2206 results about "Humanoid robot nao" patented technology

The invention provides an autonomous navigation path planning method, device and equipment for a humanoid robot in a complex environment. A navigation task is determined by obtaining the current position and path requirement of the robot; constructing a constraint preposition map based on body size constraint, joint motion constraint and dynamic balance constraint, and calculating a feasible motion space of the robot in advance; acquiring environment sensing data for variation trend analysis, and predicting a future movement track of the dynamic obstacle to generate a dynamic environment prediction sequence; establishing a bidirectional planning mechanism, performing forward and reverse path search at the same time, and generating a plurality of candidate path hypotheses through meeting point detection and path fusion; analyzing a human behavior pattern to identify a potential passage conflict area, establishing a social negotiation strategy, and taking an intermediate meeting point as a social key node to adaptively adjust a candidate path; the optimal safety path is determined through multi-dimensional safety evaluation of the falling risk, the collision risk and the energy consumption risk, and intelligent autonomous navigation of the humanoid robot in a complex environment is achieved.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a rear shell, and a frontal shell coupled to the rear shell to define a head volume between the frontal shell and the rear shell, and an electronics assembly with a display located in the head volume between the frontal shell and the rear shell. The frontal shell of the head housing assembly includes: a first arc length at a first location below the display, a second arc length at a second location aligned with a portion of the display, and wherein said second arc length is greater than the first arc length, and a third arc length at a third location above the display, and wherein said third arc length is greater than both the first arc length and the second arc length.

A mechanical end effector for a humanoid robot includes a plurality of identical finger assemblies. Each of the finger assemblies is removably connected to a frame. Each of the finger assemblies is fully self-contained and operable independently of every other one of the finger assemblies and independently of every other component connected to the frame. Each of the finger assemblies includes a single electric motor and is configured to be fully operable using only the single electric motor.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

A humanoid robot leg that may include a shank, an ankle that includes an ankle joint that is configured to perform yaw and pitch rotations, a knee joint, multiple ankle related (AR) rotational motors that are in mechanical communication, via multiple AR transmission mechanisms, with the ankle joint, and a knee related (KR) rotational motor that is in mechanical communication, via a KR transmission mechanism, with the knee joint. The multiple AR rotational motors and the KR rotational motor are positioned above the knee joint.

A humanoid robot includes a torso, a left arm assembly coupled to the torso and having a first reference line, a left wrist coupled to the left arm assembly and including at least a rotational axis, and a left end effector coupled to the left wrist. The left end effector is configured to move about the rotational axis and includes a finger assembly with a second reference line and at least two degrees of freedom, and a thumb assembly with at least three degrees of freedom. A first angle is formed between the first and second reference lines when the left wrist is in a first configuration, and a second angle is formed when the left wrist is in a second configuration. Both the first and second angles are greater than 70 degrees, and the difference between the first and second angles is greater than 150 degrees.

The present disclosure provides a humanoid robot with an arrangement of components that allows the robot to mimic the movements, functionality and capabilities of a human being. The robot includes a torso coupled to a waist, an arm assembly, and a head assembly. A pelvis is coupled to the waist and has left and right actuator mounts. Left and right hip assemblies are coupled to the respective actuator mounts. Each hip assembly includes a hip pitch actuator assembly, a hip roll actuator assembly, and a leg twist actuator assembly. The hip pitch actuator assembly has a portion positioned within the pelvis and is coupled to the actuator mount. The hip roll actuator assembly is coupled to the hip pitch actuator assembly, with a non-90 degree angle formed between their axes. The leg twist actuator assembly is coupled to the hip roll actuator assembly and positioned below extents of both the hip pitch and hip roll actuator assemblies.

The present disclosure provides a humanoid robot comprising a torso portion, an arm assembly extending from the torso portion, a leg assembly extending from the torso portion, and an exterior covering system. The exterior covering system is configured to cover at least a portion of the torso portion, the arm assembly, and the leg assembly. The exterior covering system includes an energy attenuation assembly positioned adjacent to at least one of the torso portion, arm assembly, or leg assembly. The exterior covering system further includes a cover assembly positioned over the energy attenuation assembly, and a coupling means for securing the cover assembly to the humanoid robot.

