robotic arm, a control quantity of the subsequent movement of the robotic arm by using a PID controller, so as to achieve the tracking of the expected trajectory. The present invention achieves a low-impedance and compliant interaction effect when the target of pressing is moving.

The invention provides a dynamic obstacle avoidance path planning control method for a redundant mechanical arm, which adopts a self-adaptive artificial potential field dynamic obstacle avoidance algorithm based on a second-order dynamic control barrier function. The problems that a traditional artificial potential field method cannot reach a target, is prone to falling into a local minimum value and cannot effectively deal with a dynamic obstacle are solved. The efficient and safe obstacle avoidance of the mechanical arm in a dynamic complex environment is realized by constructing a self-adaptive velocity repulsion field and a virtual obstacle model, combining a high-order dynamic control barrier function as a safety constraint and utilizing a quadratic programming optimization algorithm. Experimental results show that the method can effectively guide the mechanical arm to escape from the local minimum point, the target point can be reached, meanwhile, the safe distance between the mechanical arm and a dynamic obstacle is guaranteed, the movement efficiency and track quality of the mechanical arm are remarkably improved, and the real-time performance and safety requirements of a dynamic obstacle avoidance task are met.

The invention discloses a rigidity-variable rope traction flexible robot joint, a mechanical arm and a robot. The rigidity-variable rope traction flexible robot joint comprises a tail end motion platform (1), a flexible regulation and control supporting body, a joint driving mechanism, a rigidity-variable driving steering engine (10) and a fixed base (3). The flexible regulation and control support body is connected with the fixed base (3) and the tail end motion platform (1); a flexible supporting sleeve assembly (6) is nested outside a movable supporting ring shaft assembly (9), and a variable-rigidity driving steering engine (10) is mounted in the center of a fixed base (3), is connected with the flexible supporting sleeve assembly (6) and the movable supporting ring shaft assembly (9) and drives the flexible supporting sleeve assembly (6) and the movable supporting ring shaft assembly (9) to rotate relatively to change the rigidity of a flexible regulation and control supporting body; the joint driving mechanism pulls the tail end motion platform (1) through a rope to drive the flexible regulation and control supporting body to bend, and motion of the joint robot is achieved. And the two-degree-of-freedom bending motion capacity is achieved, active rigidity regulation and control are achieved by reconstructing an internal structure, external environment conditions do not need to be changed, and regulation and control of the joint rigidity are achieved.

A method for retrieving an inventory item based on a handling robot, where the handling robot includes: a storage frame; and a material handling device installed on the storage frame, and including a telescopic arm and a manipulator installed to the telescopic arm; and the method for retrieving an inventory item includes: driving, by the telescopic arm, the manipulator to extend to a preset position of warehouse shelf along a preset horizontal reference line; loading, by the manipulator that is remained on the reference line, the inventory item located in the preset position; driving, by the telescopic arm, the manipulator loaded with the inventory item to move to the storage frame along the reference line; and unloading, by the manipulator that is remained on the reference line, the inventory item to the storage frame.

A robot arm for minimally invasive surgery includes a modular structure in which a yaw axis of an active arm rotatable relative to a setup arm is disposed to be inclined with respect to a roll axis of an instrument for surgery and a joint of the active arm and a remote center of motion (RCM) form a parallelogram, thereby preventing a gimbal lock phenomenon, enabling configuration of various RCMs, and having various entry angles.

The invention relates to the technical field of transfer robots, in particular to an intelligent warehouse logistics automatic transfer robot. The robot comprises a scraper and a suction cup which are arranged on a robot body, the scraper is provided with specially-designed groove pressing teeth, an arc-shaped groove with a guiding function can be formed in the surface of a box body, and it is ensured that a frost layer effectively falls off along a preset path through the design. The system collects temperature gradient, surface appearance and contact stress data in real time through the integrated multi-mode environment sensing unit, the intelligent decision-making unit establishes a dynamic control model based on the data, and accurate evaluation and self-adaptive processing of the frost layer state are achieved. The whole system constructs a complete closed-loop control chain, the limitation of a traditional defrosting method is overcome, the adsorption stability is kept, meanwhile, the operation efficiency is improved, the system is particularly suitable for the low-temperature storage environment of-25 DEG C and below, and a reliable solution is provided for cold-chain logistics automatic operation.

