Heterogeneous robot control system with intelligent and multi-mode perceptual driving functions

Abstract

The invention relates to the technical field of robot control, and discloses a heterogeneous robot control system with intelligent and multi-modal perceptual driving, comprising: a quantum clock reference module used for generating a globally synchronized picosecond-level time reference signal and synchronizing a multi-modal sensor clock through a microwave pulse; the multi-mode sensing module comprises an event camera of which the dynamic range is greater than or equal to 120dB, a six-dimensional force sensor of which the noise density is less than or equal to 0.01 N / Hz and an inertial measurement unit of which the zero-bias stability is less than or equal to 0.8 degree / h; and the random differential manifold module adopts a four-layer full-connection neural network, and an input layer receives multi-modal data. According to the invention, a diamond NV color center system of the quantum clock reference module and a microwave pulse coupling technical scheme are adopted, so that a picosecond-level global signal synchronization effect is achieved. Compared with a scheme depending on a traditional crystal oscillator or a GPS clock in the prior art, the defects that electromagnetic interference is prone to occurring and signal jitter is large are overcome, and high-precision time sequence alignment of multi-module cooperation is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and specifically to a heterogeneous robot control system driven by embodied intelligence and multimodal perception. Background Art

[0002] With the wide application of robots in complex scenarios such as industrial automation and medical surgery, multimodal perception and real-time collaborative control have become the core requirements for improving system performance. Such systems usually rely on the joint data acquisition of heterogeneous sensors (such as event cameras, force sensors, IMUs) and the control of high-precision actuators, and the timing synchronization accuracy, data fusion reliability, and instruction transmission real-time performance among various modules directly determine the dynamic response ability and operation safety of the robot.

[0003] In the prior art, the clock synchronization of multimodal robot systems mostly adopts crystal oscillator clock sources or GPS timing schemes. For example, the local clock synchronization technology based on oven-controlled crystal oscillators (OCXOs) realizes microsecond-level time alignment through phase-locked loops (PLLs); the GPS synchronization scheme relies on satellite signals to provide a global time reference. In terms of data transmission, mainstream systems realize instruction distribution through ROS (Robot Operating System) and industrial bus (such as EtherCAT) protocol conversion middleware, and use traditional PID or MPC (Model Predictive Control) algorithms to generate control instructions.

[0004] However, the prior art has significant defects: crystal oscillator clocks are vulnerable to temperature drift and electromagnetic interference, and GPS signals fail in indoor or shielded environments, resulting in the accumulation of timing deviations in multi-module collaboration (typical value > 1 μs). This synchronization error will cause multimodal data fusion distortion, manifold modeling accuracy degradation, and control instruction delay, and further cause safety hazards such as robotic arm trajectory jitter and force control overshoot. How to achieve high-precision timing synchronization of the entire system in complex environments has become a key bottleneck restricting the improvement of robot performance. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a heterogeneous robot control system driven by embodied intelligence and multimodal perception, which solves the problem of global high-precision synchronization of multi-module collaboration of robots in complex environments.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A heterogeneous robot control system driven by embodied intelligence and multimodal perception, comprising:

[0007] A quantum clock reference module, used to generate a picosecond-level time reference signal for global synchronization and synchronize the clocks of multimodal sensors through microwave pulses;

[0008] The multimodal perception module includes an event camera with a dynamic range ≥ 120 dB, a six-axis force sensor with a noise density ≤ 0.01 N / √Hz, and an inertial measurement unit with a zero-bias stability ≤ 0.8° / h;

[0009] The stochastic differential manifold module uses a four-layer fully connected neural network. The input layer receives multimodal data, the number of hidden layer nodes is 256, the activation function is ReLU, and the output layer generates a metric tensor through covariance operations;

[0010] The predictive control module parallelly solves the Hamilton-Jacobi-Bellman equation on the GPU with an update frequency of 1 kHz;

[0011] The embodied control module generates joint torque signals with a bandwidth ≥ 100 Hz and an overload protection threshold of ±300 N·m;

[0012] The system middleware supports the protocol conversion between ROS2 and EtherCAT, with a delay ≤ 10 μs, and CRC-32 algorithm is used for data verification.

[0013] Preferably, the clock deviation compensation of the quantum clock reference module satisfies:

[0014] ;

[0015] Where:

[0016] is the electron gyromagnetic ratio;

[0017] is the clock deviation compensation amount, with the unit of μs;

[0018] is the time-varying magnetic field strength, with the unit of tesla;

[0019] is the NV center coherence time;

[0020] is the proportional gain;

[0021] is the derivative gain;

[0022] The module is coupled to the sensor clock circuit through a 2.87 GHz microwave resonator, and the synchronization accuracy .