The invention discloses a multi-modal collaborative decision-making method and device for a humanoid robot in an industrial scene, and relates to the technical field of robot control, and the method comprises the steps: obtaining a multi-frame monitoring image, force touch sensor data and equipment operation sensor data of a visual camera of the humanoid robot in the industrial scene; extracting feature vectors of the multi-frame monitoring image, the force touch sensor data and the equipment operation sensor data as multi-modal feature vectors; taking the multi-modal feature vector as a training sample to train a cross-modal model, so that the cross-modal model learns an association relationship among vision, haptic and equipment data; obtaining dynamic industrial scene data, extracting feature vectors, and inputting the feature vectors into the trained cross-modal model to generate scene description information; and outputting an operation decision instruction according to the scene description information so as to control the humanoid robot. Through cross-modal feature fusion and dynamic knowledge reasoning, the task execution precision and environmental adaptability of the humanoid robot in a complex industrial environment can be improved.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an electronics assembly. The electronics assembly includes various components and devices used in the operation of the humanoid robot.

The invention relates to the field of robot control, and provides a humanoid robot control system and method based on reinforcement learning, and the humanoid robot control system comprises a first control subsystem and a second control subsystem. The first control subsystem comprises a strategy reasoning module, a state conversion module and a robot control module; the second control subsystem comprises a data acquisition module and a driving control module; the data acquisition module is used for finishing timestamp alignment and abnormal value filtering of sensor data and transmitting the data to the state conversion module; the state conversion module is used for fusing multi-source sensor data and constructing a time sequence state feature containing a real-time measurement value and historical time sequence information; the strategy reasoning module is used for generating a multi-joint angle target value of the robot according to the time sequence state characteristics provided by the state conversion module; and the robot control module is used for analyzing the multi-joint angle target value output by the strategy reasoning module, selecting a control mode and generating a control command comprising a parameter adjustment instruction.

Various systems and methods are described for obtaining compensation for tasks performed by a humanoid robot, where the humanoid robot is associated with a first party. The method includes a first party providing a humanoid robot for use in an operating location. The humanoid robot engaged in performing a plurality of tasks at the operating location. A third party compensates the first party with a specified amount of currency for a pre-determined time interval during which said humanoid robot has engaged in performing the plurality of tasks at the operating location.

The invention belongs to the technical field of robot control, and discloses a high-gear-ratio humanoid robot robust walking control method and system based on potential dynamics self-adaption, and the method comprises the steps: designing a control strategy training frame based on deep reinforcement learning, and carrying out the optimization through an Actor-Critic structure and a PPO algorithm; constructing a potential dynamic adaptive network LDAN, and extracting environment and ontology dynamic parameters through a variational auto-encoder; designing a multi-dimensional reward function; constructing a periodic gait library by using von Mises distribution and motion capture data; gradually introducing terrain disturbance and dynamic change through curriculum type simulation training; the trained strategy and the LDAN module are deployed on the high-gear-ratio driven humanoid robot, the problems that the high-gear-ratio humanoid robot is poor in motion stability, poor in adaptability and insufficient in action expression in a variable environment are solved, and the high-gear-ratio driven humanoid robot has the advantages of being high in robustness, high in natural expressivity and high in migration ability.

A head and neck assembly for a humanoid robot, including a head portion having an exterior surface defining an overall shape resembling a human head; a neck portion extending from the head portion; a head housing assembly enclosing the head portion and neck portion; an electronics assembly contained within the head housing assembly; and a head actuator assembly configured to move the head portion relative to a torso of the humanoid robot.

