Nerve field guided humanoid robot self-adaptive door opening control method

Abstract

The invention relates to the technical field of robots, in particular to a neural field guided humanoid robot self-adaptive door opening control method which comprises the steps that point cloud information of a door opening area is obtained, a neural field is constructed, the position and posture of a door handle are detected, and the neural field is formed through boundary information and normal information of a door body structure; generating a collision-free approach path by using the boundary information, the normal information and the door handle pose, and controlling the robot to approach and grab the door handle by using quadratic programming; applying an initial pulling force through a preset admittance control strategy by using normal information, and estimating the position of the door spindle in combination with modeling optimization; based on the door spindle position, a structure consistency arc path is constructed in combination with boundary information and normal information; and in combination with boundary information, controlling the whole robot to execute actions according to a collision-free approaching path and a structure consistency arc path by adopting weighted quadratic programming. According to the method, a neural field is embedded into a key control link, and task-level dynamic optimization under perception driving is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robots, in particular to a neural field guided humanoid robot adaptive door opening control method. BACKGROUND

[0002] With the rapid development of robot technology in various fields, humanoid robots have superior environmental adaptability and task flexibility due to their structural bionics, and have become a research focus. Among them, door opening operation as a basic and challenging task has attracted widespread attention. When a humanoid robot performs a door opening task, it mainly faces the following two main challenges: on the one hand, the different reaction forces of the movable and immovable directions of the door limit the degrees of freedom of the robot's end motion, and the humanoid robot needs to adjust flexibly during the process of pushing and pulling the door to adapt to the changes in the reaction force of the door; on the other hand, the reaction force of the door fluctuates dynamically with the change of the motion state during the door opening process, further increasing the uncertainty in the operation process. Therefore, the humanoid robot needs to identify the movable trajectory of the door online during the door opening process, while considering maintaining appropriate contact force between the robot and the door, so as to complete the set task.

[0003] Related technical personnel have made some research on the control method of robot door opening operation. Existing technology one determines the door panel data and door handle data around the robot through point cloud data, so as to calculate the door opening motion trajectory and door opening grasping mode. This method measures and calculates the motion size of the door according to visual information in advance. When there is a deviation between the measurement result and the true value, the robot will generate a larger deviation force at the work gripping point, affecting the work, which may lead to the termination or failure of the work task. Existing technology two identifies the target object through vision, calculates its pose information, reads the relative position of the door handle and the door opening mode stored therein, and then generates the travel route of the mechanical arm pressing the door handle, executes grasping and opens the door in the preset mode. This method completes the door opening operation by reading the stored information. The robot can only complete the known door opening task and cannot adapt to new environments.

[0004] In addition, the current humanoid robot adaptive door opening control method generally has the problem of independent operation trajectory planning and movement trajectory planning. Due to the lack of coordination and optimization of the upper body and the lower body, in the actual door opening process, it often leads to incoherent whole body movement, insufficient operation precision, and difficulty in fully exerting the overall dynamic control advantage of the humanoid robot.

[0005] Therefore, in view of the problems of inaccurate perception, insufficient environmental adaptability and poor whole body coordination of the humanoid robot in the door opening task in the prior art, it is urgent to propose a humanoid robot adaptive door opening control method that can realize real-time perception-flexible response-whole body coordination, so as to improve the ability of the humanoid robot to autonomously complete the door opening task in a complex environment. SUMMARY

[0006] The object of the present application is to provide a neural field guided humanoid robot adaptive door opening control method, which models the door body in a continuous and differentiable structure by constructing a neural signed distance function, and directly embeds the neural field into key control links such as path generation, control target function, posture guidance, and weight adjustment, to realize task-level dynamic optimization under perception driving.

[0007] To achieve the above object, the present application provides the following scheme:

[0008] A neural field guided humanoid robot adaptive door opening control method, comprising:

[0009] Obtaining point cloud information of a door opening area;

[0010] Constructing a neural field based on the point cloud information, wherein the boundary information and normal information of the door body structure output by the neural signed distance function form the neural field;

[0011] Detecting the door handle pose based on the point cloud information using a pre-trained PoseCNN algorithm;

[0012] Generating a collision-free approach path using the boundary information, normal information, and door handle pose, and using quadratic programming to control the robot to approach and grasp the door handle;

[0013] Using the normal information, applying an initial pulling force through a preset admittance control strategy, and combining modeling optimization to estimate the door shaft position;

[0014] Based on the door shaft position, constructing a structure-consistent circular arc path in combination with the boundary information and normal information;

[0015] In combination with the boundary information, using weighted quadratic programming to control the robot to perform actions according to the collision-free approach path and structure-consistent circular arc path, to complete the adaptive door opening control of the humanoid robot.