[0023] Preferably, the data transmission architecture of the multimodal perception module includes:

[0024] The event camera transmits data at 8 Gbps through the MIPI CSI-3 interface, and each frame data packet header contains a 64-bit timestamp;

[0025] The six-axis force sensor distributes the original data in the range of 0x100 - 0x1FF on the CANFD bus, and the transmission interval ≤ 100 μs;

[0026] The DMA channel trigger condition of the inertial measurement unit is that the angular velocity change rate ≥ 500° / s 2 。

[0027] Preferably, the metric tensor calculation of the stochastic differential manifold module includes:

[0028] The curvature regularization weighting coefficient is 0.2 - 0.3;

[0029] The expansion manifold time curvature adjustment coefficient is 0.1 - 0.2;

[0030] The dynamic prediction time domain adjustment step size is 10 - 15 ms.

[0031] Preferably, the prediction time domain adjustment rule of the predictive control module is:

[0032] When the manifold curvature change rate > 0.05 rad / ms, shorten the time domain to 50 ms;

[0033] When the joint angular velocity < 5° / s and the manifold diffusion coefficient ≤ 0.01, extend the time domain to 200 ms.

[0034] Preferably, the torque generation of the embodied control module includes:

[0035] The inverse operation of the inertia matrix uses Cholesky decomposition, and the accuracy ≤ 0.01%;

[0036] The damping term coefficient has a negative exponential relationship with the joint velocity, and the decay constant is 0.05 s;

[0037] When the overload protection is triggered, cut off the drive power supply ≤ 100 μs.

[0038] Preferably, the protocol conversion interface of the system middleware includes:

[0039] The accuracy of the timestamp field is 0.1 μs;

[0040] The mapping table from ROS2 topics to EtherCAT PDOs is stored in the on-chip RAM of the FPGA;

[0041] The data frame format includes a manifold state field, a control instruction field, and a CRC check field.

[0042] Preferably, the dynamic performance verification indicators of the system include:

[0043] When the slope terrain adaptation angle ≥ 30°, the ZMP deviation of the legged robot ≤ 2 cm;

[0044] The recovery time under a 50N·s impact is ≤0.5s;

[0045] The average power consumption during 8 hours of continuous operation is ≤20W.

[0046] Preferably, the stability control of the stochastic differential manifold module satisfies:

[0047] ;

[0048] Where:

[0049] is the time derivative of the Lyapunov function;

[0050] is the i-th dimensional component of the manifold state vector;

[0051] is the attenuation coefficient;

[0052] is the gradient of the manifold embedding function;

[0053] is the two-norm;

[0054] The optimization process is implemented on the AI engine of the Xilinx Versal ACAP chip.

[0055] Preferably, the hardware deployment architecture of the system is:

[0056] The quantum clock module and the multi-modal perception module are connected by a star topology, and the wire length is ≤10cm;

[0057] The predictive control module adopts the NVIDIA Jetson AGX Orin platform and is connected to the manifold module through a PCIe 4.0×16 interface;

[0058] The embodied control module and the actuator form a closed loop, and the feedback delay is ≤50μs.

[0059] The present invention provides a heterogeneous robot control system driven by embodied intelligence and multi-modal perception. It has the following beneficial effects:

[0060] 1. The present invention adopts the technical solution of coupling the diamond NV color center system of the quantum clock reference module with microwave pulses, achieving a picosecond-level global signal synchronization effect. Compared with the existing solutions that rely on traditional crystal oscillators or GPS clocks, it solves the problems of susceptibility to electromagnetic interference and large signal jitter, ensuring high-precision timing alignment for multi-module collaboration.

[0061] 2. The present invention realizes zero-bias fusion of heterogeneous sensor data in a dynamic environment through the spatiotemporal data alignment and adaptive noise reduction technology of the multimodal perception module. The problem of fusion failure of multi-sensor data due to clock drift or noise interference in traditional solutions has been completely solved.

[0062] 3. The present invention is based on the nonlinear embedding and dynamic manifold optimization technology of the random differential manifold module, which greatly improves the multimodal data modeling capabilities in complex environments. The modeling distortion defects caused by linear assumptions or fixed manifold structures in the existing technology no longer exist under this solution.

