3201 results about "Humanoid robot nao" patented technology

The invention discloses a multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

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.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

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 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 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.

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.

The embodiment of the invention provides a humanoid robot self-adaptive control method and system, electronic equipment and a storage medium, and belongs to the technical field of robot intelligent control. The method comprises the steps that electroencephalogram signals and electromyographic signals of a target object are collected; determining a first time-frequency domain feature of the electroencephalogram signal according to the electroencephalogram signal, and determining a second time-frequency domain feature of a muscle activation state according to the electromyographic signal; fusing the first time-frequency domain feature and the second time-frequency domain feature by adopting a dynamic weighting algorithm to obtain a multi-modal feature; inputting the multi-modal features into an action intention recognition model to obtain a predicted action intention; and determining a robot control instruction according to the predicted action intention and the current state of the robot so as to control the humanoid robot. The precision of self-adaptive control of the humanoid robot can be improved.

The invention discloses a robot anthropomorphic motion control method and system based on skeleton mapping. The method and system are used for enhancing anthropomorphic motion performance and environment adaptability of a humanoid robot. The method comprises the following steps: calculating a scale proportion and a posture transformation matrix between a human body and the humanoid robot based on initial posture data; according to the motion data, recursively calculating original pose information of each human body joint in a world coordinate system through a forward kinematics algorithm; mapping the original pose information into expected pose information of the humanoid robot on the key coordinate system by applying a scale proportion and a pose transformation matrix; a target joint angle of the humanoid robot is solved according to the expected pose information, and contact state information of legs of the humanoid robot and the ground is calculated according to the target joint angle; and the target joint angle and the contact state information serve as input, a motion control strategy of the humanoid robot is trained through a deep reinforcement learning algorithm, and humanoid motion control over the humanoid robot is achieved.

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.

The invention provides a wheeled humanoid robot which comprises a wheeled chassis walking mechanism, a robot trunk mechanism is arranged above the wheeled chassis walking mechanism, a robot head mechanism is mounted on the robot trunk mechanism, and robot arm mechanisms are mounted on the two sides of the robot trunk mechanism. The end, away from the robot trunk mechanism, of each robot arm mechanism is provided with a clamping jaw. A robot lower limb waist mechanism is arranged between the robot trunk mechanism and the wheel type chassis walking mechanism, and the robot trunk mechanism and the wheel type chassis walking mechanism are connected through the robot lower limb waist mechanism. Through cooperative arrangement of all the structures and arrangement of the wheeled humanoid robot, the problem that in the prior art, a foot type humanoid robot is low in moving speed under the flat ground environment is effectively solved, the degree of freedom of the whole robot is high, the robot can adapt to various working environments, and popularization is facilitated.

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.

The invention belongs to the technical field of humanoid robot control, and particularly relates to a humanoid robot two-arm self-adaptive force control operation method and system, and the method comprises the steps: building a coordinate conversion algorithm between a world coordinate system and a left arm base coordinate system, a right arm base coordinate system, a left arm tail end coordinate system, a right arm tail end coordinate system and an operation target coordinate system; six-dimensional force data and torque data collected by six-dimensional force sensors at the tail ends of the left mechanical arm and the right mechanical arm are obtained, and gravity compensation and conversion are conducted; defining a grasping matrix to decompose the six-dimensional force data and the torque data into internal force and external force; dividing the cooperative task into a free motion stage, a constraint establishment stage and a constraint motion stage, and generating a corresponding control instruction in each stage; and the generated control instruction is input into a double-arm robot system, and coordinated motion control of the double-arm robot in different stages is achieved. According to the invention, coordination work can be completed and anti-interference capability is possessed.

The invention provides a natural language control method for a humanoid robot. By constructing the simple skill library and the complex skill library of the robot, the dependence on a large amount of operation data in a specific field is effectively reduced. According to the method, a large language model is used as a base of a task planner, and the capabilities of scene learning, semantic understanding and common sense reasoning of the task planner are brought into full play. By introducing a visual model in the field of computer vision, high-level semantic information and low-level spatial information of the environment can be obtained, and an accurate scene graph is constructed to serve as environment state representation. According to the voice interaction interface constructed by the invention, the naturalness of man-machine interaction is improved, so that a user can more conveniently communicate with the robot and input instructions, the user experience is effectively improved, and popularization of the humanoid robot in an actual application scene is facilitated.

The invention discloses a three-degree-of-freedom series-parallel hybrid humanoid robot waist structure, and relates to the technical field of robots. The defects that an existing multi-degree-of-freedom waist structure is not compact enough, limited in precision, low in rigidity and poor in stability are overcome. The robot comprises a yaw joint mechanism and a pitching-sidesway joint mechanism which are connected in series, upper limbs and legs of the robot are connected through the yaw joint mechanism and the pitching-sidesway joint mechanism, and the yaw joint mechanism can drive the upper limbs of the robot to do yaw motion; the pitching-sidesway joint mechanism comprises a movable platform, a static platform, a joint universal joint and two joint driving assemblies, the movable platform is fixedly connected with the upper limbs of the robot, the static platform is fixedly connected with the legs of the robot, the joint universal joint is connected with the movable platform and the static platform, and the two joint driving assemblies are symmetrically arranged on the left side and the right side of the joint universal joint; and the two joint driving assemblies are connected with the movable platform and the static platform correspondingly, and when the two joint driving assemblies drive the movable platform, the movable platform can do pitching motion and / or side-sway motion around the joint universal joint.