[0016] Optionally, the neural signed distance function is constructed based on a fully connected multilayer perceptron, and is:

[0017] ;

[0018] Wherein, the input is an arbitrary three-dimensional space coordinate point x; the output is the signed shortest distance d = Φ(x) from the point to the door body surface, and Φ(x) is the boundary function calculated by the neural signed distance function for the point x; is a neural signed distance function, θ is a network parameter, R represents a one-dimensional space, and R 3 represents a three-dimensional space.

[0019] Optionally, outputting the boundary information and normal information of the door structure comprises:

[0020] The neural signed distance function calculates any three-dimensional space coordinate point x to obtain the boundary function Φ(x), if Φ(x) = 0, it indicates that the point is located on the door surface; if Φ(x) < 0, it indicates that the point is located inside the door; if Φ(x) > 0, it indicates that the point is located outside the door; and the door surface normal direction at the point x, i.e. the structure gradient .

[0021] Optionally, the boundary information, normal information and door handle pose are used to generate a collision-free approach path by a path planning objective function under the constraint of structure consistency, wherein the constraint of structure consistency is:

[0022] Avoiding crossing the area of Φ(x) < 0; keeping the end pose and relatively constant or slowly changing in the feasible space;

[0023] The path planning objective function is:

[0024] ;

[0025] Wherein, is the path planning loss, is the boundary function calculated by the neural signed distance function for the point , is the Cartesian space position of the robot end at time step t; is the expected motion direction of the current position in path planning; δ is the minimum safety distance of the structure boundary; λ and α are proportional factors, is the structure gradient at the point .

[0026] Optionally, the robot is controlled to approach and hold the door handle by using the quadratic programming comprises:

[0027] Converting the robot control problem into a quadratic programming optimization problem, and dynamically adjusting the control strategy according to the real-time contact state between the robot end and the door handle in the process that the robot holds the door handle, wherein the optimization objective of the quadratic programming optimization problem is:

[0028] ;

[0029] Wherein, is the joint velocity vector, is the Jacobian matrix of the end effector, is the speed of the expected trajectory;

[0030] The conditional constraint of the quadratic programming optimization problem is:

[0031] ;

[0032] wherein q is a joint position variable, and represent the lower and upper limits of the joint position, respectively, and represent the lower and upper limits of the joint velocity, respectively.

[0033] Optionally, the admittance control strategy is implemented through a position control inner loop and an admittance control outer loop, comprising:

[0034] When receiving an input displacement instruction, an expected displacement is formed by comparing the input displacement instruction with an actual feedback displacement; in the position control inner loop, an expected position and pose of the robot end are converted into motion parameters of each joint, a control signal is generated based on the motion parameters to drive the robot motion, and the actual position of the robot end is calculated in real time according to the actual motion of each joint of the robot, compared with the expected displacement, and closed-loop control of the position is realized;

[0035] In the admittance control outer loop, when the robot is subjected to a load force during motion, a preset admittance control model adjusts the motion of the robot according to the size and direction of the force; in the admittance control model , represents a virtual mass; represents a viscous damping coefficient; is a virtual stiffness; is a complex variable in Laplace transform.

[0036] Optionally, the optimization objective function for estimating the door shaft position in combination with modeling optimization is:

[0037] ;

[0038] ;

[0039] ;

[0040] wherein, is a trajectory circular arc consistency error, is a structural normal consistency error, is a structural consistency weight adjustment factor, r is a fitting radius, and c is a door shaft center, is a Cartesian space position of the robot end at time step t, is a point structure gradient.

[0041] Optionally, constructing the structural consistency circular arc path comprises:

[0042] constructing a circular arc path based on the door shaft position;

[0043] introducing a boundary function Φ(x) to perform validity verification on the circular arc path, and adopting a structure gradient guiding the direction of the circular arc path, and optimizing the discretized circular arc path to obtain the structure-consistent circular arc path.