[0063] 4. The present invention uses the multi-protocol real-time conversion and quantum clock driven synchronization technology solutions of the system middleware to open up the ultra-low latency command chain from perception to control. The pain points of the traditional system communication protocol fragmentation and timing confusion are solved in one fell swoop, and the real-time response of the whole system has reached a new level. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Please see attached Figure 1 , an embodiment of the present invention provides a heterogeneous robot control system driven by embodied intelligence and multimodal perception, including:

[0067] The quantum clock reference module is used as the global time reference source for the heterogeneous robot control system. The picosecond synchronization signal it generates is coupled to the sensor clock circuit of the multimodal perception module through microwave pulses, solving the signal delay and jitter problems in the traditional clock distribution network. The phase accumulation data output by this module is further used as the time domain alignment reference of the random differential manifold module to ensure the spatiotemporal consistency of multimodal data in the dynamic manifold space.

[0068] The quantum clock reference module is implemented using a diamond nitrogen-vacancy (NV) color center system.

[0069] Specifically, a DNV-B1 diamond chip produced by ElementSix was used, with a size of 3×3 mm² and a crystal plane orientation of (100).

[0070] The operating frequency of the microwave resonator is 2.87 GHz, which matches the transition frequency of the ground state energy level of the NV color center, and the quality factor Q value of the resonator is ≥5000.

[0071] The laser excitation wavelength is 532 nm, the power is 20 mW, it is coupled to the diamond surface through an optical fiber, the optical pulse width is 50 ns, and the repetition frequency is 1 kHz.

[0072] The phase accumulation process of the quantum clock is described by the spin state evolution equation:

[0073] ;

[0074] Parameter definition:

[0075] is the quantum phase accumulation amount, with the unit of radian (rad);

[0076] : Electron gyromagnetic ratio;

[0077] : Time-varying magnetic field intensity, which is compensated in real time by a barium atomic magnetometer, and the compensation bandwidth is 10 kHz;

[0078] : Decoherence rate, is the transverse relaxation time;

[0079] : Quantum noise process, obeying the statistical characteristics of the standard Wiener process.

[0080] The clock synchronization control law is realized through proportional-derivative feedback:

[0081] ;

[0082] Parameter definition:

[0083] is the quantum phase accumulation amount, with the unit of radian (rad);

[0084] is the phase of the sensor local clock, and the measurement method is a phase-locked loop circuit, with the unit and being consistent;

[0085] : The th clock deviation of the sensor;

[0086] : Proportional gain, dynamically adjusted according to the quantum noise power spectral density;

[0087] : Differential gain, used to suppress high-frequency jitter.

[0088] The correction voltage generation circuit is integrated in the Xilinx Kintex UltraScale series FPGA: The spin state measurement results are calculated in real time:

[0089] ;

[0090] Parameter definitions:

[0091] : Correction voltage;

[0092] : Phase-voltage conversion coefficient;

[0093] : Real part extraction operation;

[0094] : Spin operator In the quantum state The expected value;

[0095] : The spin quantum state of the system at time ;

[0096] : Pauli-Z spin operator.

[0097] The voltage-controlled crystal oscillator (VCXO) model is Rakon RTX-501, with a voltage control sensitivity of 50 ppm / V and an output frequency stability of ±0.1 ppm.

[0098] In some embodiments, microwave pulse modulation is implemented using an IQ mixer:

[0099] The local oscillator signal frequency is 2.87 GHz, and the phase noise is -110 dBc / Hz @ 1 kHz offset;

[0100] The modulation bandwidth is 100 MHz, and the pulse rise time ≤ 5 ns;

[0101] The microwave power amplifier has a gain of 30 dB, an output power of 1 W, and a harmonic suppression ratio ≥ 40 dBc.

[0102] Parameter configuration and performance verification:

[0103] The synchronization accuracy of the quantum clock module is verified through experiments:

[0104] Under a 10 mT magnetic field perturbation, the standard deviation of the clock deviation s;

[0105] The test results of the phase noise power spectral density: -110 dBc / Hz @ 1 kHz offset, -130 dBc / Hz @ 10 kHz offset.

[0106] In some embodiments, the coupling efficiency of the microwave resonator is optimized:

[0107] Use a superconducting coplanar waveguide structure with an impedance match of 50 Ω and a reflection coefficient ≤ -20 dB;

[0108] The distance between the diamond chip and the waveguide is 50 μm, and the deviation of the microwave field uniformity is ≤ 5%.