The invention provides an intelligent grabbing control method and system based on tactile perception, and the method comprises the steps: obtaining a tactile feedback signal through a sensor array of a humanoid robot, carrying out the frequency domain decomposition of the signal through a Fourier transform method, and carrying out the analysis of the pressure and deformation data of each contact point on the surface of a to-be-grabbed object, calculating to obtain frequency characteristic distribution of the tactile feedback signal; if the initial estimation value of the object rigidity gradient exceeds a threshold value, performing finite element analysis on the object rigidity gradient, and combining a time domain processing result of the tactile feedback signal to obtain object rigidity gradient distribution; carrying out probability modeling on the deformation field by adopting a Bayesian inference method according to the gradient distribution of the rigidity of the object, and carrying out iterative optimization on a modeling result to obtain dynamic update parameters mapped by the deformation field; and regenerating an action sequence of the end effector of the humanoid robot based on the optimized strategy according to a trigger condition of grabbing failure detection, and determining a final grabbing control parameter.

The invention relates to the technical field of robot intelligent control, in particular to a humanoid robot multi-modal environment sensing and self-adaptive chassis control method, which comprises the following steps: monitoring a contact force vector and a sliding trend in real time through a multi-dimensional touch sensor, mapping pose drift to a chassis coordinate system based on a Lie group constraint space-time synchronization layer, and performing multi-modal environment sensing and self-adaptive chassis control on the basis of a multi-dimensional touch sensor; a compensation vector is generated, a three-level tactile response layer dynamic switching force / bit mixed impedance mode is adopted, reverse translation of an omnidirectional chassis is combined to counteract drift, compensation parameters are optimized through a depth deterministic strategy gradient algorithm, and the system ensures control instruction time sequence alignment through a time-space stamp synchronization engine. The attitude oscillation in the compensation process is suppressed by using the inertial measurement unit, the obstacle avoidance interference domain is constructed based on the kinematics chain of the operation arm, and the compensation trajectory is smoothed by using the B-spline curve, so that the precision, stability and safety of the humanoid robot in the complex contact operation are improved, and an efficient and reliable solution is provided for a man-machine cooperation scene.

The invention discloses a humanoid robot real-time cooperation decision-making method based on multi-mode perception fusion. The method comprises the following steps: dynamically deploying a sensor array, acquiring multi-modal original sensing data of the humanoid robot through the deployed sensor array, and performing physiological signal fusion processing and multi-modal feature enhancement processing on the multi-modal original sensing data to obtain a multi-level feature set; performing cross-modal semantic mapping and intention reasoning on the multi-level feature set by using a cross-modal semantic reasoning system supported by a large model to obtain a cooperative execution instruction containing a target action and a force control parameter; and dynamically updating a behavior decision model of the humanoid robot by using a double-buffer collaborative distributed incremental learning mechanism based on the collaborative execution instruction, and obtaining a real-time collaborative decision of the humanoid robot based on the behavior decision model. The technical problem that the man-machine cooperation efficiency of the humanoid robot in a complex scene is low is solved.

The invention provides a humanoid robot inertial navigation and vision fusion positioning method, device and equipment. The method comprises the following steps: firstly, pre-evaluating the quality of IMU inertial data and visual SLAM data to generate a data credibility identifier; establishing a causal relationship chain between the data sources through time sequence correlation analysis; constructing a multi-strategy superposition state based on data credibility, and determining an adaptive fusion strategy through collapse judgment; constructing a data evolution path by using a causal relationship, and generating an expected behavior feature and a causal deviation feature; the fusion parameters are optimized according to the deviation characteristics, and a plurality of fusion positioning estimations with different weights are generated; and through space aggregation analysis and confidence weight calculation, selecting optimal positioning estimation and outputting a high-precision fusion positioning result. According to the method, deep understanding of sensor data, intelligent selection of fusion strategies and robust output of positioning results are realized, and the positioning performance of the humanoid robot in a complex environment is improved.

The invention relates to a humanoid robot double-arm body operation method based on reinforcement learning, belongs to the field of robot cooperative control, and is characterized in that a strategy of trajectory block prediction and time integration fusion is used for double-arm cooperative control, and continuity and stability of double-arm operation are improved; in reinforcement learning control, a three-dimensional pose track generation and correction module is introduced, condition generation and denoising correction of a three-dimensional space are carried out on a track block layer, and geometric consistency and naturalness of a generated track are guaranteed; the invention further provides a reinforcement learning optimization framework and a simulation-reality migration process, and through system integration of reward, value guidance and migration processes, strategy deployability and safety are guaranteed; compared with the prior art, the method has the advantages that the naturalness, the collaboration and the success rate of double-arm operation can be remarkably improved, the generalization ability is high, and the good simulation-to-reality migration ability is achieved.