[0044] Optionally, the objective function of the weighted quadratic programming control is:

[0045] ;

[0046] wherein, and is a structure-aware term weight hyperparameter; is a boundary consistency loss; is a gradient guidance loss; , are Jacobian matrices of the upper body and the lower body of the robot respectively; , are expected speeds of the upper body and the lower body respectively; , are control weights of the upper body and the lower body respectively, is a joint speed vector.

[0047] Optionally, the upper body and the lower body adopt a dynamic adjustment mechanism when the action is performed, and the dynamic adjustment mechanism dynamically adjusts the upper body and the lower body according to the consistency of the force direction of the end of the robot and the normal direction of the door structure, so as to achieve the balance and stability of the robot.

[0048] The present application has the following advantages:

[0049] (1) The present application improves the perception and understanding ability of the door structure by using neural field modeling: the present application introduces N-SDF to construct a continuous geometric representation model of the door body, which can complete the modeling and identification of key structures such as door surface, door shaft and door handle without relying on CAD model or explicit template. Compared with the traditional point cloud + geometric fitting method, this modeling method has higher expression accuracy and differentiability, so that the robot can still achieve high robustness in perception and interaction when facing different sizes, opening and closing modes or local occlusion of the door structure.

[0050] (2) The present invention enables structure-guided path planning and task execution: The neural field model established by the present invention is not only used for door recognition, but also guides the trajectory generation and interaction direction optimization of the robot terminal through its output structural boundary function and gradient information. During the path planning process, the robot can automatically avoid obstacles and calibrate the trajectory direction according to the door structure, and generate an arc path that conforms to the door opening and closing rules, realizing adaptive operation of complex door types (such as inward-opening doors, outward-sliding doors, revolving doors, etc.).

[0051] (3) The present invention introduces a structural perception mechanism into control optimization to improve operational stability and coordination: Based on the WQP control framework, the present invention introduces a neural field boundary consistency term and a gradient direction guidance term into the objective function, allowing the controller to fully consider the door structure constraints when executing actions. In addition, the upper and lower body control weights are dynamically adjusted through the structure-force coupling index, achieving a dynamic trade-off between operational accuracy and body stability, significantly enhancing the autonomous control performance and fault tolerance of the humanoid robot during complex operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a flow chart of a neural field-guided humanoid robot adaptive door opening control method according to an embodiment of the present invention;

[0054] Figure 2 This is a control block diagram of a neural field-guided humanoid robot adaptive door opening control method according to an embodiment of the present invention;

[0055] Figure 3 A flowchart for constructing an N-SDF model according to an embodiment of the present invention;

[0056] Figure 4 This is a block diagram of a preset admittance control strategy for a robot performing preliminary door opening according to an embodiment of the present invention;

[0057] Figure 5 A schematic diagram of a planning path determination process according to an embodiment of the present invention;

[0058] Figure 6 Schematic diagram of the neural field guidance mechanism according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0060] In order to make the above objectives, characteristics and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment provides a neural field guided humanoid robot adaptive door opening control method, as shown in the figure, which comprises the following steps: Figure 1

[0062] Obtain point cloud information of a door opening area;

[0063] Construct a neural field based on the point cloud information, wherein the boundary information and normal information of a door body structure output by a neural symbolic distance function form the neural field;

[0064] Detect a door handle pose by using a pre-trained PoseCNN algorithm based on the point cloud information;

[0065] Generate a collision-free approach path by using the boundary information, normal information and door handle pose, and control the robot to approach and hold the door handle by using quadratic programming;

[0066] Apply an initial pulling force by using a preset admittance control strategy by using the normal information, and estimate a door shaft position in combination with modeling optimization;

[0067] Construct a structure-consistent circular arc path based on the door shaft position in combination with the boundary information and normal information;

[0068] Control the robot to perform actions according to the collision-free approach path and structure-consistent circular arc path by using weighted quadratic programming in combination with the boundary information, so as to complete adaptive door opening control of the humanoid robot.