[0109] In some embodiments, decoherence suppression is achieved through a dynamic decoupling sequence:

[0110] Adopt the XY-8 pulse sequence with a pulse interval of 200 ns;

[0111] After decoupling, the coherence time is extended to , suitable for high-noise environments.

[0112] The multi-modal perception module and the quantum clock reference module are hardware-level synchronized through microwave pulse signals to ensure the spatio-temporal consistency of visual, tactile, and inertial data. The raw data collected by this module is input into the stochastic micro-differential manifold module for non-linear correlation modeling after noise reduction and coordinate transformation, providing a high-confidence environmental state representation for the predictive control module.

[0113] The multi-modal perception module consists of an event camera, a six-axis force sensor, and an inertial measurement unit. Each sensor receives a synchronization signal through a dedicated interface protocol:

[0114] The event camera uses a global shutter CMIS image sensor with a pixel size of 3.45 μm × 3.45 μm and a full well capacity of 12,000 e − , and the peak quantum efficiency is 85%;

[0115] The six-axis force sensor adopts a silicon strain gauge full-bridge structure with a sensitivity coefficient of 2.0 mV / V, a natural frequency ≥ 2 kHz, and a transverse interference error ≤ 0.5% F.S.;

[0116] The inertial measurement unit integrates a three-axis MEMS gyroscope and an accelerometer, with an angular velocity random walk coefficient of 0.003 ° / √h and an accelerometer non-linearity of 0.02% F.S.

[0117] The data transmission of the event camera is achieved through the MIPI CSI-3 interface, and the protocol configuration is as follows:

[0118] Number of channels: 4 data channels (DataLane), with a rate of 2 Gbps per channel;

[0119] Packet format: 64-bit header (48-bit timestamp + 8-bit coordinates X / Y each) + 512-bit payload (event polarity + light intensity gradient);

[0120] Trigger mode: Triggered by the rising edge of the quantum clock synchronization pulse, with a trigger delay jitter ≤ 5 ns.

[0121] In some embodiments, the data of the six-axis force sensor is transmitted through the CANFD bus, and the bus configuration parameters include:

[0122] Bit rate: 5 Mbps (arbitration segment), 15 Mbps (data segment);

[0123] Message ID assignment: In the range of 0x100 - 0x1FF, with a data field length of 64 bytes;

[0124] Real-time guarantee: Priority preemption scheduling strategy, with a maximum transmission interval ≤ 100 μs.

[0125] In some embodiments, the DMA channel trigger condition of the inertial measurement unit (IMU) is based on the angular acceleration threshold:

[0126] Trigger threshold: Start batch transmission when the angular acceleration ≥ 500° / s²;

[0127] Data block size: 64 bytes (including three-axis angular velocity, three-axis acceleration, and temperature value).

[0128] The multi-modal perception module consists of an event camera, a six-axis force sensor, and an inertial measurement unit. Each sensor receives a synchronization signal through a dedicated interface protocol:

[0129] The event camera uses a global shutter CMIS image sensor with a pixel size of 3.45 μm × 3.45 μm and a full well capacity of 12,000 e − , and the peak quantum efficiency is 85%;

[0130] The six-axis force sensor adopts a silicon strain gauge full-bridge structure with a sensitivity coefficient of 2.0 mV / V, a natural frequency ≥ 2 kHz, and a transverse interference error ≤ 0.5% F.S.;

[0131] The inertial measurement unit integrates a three-axis MEMS gyroscope and an accelerometer, with an angular velocity random walk coefficient of 0.003° / √h and an accelerometer non-linearity of 0.02% F.S.;

[0132] In some embodiments, the data transmission of the event camera is implemented through the MIPI CSI-3 interface, and the protocol configuration is as follows:

[0133] Number of channels: 4 data channels (DataLane), with a rate of 2 Gbps for each channel;

[0134] Packet format: 64-bit header (48-bit timestamp + 8 bits each for coordinates X / Y) + 512-bit payload (event polarity + light intensity gradient);

[0135] Trigger mode: Triggered by the rising edge of the quantum clock synchronization pulse, with a trigger delay jitter ≤ 5 ns.

[0136] In some embodiments, the data of the six-axis force sensor is transmitted through the CANFD bus, and the bus configuration parameters include:

[0137] Bit rate: 5 Mbps (arbitration segment), 15 Mbps (data segment);

[0138] Message ID assignment: In the range of 0x100 - 0x1FF, and the data field length is 64 bytes;

[0139] Real-time guarantee: Priority preemption scheduling strategy, with a maximum transmission interval ≤ 100 μs.