The invention provides a humanoid robot action coordination method and system based on joint torque, and the method comprises the steps: obtaining the original data of the joint torque of a humanoid robot through a sensor, carrying out the preprocessing of the original data of the torque, outputting stable torque data, constructing an impedance control model through the combination of the angle data of the mechanical arm joint of the humanoid robot, and carrying out the control of the mechanical arm joint. Predicting a moment change trend to obtain a predicted moment sequence; adaptive threshold detection is carried out on the predicted moment sequence, and moment peak values of different amplitudes are identified; analyzing the propagation velocity of the torque peak value, and calculating a velocity vector of peak value propagation by combining the propagation direction of the torque peak value; whether the velocity vector of peak propagation exceeds a preset threshold value or not is judged, and if yes, the response trend of a joint torque controller is predicted to judge whether joint control parameters of the humanoid robot need to be adjusted or not; and if the joint control parameters of the humanoid robot need to be adjusted, optimizing the rigidity and damping parameters of the impedance control model to output a stable control signal.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an electronics assembly. The electronics assembly includes various components and devices used in the operation of the humanoid robot.

A humanoid robot includes an upper region with a torso, a pair of arm assemblies coupled to the torso, and a head and neck assembly. The head and neck assembly includes a neck portion coupled to the torso and a head portion coupled to the neck portion. The head portion has a head housing assembly with a first shell and a second shell defining a head volume. The first shell includes an opening with a cover extending across at least a majority of the opening. A first camera and a second camera are located within the head volume, each having horizontal and vertical fields of view exceeding specified angles. The first and second camera bodies are positioned less than 15 centimeters apart. The robot also includes a lower region with a pair of legs coupled to and spaced apart from the upper region.

The invention relates to the technical field of robot cooperative control, in particular to a humanoid robot body intelligent cooperative control system based on multi-modal sensing fusion, and the system comprises a multi-modal sensing fusion module which recognizes a target boundary position, analyzes a pressure change, and combines with a posture to extract audio features to generate an environment sensing graph; the dynamic time sequence adjustment module optimizes an action rhythm adjustment detail generation execution plan, the cross-modal behavior correction module corrects an offset optimization track generation coordination sequence, the task priority distribution module analyzes task distribution to generate an execution list, and the time sequence conflict correction module optimizes a path adjustment conflict generation coordination path. According to the method, an environment perception graph is constructed through matching and fusion of multi-source perception data, the action sequence and interval are dynamically adjusted to optimize an execution chain, trajectory offset is corrected to improve action precision, nearest response and load balancing are achieved through real-time task allocation, conflict blocking is reduced through path rearrangement and time sequence coordination, and a perception decision execution closed loop is formed; and the identification precision and the cooperation efficiency are improved.

The invention discloses a humanoid robot somatosensory control system and method based on deep learning. The method comprises the steps that physiological data and environment information of a user are collected through a wearable device and an environment sensor, and full-modal synchronous collection is achieved in combination with audio and visual data. The system carries out noise reduction processing on the collected sound, extracts a voice instruction and recognizes non-language sound features to judge the physiological state of the user; and analyzing the posture and gesture intention of the user through a human body detection and posture recognition algorithm. And multi-modal data is further fused, the intention of a user is accurately judged in combination with scene context and a security verification loop, a multi-dimensional state space and an atomic action library are constructed, and a composite reward function is designed to realize intelligent decision and execution of robot actions. The system comprises a multi-modal data acquisition module, a user intention understanding module, a dynamic action planning module, a safety monitoring and exception handling module and a continuous learning and collaborative optimization module, and can effectively improve the intention recognition precision, environmental adaptability and task execution efficiency of the robot in a complex home scene. And intelligent and personalized family service support is provided for the elderly.