[0069] Specifically, as shown in the figure, Figure 1 , Figure 2 The neural field guided humanoid robot adaptive door opening control method provided by the embodiment comprises the following steps:

[0070] ​Step 101, perception and structure modeling: using a robot-mounted RGB-D camera to obtain point cloud information of the door opening area, constructing a neural signed distance function (N-SDF) based on deep learning, i.e., N-SDF model, to realize continuous implicit modeling of key structures such as door handle and door plane. The model outputs the door body boundary function Φ(x) and its structure gradient , as the basis for subsequent path planning and control.

[0071] As shown in Figure 3 , the construction process of the N-SDF model mainly includes the following steps:

[0072] (1) Point cloud collection and preprocessing: using a robot head or upper body mounted RGB-D camera to collect dense point cloud data of the door opening area , where is the i-th original point coordinate, N is the number of original point clouds, including door surface, door handle, door frame and other structures, and filtering and noise reduction operations are performed on the original point cloud.

[0073] (2) Spatial sampling to construct neural field input points: constructing a 3D sampling boundary box around the point cloud coverage area, and adaptively sampling a large number of spatial point cloud data as input query points for the N-SDF network, where is the j-th spatial point coordinate, and M is the number of spatial point clouds.

[0074] (3) Constructing N-SDF model: using a fully connected multilayer perceptron to define the neural signed distance function:

[0075] ;

[0076] where the input is an arbitrary three-dimensional coordinate point x; the output is the signed shortest distance d=Φ(x) from the point to the door body surface, Φ(x) is the boundary function calculated by the neural signed distance function for the coordinate point x; is the neural signed distance function, θ is the network parameter, R represents one-dimensional space, and R 3 represents three-dimensional space.

[0077] (4) N-SDF model training: for each spatial point , the nearest neighbor from the point cloud can be constructed to represent the supervised target point relative to the door body "inside / outside", and "how far away":

[0078] ;

[0079] where, For the point Estimate the normal.

[0080] The training loss function is:

[0081] ;

[0082] (5) Model output: Obtain the structure boundary and normal information: After the model training or deployment is completed, The SDF value of any point in space: If Φ(x)=0, it means that the point is located on the door surface; if Φ(x)<0, it means that the point is inside the door body (illegal area); if Φ(x)>0, it means that the point is outside the door body (passable area). Structure gradient Indicates the door surface normal direction at point x.

[0083] (6) Output interface integration to downstream links: Function Φ(x) and structure gradient As an interface output: The path planner uses Φ(x) to determine whether the trajectory enters the illegal area; the WQP controller uses Φ(x) as a boundary constraint term to guide the structure consistency; the admittance controller uses Adjust the end-effector force response direction to maintain natural interaction; the weight adjuster uses Calculate the force-structure consistency index.

[0084] (7) Door handle pose detection: Use the PoseCNN algorithm to detect the 6D space pose of the door handle. PoseCNN trains a deep learning model to extract key features from point cloud data and predicts the pose of the object based on these features.

[0085] Step 102, approach and grasp: Based on the door handle pose and door surface normal information output by N-SDF, generate a collision-free approach path. Use the quadratic programming (QP) method to convert robot control into an optimization problem, realize the mapping and tracking of Cartesian space path to robot joint space, control the robot end-effector to approach and grasp the door handle. Specifically includes:

[0086] (1) Path planning: Based on the pose of the door handle in step 101 and the N-SDF output door body structure function Φ(x) and door surface normal, i.e. structure gradient , generate a collision-free trajectory under the constraint of structure consistency;

[0087] The structure consistency constraint condition is: the path should approach along the outer normal direction of the door surface; avoid crossing the area where Φ(x)<0 (i.e. inside the door body); keep the end-effector pose and relatively unchanged or slowly changing in the feasible space.

[0088] Path planning objective function (the first term keeps the end outside the door body, the second term guides the consistency of the direction, and the minimum objective function obtains the planning path):

[0089] ;

[0090] wherein, is the path planning loss, is the boundary function calculated by the neural symbolic distance function on the point , is the Cartesian space position of the robot end at time step t; is the expected motion direction of the current position in path planning; δ is the minimum safety distance of the structure boundary; λ and α are proportional factors, is the structure gradient at point .

[0091] (2) QP control: assuming that the expected trajectory of the robot end effector is , and the actual trajectory is , the trajectory error is defined as:

[0092] ;

[0093] In order to make the end effector track the expected trajectory, the two-norm of the trajectory error needs to be minimized, and the robot control problem is converted into a QP optimization problem, and the optimization objective is:

[0094] ;

[0095] wherein, is the joint velocity vector, is the Jacobian matrix of the end effector, is the velocity of the expected trajectory (obtained according to the planning trajectory).