[0140] In some embodiments, the DMA channel trigger condition of the inertial measurement unit (IMU) is based on the angular acceleration threshold:

[0141] Trigger threshold: Batch transmission is started when the angular acceleration ≥ 500° / s²;

[0142] Data block size: 64 bytes (including three-axis angular velocity, three-axis acceleration, and temperature value).

[0143] Preprocessing process: Affine transformation matrix is used for installation error compensation;

[0144] Data synchronization and spatio-temporal alignment In some embodiments, the time alignment of multi-modal data is achieved through the following mechanism:

[0145] Timestamp generation: The internal counter of each sensor is based on the quantum clock signal, and the counting resolution is 0.1 μs;

[0146] Delay compensation: The optical fiber transmission delay is compensated through ranging feedback, and the compensation formula:

[0147] ;

[0148] Parameter definition:

[0149] is the time of the compensated clock signal, unit: s, used for spatio-temporal alignment of multi-modal data;

[0150] is the transmission time of the original clock signal, unit: s, directly measured by the TDC (time-to-digital converter) of the quantum clock module;

[0151] L: Optical fiber length (unit: m);

[0152] c: Speed of light (299,792,458 m / s);

[0153] : Effective refractive index of the optical fiber (1.467).

[0154] In some embodiments, the spatial coordinate alignment is achieved through extrinsic calibration:

[0155] Calibration target: checkerboard target (square size 10mm×10mm), installed at the end effector of the robot;

[0156] Calibration algorithm: PnP (Perspective-n-Point) optimization, reprojection error ≤ 0.1 pixel;

[0157] Coordinate system transformation: The update frequency of the homogeneous transformation matrix from the sensor coordinate system to the robot base coordinate system is 10Hz.

[0158] Data denoising and preprocessing In some embodiments, the noise suppression of the event camera adopts spatio-temporal joint filtering:

[0159] Spatial filtering: 3×3 median filter to filter out isolated noise events;

[0160] Temporal filtering: The event survival time window is set to 200μs, and timeout events are automatically discarded;

[0161] Light intensity integration model:

[0162] ;

[0163] Parameter definition:

[0164] I is defined as the instantaneous light intensity, unit lux·ms;

[0165] : The light intensity integration of pixel (x,y) within the window (unit: lux·ms);

[0166] : Event trigger threshold.

[0167] In some embodiments, the temperature drift compensation of the six-axis force sensor is achieved through piecewise polynomial fitting:

[0168] Temperature sampling: PT1000 platinum resistance, sampling rate 1Hz, accuracy ±0.1℃;

[0169] Compensation model:

[0170] ;

[0171] Parameter definition:

[0172] is the compensated force value, unit Newton (N);

[0173] : Original force measurement value (unit: N);

[0174] : Sensor temperature (unit: °C);

[0175] , , .

[0176] IMU data fusion adopts Extended Kalman Filter (EKF):

[0177] State vector: (Position, velocity, attitude quaternion);

[0178] Process noise covariance:

[0179]

[0180] Parameter definition: Position noise standard deviation 0.01 m, velocity noise standard deviation 0.1 m / s, attitude angle noise standard deviation 0.001 rad;

[0181] Observation noise covariance:

[0182] ;

[0183] Parameter definition: Acceleration observation noise standard deviation 0.005 m / s², angular velocity observation noise standard deviation 0.002 rad / s.

[0184] The stochastic differential manifold module receives the spatio-temporal aligned data preprocessed by the multi-modal perception module, and maps the heterogeneous sensor data to a unified geometric space through non-linear embedding and dynamic manifold optimization. The manifold state generated by this module is used as the input of the predictive control module to drive the energy optimal control strategy, and at the same time cooperate with the synchronization signal of the quantum clock reference module to ensure the consistency of the time evolution and the physical world.

[0185] The core architecture of the stochastic differential manifold module is based on a four-layer fully connected neural network to realize multi-modal data embedding:

[0186] The input layer dimension is 256, which receives the light intensity integral value of the event camera, the compensated force / torque vector of the six-axis force sensor, and the angular velocity / acceleration data of the IMU;

[0187] The number of hidden layer nodes is 256, the activation function is ReLU, the weight initialization adopts He normal distribution, and the initial bias is set to 0.01;

[0188] The output layer dimension is 128, generating manifold coordinates , representing the projection of multi-modal data in the manifold space.

[0189] The training data is sourced from the robot dynamic motion trajectory dataset, with a batch size of 64, a learning rate of 0.001, the AdamW optimizer is used, and the loss function is the weighted sum of the mean square error and the curvature regularization term.