The present disclosure provides a system for generating motor control commands for a humanoid robot, comprising an alpha model with over 1 billion parameters that processes visual observations and language instructions at a first frequency to generate contextual embeddings, and a beta model operating at a higher second frequency. The beta model includes an embodiment-specific state encoder projecting robot state information into a shared embedding space, a diffusion transformer module generating denoised action sequences through iterative flow-matching that cross-attends to the alpha model's contextual embeddings, and an embodiment-specific action decoder converting denoised sequences into motor control commands. The beta model generates action chunks comprising future action sequences over a predetermined time horizon in a single inference step, with the complete system having less than 5 billion parameters.

The present disclosure provides a humanoid robot comprising a torso having an alpha model deployed on a first GPU, and wherein said alpha model includes a first number of parameters and is configured to receive a natural language command from a human and generate processed data, a beta model deployed on a second GPU, and wherein said beta model includes a second number of parameters and is configured to receive the processed data from the alpha model and provide output data used to control an extent of the left wrist, and wherein the first number of parameters is larger than the second number of parameters, and a unified training framework is used to jointly train the alpha model and the beta model.

The embodiment of the invention provides a humanoid robot industrial task scene generation method and system based on a large language model, and belongs to the technical field of industrial automation and artificial intelligence. The method comprises the steps of obtaining an industrial task database; extracting and converting the task parameters according to an industrial task database to obtain first task prompt information; performing thinking chain task decomposition on the industrial task to obtain subtask sequence information; training the large language model to obtain an initial task execution strategy; performing multi-dimensional evaluation on the initial task execution strategy to obtain an evaluation result; optimizing the first task prompt information according to the evaluation result to obtain second task prompt information; and adjusting the large language model according to the second task prompt information to obtain a target task execution strategy. According to the embodiment of the invention, the task adaptability and strategy reliability of the humanoid robot in a complex industrial scene can be improved, and the flexibility and efficiency of industrial automatic production are remarkably improved.

A robotic torso for a humanoid robot includes a first torso member having an axial axis aligned with a reference axis and a second torso member having at least one mounting portion for at least one humanoid component. The robotic torso includes a series of actuators arranged in a column between the first torso member and the second torso member and coupling the first torso member to the second torso member. Each of the actuators has a rotatable member defining a rotational axis. The rotational axes of each adjacent pair of actuators in the series of actuators are orthogonal to each other.

The invention discloses a humanoid robot object interaction method guided by a diffusion model based on key frames. Comprising the following steps: S1, generating interaction between a human body and an object based on a diffusion model of a key frame; s2, guiding the humanoid robot to interact with the object based on the contact instruction; and S3, interaction between the humanoid robot and the object based on the action generation guidance. According to the method, in an action generation stage, key frames of a robot and an object within a period of time are predicted according to a current state, a motion sequence is recovered by using interpolation, and then a universal robot and object interaction strategy is finely adjusted based on a generated and simulated closed-loop structure and a fixed diffusion model weight. The interaction strategy is rewarded due to accurate simulation of aligned movement and contact information and successful object control, the gap between the output of the diffusion model and the training data set of the simulation environment is made up, and the reward does not need to be designed for a specific task.

The invention discloses a full-size integrated humanoid robot which comprises a trunk mechanism, arm mechanisms arranged on the left side and the right side of the upper end of the trunk mechanism, a hip mechanism arranged at the lower end of the trunk mechanism and leg mechanisms arranged at the left end and the right end of the hip mechanism. The driving modules of the arm mechanisms and the leg mechanisms are mounted in the inner cavities of the corresponding curved-surface metal shells through bolts respectively; the hip mechanism comprises a hip assembly and hip curved surface shells installed on the front side and the rear side of the hip assembly. The hip assembly comprises a hip base connected with an output shaft of a trunk driving module of the trunk mechanism. Hip driving modules are symmetrically arranged at the left end and the right end of an inner cavity of the hip base; a control circuit board of the hip driving module is arranged on the top of the hip base through a fixing plate. On the basis of replacing a traditional shell and a skeleton with the curved metal shell, the size of the hip mechanism is reduced, the assembly steps are simplified, the light weight of the humanoid robot is achieved, and the requirement for structural strength is met.