[0096] The conditional constraint is:

[0097] ;

[0098] wherein, q is the joint position variable, and represent the lower limit and upper limit of the joint position respectively, and represent the lower limit and upper limit of the joint velocity respectively.

[0099] In the process of grasping the door handle, the control strategy is dynamically adjusted according to the real-time contact state of the robot end effector and the door handle, so as to prevent unstable grasping or damage to the door handle.

[0100] Step 103, preliminary door opening and door shaft estimation: based on the preset admittance control strategy, an initial pulling force is applied in the normal direction of the door plane, and the motion trajectory is adjusted through the feedback of the end six-dimensional force sensor to ensure smooth and effective force control process. At the same time, combined with the robot end trajectory and the door body geometric information output by the neural field, the door shaft position estimation is carried out by adopting the structural consistency optimization method. Specifically, it includes:

[0101] (1) Preliminary door opening admittance control strategy: to ensure the dynamic stability and smooth response in the process of interacting with the door body, the end admittance control model is adopted in this embodiment to realize soft force application.

[0102] As shown in Figure 4 , the flow of the preset admittance control strategy for the robot to preliminarily pull the door is carried out around the position control inner loop and the admittance control outer loop. First, the robot receives the input displacement instruction (the initial pulling force direction in this embodiment is set to be consistent with the normal direction of the door plane output by the neural field), which forms the expected displacement after being compared with the actual feedback displacement. In the position control inner loop, the expected displacement is transmitted to the inverse kinematics module, which converts the expected position and attitude of the robot end into motion parameters of each joint. These parameters are sent to the position controller to generate specific control signals to drive the robot motion. At the same time, the forward kinematics calculates the actual position of the robot end effector in real time according to the actual motion of each joint of the robot, and feeds back to the system, which is compared with the expected displacement to realize the closed-loop control of the position, so as to ensure that the robot reaches the expected position as accurately as possible. In the admittance control outer loop, when the robot is subjected to load force in the motion process (the force information sensed by the robot end six-dimensional force sensor in this embodiment), the preset admittance control model will adjust the motion of the robot according to the size and direction of the force. This makes the robot not only accurate positioning, but also flexible to change the motion state and adjust the robot end motion trajectory in real time when interacting with the external environment.

[0103] In the admittance control model , represents the virtual mass (or equivalent inertia), which simulates the inertia characteristics of the robot in the motion process; represents the viscous damping coefficient, which embodies the damping effect of the system in the motion process due to friction and other factors; is the virtual stiffness (or equivalent stiffness), which describes the ability of the system to resist deformation; is a complex variable in Laplace transform.

[0104] (2) Neural field consistency based door axis estimation method: Traditional door axis estimation methods mostly use pure geometric fitting (such as RANSAC for hand trajectory circle fitting), but this method is easily disturbed by point noise and path deviation, and has poor robustness. To solve this problem, the embodiment introduces a structure consistency optimization strategy guided by neural field perception for door axis estimation.

[0105] Define the sequence of robot end grip point trajectories in the preliminary door opening process as , and the door axis center as an unknown variable , which is expected to satisfy the following two types of consistency constraints:

[0106] 1) Trajectory arc consistency: assuming that the end trajectory is a circular arc rotating around the door axis, minimize the consistency error of the end trajectory and concentric circles:

[0107] ;

[0108] Where r is the fitting radius.

[0109] 2) Structural normal consistency: assuming that the normal of the rotation plane should be perpendicular to the end motion direction, add the normal consistency constraint consistent with the neural field:

[0110] ;

[0111] Ensure that the direction of the connecting line between the door axis center and the trajectory point is orthogonal to the door surface direction.

[0112] In summary, the comprehensive optimization objective function is:

[0113] ;

[0114] Where is the structural consistency weight adjustment factor, and the comprehensive optimization objective function is solved by least squares method.