A robot arm joint, a connector and a robot arm are disclosed. The robot arm joint has a joint body and a joint wire assembly. The joint body includes a fixing end and a transmission end. The joint wire assembly is arranged in the joint body and extends on a preset connection path. The preset connection path includes a straight path. The joint wire assembly includes a torsion section connected to the transmission end and a fixed section connected to the fixing end. When the transmission end rotates, the torsion section is simultaneously driven to twist on the straight path.

The invention discloses a visual servo control method and system for assembling multi-shaft-hole parts through multiple mechanical arms in a tight coordination mode, and belongs to the field of multi-robot cooperative control. The invention aims to solve the problems of visual feature extraction and collaborative assembly control in the task of tightly coordinating and assembling large-size multi-shaft-hole parts by multiple mechanical arms. The method is technically characterized in that a shaft hole assembly task image data set containing different hole shapes is established and used for training a positioning neural network and a relative state estimation neural network, and a state classification neural network is used for dividing the states of shafts and holes in images into three types of shielding, separation and overlapping; the positioning neural network is used for determining the positions of the shaft and the hole in the image. A multi-mechanical-arm visual feature fusion method based on the virtual acting force is provided, the virtual acting force acting on an object is calculated according to position information and relative state information of a shaft and a hole in an image, and the virtual acting force is input into a cooperative controller of the multiple mechanical arms to achieve cooperative assembly movement of the multiple mechanical arms. And natural combination of visual features and a multi-mechanical-arm cooperative controller is achieved. Finally, the method provided by the invention is verified in the task of tightly coordinating and assembling the double-shaft-hole parts by the double mechanical arms with the assembling clearance of 0.2 mm, the success rate reaches 85%, and the effectiveness of the method is verified.

The invention discloses a mechanical arm vibration auxiliary automatic sea-buckthorn harvesting device based on machine vision and a grabbing point positioning and sea-buckthorn harvesting method, relates to the technical field of sea-buckthorn harvesting, and solves the problem that sea-buckthorn harvesting is difficult. A crawler belt moving device is provided with a rubber crawler belt, a picking mechanical arm has six degrees of freedom, and the mechanical arm has six degrees of freedom; the tail end execution device is sent to a harvesting position in a point-to-point mode, the tail end execution device vibrates branches, close to the tail end, of a sea-buckthorn tree and detects the vibration amplitude of the branches of the sea-buckthorn tree, then comparison of a binocular vision system is combined, an excitation power source is combined to output an electric signal with the appropriate frequency, the tail end execution device has the optimal amplitude, and the recovery efficiency is improved; the collecting umbrella device works cooperatively and is unfolded when the tail end executing device works, fallen fruits are collected, the fallen fruits fall into a fruit frame of the fallen fruit collecting device, the fruit frame is placed on a pressure sensor, workers are informed to timely come to replace the fruit frame, and efficient and continuous harvesting work of the sea-buckthorn trees planted in a plantation in a large scale is achieved.

Disclosed is a contaminated site sampling robot and an intelligent sampling method thereof. The robot includes a body with a walking mechanism, vision sensing system, drilling mechanism, and negative-pressure suction mechanism. The walking mechanism features two servo motors within a mounting platform, with track wheels attached to the servo motors' output ends. The vision sensing system comprises a supporting frame at one end of the mounting platform, equipped with a vision sensing camera and radar sensor. The drilling mechanism consists of a U-shaped base on the mounting platform's top, with a mechanical arm and drilling machine mounted at one end. The negative-pressure suction mechanism includes a U-shaped box on the mounting platform's middle part, housing a vacuum cleaner whose dust outlet connects to a sample collecting box. This setup allows for optimal path identification, positioning, obstacle avoidance, and control of sampling conditions, facilitating intelligent sampling of contaminated sites.