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 discloses a humanoid robot whole-body coordination control method based on a mass center dynamical model. The method comprises the steps that system modeling is conducted on a humanoid robot according to the mass center dynamical model; establishing a relationship between arm momentum and centroid momentum based on whole-body kinematics and a momentum conservation principle; a whole nonlinear optimization control problem is constructed and solved in real time, and a controller predicts the motion trail of the robot in a future period of time; feedforward torque is obtained through inverse dynamics calculation, and joint torque is issued in combination with a joint PD controller. The invention designs a nonlinear model predictive control problem for whole-body coordination control of the humanoid robot, reasonably plans a system state, an arm tail end state, a foot end state and a reference trajectory of control input, and designs equality constraints and inequality constraints necessary for whole-body coordination movement of the robot; task trajectory constraint and momentum conservation constraint on the tail end of the arm are added, so that the ability of the arm to assist in motion balance is more prominent.

The embodiment of the invention provides a humanoid robot flexible assembly method and system based on a thinking chain and a medium, and belongs to the technical field of natural language processing. According to the scheme, the assembly task instruction provided by the user is obtained, the assembly task instruction is subjected to task analysis through the thinking chain model, the weight of each word in the assembly task instruction is calculated through the attention mechanism in the analysis process, and the analysis target related to the current task is dynamically adjusted according to the weight; the task is decomposed into a plurality of subtasks layer by layer according to an analysis target, a task tree structure is obtained, the task tree structure is traversed to determine an assembly plan, the assembly plan comprises an execution arrangement sequence, task path planning and assembly action parameters, and the humanoid robot is controlled to execute assembly operation according to the assembly plan. The task information is automatically extracted by combining a hierarchical thinking chain and an attention mechanism and is converted into assembly operation, a clear execution plan is generated, and the flexibility of task analysis is improved.

The invention relates to the technical field of robots, and discloses a leg structure and a humanoid robot. A motor template; the hip joint assembly is mounted on the thigh bracket and comprises a rotary mounting frame which is in assembly connection with the motor template; the first end of the hip linear actuator is rotationally connected with the thigh support, and the second end of the hip linear actuator is connected with the hip joint assembly; the shank support is connected with the lower portion of the thigh support through a knee joint assembly; the knee joint linear actuator is arranged on the thigh support, and the driving end is connected with the knee joint assembly; the foot support is connected with the lower portion of the shank support through an ankle joint assembly; the first end of the ankle linear actuator is rotationally connected with the shank support, and the second end of the ankle linear actuator is rotationally connected with the foot support. According to the humanoid robot, the corresponding linear actuators are arranged on the thigh support and the shank support, the space on the thigh support and the space on the shank support are fully utilized, the inertia of the leg structure is reduced, the response speed is high when the leg structure conducts corresponding actions, and the dynamic stability of the humanoid robot is improved.

The invention relates to the technical field of mechanical joints, in particular to a humanoid robot linear electric drive joint which comprises a shell, a front end cover and a rear end cover, the front end cover and the rear end cover are fixed to the two ends of the shell respectively, and an execution assembly and a drive control assembly are arranged in the shell; the execution assembly comprises a reverse planetary roller screw and a motor, and the drive control assembly comprises an encoder composed of a coded disc and a magnetic ring, a pull pressure sensor and a driver. Aiming at the technical problem that an existing linear joint has defects, a reverse planetary roller lead screw is adopted as a main transmission component, and the lead screw, a roller and a nut of the reverse planetary roller lead screw are in linear contact, so that high precision, long service life, high integration level and high thrust density can be achieved while high bearing and high speed are achieved; therefore, the method is suitable for use scenes of humanoid robot joints.

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.

The invention discloses a humanoid robot grabbing method based on three-dimensional vision, and the method comprises the following specific steps: S1, environment modeling and perception: carrying out the three-dimensional scanning of a working environment, and constructing a three-dimensional map of the environment; s2, object identification and 6D attitude estimation: identifying a target object, and estimating the position and attitude of the object in a camera coordinate system; s3, coordinate conversion and target positioning, wherein the posture of the object is converted into a robot base coordinate system from a camera coordinate system through TF conversion; and S4, path planning and motion control are conducted, specifically, path planning is conducted through a cumotion algorithm, a grabbing path is generated, and the two arms are driven to move according to the planned track to achieve grabbing. By means of environment modeling and sensing, the robot can update environment information in real time, conduct three-dimensional scanning on the working environment and generate the three-dimensional grid model of the environment in real time, the model is used for obstacle detection and avoidance planning, and the dynamic environment adaptability of the robot to object grabbing is improved.

The invention provides a humanoid robot falling control method, device and equipment and a storage medium, and relates to the technical field of humanoid robots. The control method comprises the following steps: acquiring state data of the humanoid robot in real time; predicting the current state of the humanoid robot according to the state data; and if the current state is the high-risk tumble state, controlling the humanoid robot to execute tumble protection action. According to the fall control method for the humanoid robot, the accuracy of fall prediction is improved by monitoring the gravity center position, the movement speed and the acceleration in real time. The method can quickly start the protection action under the high-risk condition, reduce the damage risk and protect the safety of surrounding personnel. The adaptability and autonomy of the robot are enhanced, the stability and safety are improved, the maintenance cost is reduced, and the service life is prolonged, so that the practicability and market competitiveness of the robot are enhanced, and the application range is widened.

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.