[0115] Step 104, path planning: based on the estimated door axis, combined with the door surface normal and boundary constraints in the neural field, a structure consistency circular arc path is constructed. The path planning process is controlled by the Φ(x) control point sampling and direction guidance, ensuring that the motion trajectory conforms to the door structure logic and avoids border crossing. Specifically, it includes:

[0116] (1) Path generation framework: as shown in Figure 5 , assuming that the door axis estimation result is , the path should be a circular arc trajectory with c as the center and r as the radius, the trajectory starting point is the current end grip point , the end point is determined by the desired door opening angle , and the plane of the circular arc trajectory is parallel to .

[0117] (2) Path screening under the constraint of neural field: In order to ensure that the planned path does not collide with the door body, the N-SDF boundary function Φ(x) is introduced to check the path validity:

[0118] ;

[0119] wherein, is the sequence of sampling points of the circular arc path; is a small positive value representing the minimum safety distance; if a certain path point , it will be considered that the point is not feasible, triggering the path correction or obstacle avoidance strategy.

[0120] (3) Structural gradient guided path direction: The motion direction between path points should be consistent with the normal direction of the door surface structure to avoid phenomena such as transverse cutting and attitude deviation. The neural field structural gradient is used to guide the path direction, and the following direction consistency loss is constructed for the path optimization objective:

[0121] ;

[0122] (4) Discrete representation and optimization of circular arc trajectory: The path trajectory is discretized into a series of trajectory points:

[0123] ;

[0124] wherein, is the rotation matrix of rotating an angle around the door axis, is the angle sequence uniformly divided from 0 to the desired opening angle , and T is the total time. The optimization objective is as follows:

[0125] ;

[0126] wherein, the first term limits the boundary legality, the second term maintains the path direction consistency, and the final output is the planned path .

[0127] Step 105, whole body coordinated control enhanced by structure perception: After the path generation is completed, the robot needs to coordinate the upper body (mainly the arm and torso) and the lower body (mainly the legs and support surface) to complete the task. This embodiment introduces a neural field structure perception term based on the Weighted Quadratic Programming (WQP) control framework to construct a coordinated controller, ensuring the dynamic optimal coordination of trajectory accuracy, attitude consistency, and support stability. Specifically, it includes:

[0128] (1) WQP control framework foundation: The goal is to solve the joint velocity command , so that the robot end (hand) follows the desired path as much as possible while maintaining body coordination and balance, in the form of:

[0129] ;

[0130] in, 、 are the Jacobian matrices of the upper and lower bodies of the robot respectively; 、 are the desired velocities of the upper and lower body, respectively; 、 are the control weights of the upper and lower body respectively, satisfying: , .

[0131] (2) Structural Perception WQP Control: Adding structural perception to WQP control and building a coordinated controller enables the robot to respond to the door geometry in real time during path execution, ensuring the physical legitimacy of the end motion, directional consistency, and dynamic coordination of the whole-body control. Structural perception mainly includes:

[0132] 1) Boundary consistency loss term:

[0133] ;

[0134] in, A safety buffer is ensured to avoid collisions between the path and the door. In essence, this item forms a soft constraint on the end position during optimization, controlling the end path point to always be outside the boundary of the door structure defined by the neural field.

[0135] 2) Gradient guided loss term:

[0136] ;

[0137] This item is used to control the end motion direction and the normal direction of the facade structure Keeping it consistent, the parameter 𝛼 controls the speed scale of the motion.

[0138] 3) Dynamic Weight Adjustment Mechanism: To achieve a balance between task flexibility and support stability, a mechanism is introduced to adjust the upper and lower body control weights based on neural fields and force feedback:

[0139] ;

[0140] in, Indicates the current ZMP (zero moment point) center of gravity offset; The six-dimensional force sensor data of the terminal; Ensure that the force direction is consistent with the normal direction of the facade; and is the corresponding weight; Indicates that the Sigmoid function is used to compress the weight range.

[0141] The above-mentioned dynamic weight adjustment mechanism can realize the automatic and real-time adjustment of the robot's upper and lower body control weights according to the consistency relationship between the door structure direction and the end interaction force, combined with the ZMP center of gravity offset, so as to adapt to the different requirements of stability and dexterity in different task stages. Increased, lower limb support is dominant, and the posture is stable; when the door is light or requires delicate operation Increased size allows for more dominant arm control and improved dexterity.

[0142] The final structure-aware WQP control objective function is:

[0143] ;

[0144] in, and is the structure-aware item weight hyperparameter.

[0145] like Figure 6 As shown, the neural field guidance mechanism proposed in this embodiment is reflected in the following four core aspects in the entire door opening control process:

[0146] (1) Guided path planning (structurally consistent trajectory generation);

[0147] Boundary function Φ(x) and normal gradient output by N-SDF model , is used to construct an arc path that fits the facade structure, and to perform path screening and correction:

[0148] If the path sampling point satisfy , it is located inside the door body and is considered an illegal path point, and the path will be excluded;

[0149] If the plane where the path is located is perpendicular to the facade If they are inconsistent, the trajectory is adjusted by introducing a direction consistency loss term.

[0150] (2) Interaction direction prediction in guided admittance control;

[0151] During the initial opening of the door, the admittance controller adjusts the motion response based on the feedback from the six-dimensional force sensor at the end. As the normal direction of the facade, it is introduced into the interaction modeling:

[0152] If the direction of the force applied at the end significantly deviates from , a direction compensation mechanism is triggered to effectively improve the alignment between the direction of the force applied at the end and the rotation trend of the door body, making the interaction more smooth and natural.

[0153] (3) Guidance control objective function construction (structure perception control constraint);

[0154] In the objective function of the WQP controller, this embodiment introduces two types of structure perception constraints based on neural fields:

[0155] Structure boundary soft constraint term (limit the end to cross the door body):

[0156] ;

[0157] Structure gradient guidance term (guide the trajectory direction to fit the door surface):

[0158] ;

[0159] The controller will automatically respond to the door structure field when performing path tracking and posture optimization, improving the physical consistency and environmental adaptability of trajectory execution.

[0160] (4) Dynamic adjustment mechanism for guiding upper and lower body weights;

[0161] This embodiment constructs a control weight adjustment function based on a structure-force coupling index, which is used to dynamically balance dexterity and stability:

[0162] ;

[0163] wherein, represents the current ZMP center of gravity offset; is the six-dimensional force sensor data at the end; ensures the consistency of the force direction with the normal direction of the door surface; and are the corresponding weights; represents a Sigmoid function used to compress the weight range.

[0164] used to measure the consistency of the force direction applied at the end with the normal direction of the door structure: if the consistency is low (the direction is not aligned), the lower body weight is automatically increased , enhancing the support force and the stability of the center of gravity; if the consistency is high, the lower body constraint is appropriately released, improving the flexibility and operation precision of the upper body. This mechanism allows the controller to automatically adjust the coordination strategy of the upper and lower bodies according to different door weights, path lengths, and support states, achieving optimal dynamic control of the whole body under the structure drive.

[0165] The above described embodiments are only to illustrate the preferred modes of the present application, and are not intended to limit the scope of the present application. Any modification and improvement made by those skilled in the art to the technical solutions of the present application without departing from the design spirit of the present application shall fall within the protection scope of the present application as defined by the claims.

Claims

1. A neural field guided humanoid robot adaptive door opening control method, characterized in that: include: Get the point cloud information of the door opening area; Constructing a neural field based on the point cloud information, wherein the neural field is formed by boundary information and normal information of the portal structure output by a neural signed distance function; Based on the point cloud information, the door handle posture is detected using the pre-trained PoseCNN algorithm; Using the boundary information, normal information, and the door handle pose, a collision-free approach path is generated, and quadratic programming is used to control the robot to approach and grasp the door handle; Using the normal information, an initial pulling force is applied through a preset admittance control strategy, and the door axis position is estimated in combination with modeling optimization; Based on the door axis position, combining the boundary information and normal information, a structurally consistent arc path is constructed; Combined with the boundary information, weighted quadratic programming is used to control the entire body of the robot to perform actions according to the collision-free approach path and structural consistency arc path, thereby completing the adaptive door opening control of the humanoid robot.

2. The neural field guided humanoid robot adaptive door opening control method according to claim 1, characterized in that: The neural signed distance function is constructed based on a fully connected multilayer perceptron and is: ； The input is any three-dimensional coordinate point x; the output is the signed shortest distance d = Φ(x) from the point to the door surface, where Φ(x) is the boundary function calculated for point x using the neural signed distance function. is the neural signed distance function, θ is the network parameter, R represents the one-dimensional space, R 3 Represents three-dimensional space.

3. The neural field guided humanoid robot adaptive door opening control method according to claim 2, characterized in that: Outputting the boundary information and normal information of the door structure includes: The neural signed distance function calculates any three-dimensional space coordinate point x to obtain the boundary function Φ(x). If Φ(x)=0, it means that the point is on the surface of the door body; if Φ(x)<0, it means that the point is inside the door body; if Φ(x)>0, it means that the point is outside the door body; and obtains the normal direction of the door surface at point x, that is, the structural gradient .

4. The neural field guided humanoid robot adaptive door opening control method according to claim 3, characterized in that: Using the boundary information, normal information, and door handle pose, a collision-free approach path is generated through a path planning objective function under a structural consistency constraint, wherein the structural consistency constraint is: Avoid crossing the area where Φ(x)<0; maintain the terminal posture and relatively unchanged or slowly changing; The path planning objective function is: ； in, is the path planning loss, To use the neural signed distance function to find the point The calculated boundary function, is the Cartesian space position of the robot end at time step t; is the expected movement direction of the current position in path planning; δ is the minimum safe distance of the structure boundary; λ and α are scale factors, for point Structural gradient.

5. The neural field guided humanoid robot adaptive door opening control method according to claim 1, characterized in that: Using the quadratic programming to control the robot to approach and grasp the door handle includes: The robot control problem is converted into a quadratic programming optimization problem. During the robot grasping the door handle, the control strategy is dynamically adjusted according to the real-time contact state between the robot end and the door handle. The optimization objective of the quadratic programming optimization problem is: ； in, is the joint velocity vector, is the Jacobian matrix of the end effector, is the speed of the desired trajectory; The conditional constraints of the quadratic programming optimization problem are: ； Among them, q is the joint position variable, and Represent the lower and upper limits of the joint position, and Represent the lower and upper limits of joint velocity respectively.

6. The neural field guided humanoid robot adaptive door opening control method according to claim 1, characterized in that: The admittance control strategy is implemented through an inner position control loop and an outer admittance control loop, including: When an input displacement command is received, the input displacement command is compared with the actual feedback displacement to form a desired displacement; in the position control inner loop, the desired position and posture of the robot end are converted into motion parameters of each joint, and a control signal is generated based on the motion parameters to drive the robot movement. At the same time, based on the actual motion of each joint of the robot, the actual position of the robot end is calculated in real time and compared with the desired displacement to achieve closed-loop position control; In the outer loop of admittance control, when the robot is subjected to load force during movement, the preset admittance control model The robot's motion will be adjusted according to the magnitude and direction of the force; the admittance control model middle, Indicates virtual mass; represents the viscous damping coefficient; is the virtual stiffness; is the complex variable in the Laplace transform.

7. The neural field guided humanoid robot adaptive door opening control method according to claim 1, characterized in that: The optimization objective function of the door axis position is estimated by combining modeling optimization: ； ； ； in, is the trajectory arc consistency error, is the structural normal consistency error, is the structural consistency weight adjustment factor, r is the fitting radius, c is the door axis center, is the Cartesian space position of the robot end at time step t, for point Structural gradient.

8. The neural field guided humanoid robot adaptive door opening control method according to claim 1, characterized in that: Constructing the structural consistency arc path includes: Constructing an arc path based on the door axis position; The boundary function Φ(x) is introduced to verify the validity of the arc path, and the structural gradient The arc path direction is guided, and the arc path is discretized and optimized to obtain the structurally consistent arc path.

9. The neural field guided humanoid robot adaptive door opening control method according to claim 1, characterized in that: The objective function of the weighted quadratic programming control is: ； in, and is the structure-aware item weight hyperparameter; is the boundary consistency loss; is the gradient guided loss; 、 are the Jacobian matrices of the upper and lower bodies of the robot respectively; 、 are the desired velocities of the upper and lower body, respectively; 、 are the control weights for the upper and lower body respectively, is the joint velocity vector.

10. The neural field guided humanoid robot adaptive door opening control method according to claim 8, characterized in that: When performing the above action, the upper and lower bodies adopt a dynamic adjustment mechanism, which dynamically adjusts the upper and lower bodies according to the consistency between the force direction applied by the robot end and the normal direction of the door structure to achieve balance and stability of the robot.

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