[0190] The calculation of the manifold metric tensor fuses covariance statistics and geometric curvature constraints:

[0191] ;

[0192] Parameter definitions:

[0193] is the component of the manifold metric tensor, dimensionless;

[0194] is the dimension coordinate of the manifold coordinate system, generated by the autoencoder;

[0195] : the output of the embedding function for the k-th sample, calculated by the forward propagation of the neural network;

[0196] : the covariance calculation window length, corresponding to a time window of 10 ms (sampling rate 10 kHz);

[0197] : the curvature regularization weighting coefficient, determined by grid search optimization;

[0198] : the Ricci curvature estimate value, calculated based on the kernel density estimation method, where:

[0199] : the kernel function bandwidth;

[0200] : the number of nearest neighbors, used for local curvature estimation.

[0201] The construction of the extended manifold is used for time-domain predictive control:

[0202] Space-time expansion: Embed the time dimension into the manifold space to construct the extended manifold , the time curvature adjustment coefficient ;

[0203] Predictive time-domain dynamic adjustment rules:

[0204] When the manifold curvature change rate is, shorten the predictive time domain to ;

[0205] When the joint angular velocity and the manifold diffusion coefficient When, extend the prediction time domain to .

[0206] In some embodiments, the adaptive update mechanism of the manifold diffusion coefficient is as follows:

[0207] ;

[0208] Parameter definition:

[0209] : Update learning rate, which determines the weight decay rate;

[0210] : Gradient of the embedding function with respect to the manifold coordinates, calculated by automatic differentiation;

[0211] Initial value , with an update period of 10 ms to ensure the dynamic stability of the manifold space.

[0212] In some embodiments of dynamic optimization and stability control, the manifold state optimization adopts the Lyapunov adaptive control strategy:

[0213] ;

[0214] Parameter definition:

[0215] : Manifold state tracking error;

[0216] : Exponential convergence rate, set by the pole placement method;

[0217] Controller gain matrix , and the diagonal elements correspond to the control strengths of different manifold dimensions.

[0218] The online learning strategy includes incremental training and data replay:

[0219] Trigger condition: When the manifold reconstruction error , start updating the network weights;

[0220] Data buffer: With a capacity of 10,000 samples, adopting the prioritized experience replay (PER) strategy to preferentially sample data in high-curvature regions;

[0221] Composite loss function:

[0222] ;

[0223] is the predicted manifold metric tensor (dimensionless), inferred and output by the neural network;

[0224] is the true manifold metric tensor (dimensionless), calculated offline by the Lie group parameterization method;

[0225] is the deformable body stiffness parameter (unit: N / m), estimated online by the tactile sensor;

[0226] is the target stiffness parameter (unit: N / m), set by the task planning module;

[0227] : network weight parameter, L2 regularization coefficient 0.01.

[0228] Hardware acceleration implementation

[0229] In some embodiments, the manifold calculation is hardware-accelerated by the Xilinx Versal ACAP chip:

[0230] AI Engine array: configured with 8 computing units, each unit contains 4 floating-point multiply-accumulators (FMACs), clock frequency 1.25 GHz;

[0231] Data flow architecture: input data is transmitted to the AI Engine through the programmable logic (PL) side DMA channel, throughput 512 Gbps;

[0232] Real-time metrics: single manifold state update latency ≤ 500 μs, power consumption ≤ 5 W, supporting real-time control requirements.

[0233] In some embodiments, the covariance matrix calculation is optimized by the systolic array:

[0234] Computing unit: 32×32 floating-point multiply-accumulate unit array, supporting parallel calculation of 100 groups of covariance;

[0235] Storage allocation: input buffer 64 KB (stores data), output buffer 128 KB (stores results);

[0236] Computing cycle: covariance window When, the total time consumption is 10 μs, meeting the real-time requirements.

[0237] The data interface between the stochastic differential manifold module and the multi-modal perception module includes:

[0238] Event camera: light intensity integral data As the input layer feature, it participates in the generation of manifold coordinates;

[0239] Six-axis force sensor: compensated force value Participates in the covariance calculation and affects the local geometric properties of the metric tensor;

[0240] IMU: angular velocity data Used to adjust the manifold time curvature and dynamically adjust the prediction time domain .

[0241] The synchronization signal (deviation ≤ 0.3μs) provided by the quantum clock reference module ensures the manifold state Strictly aligned with physical time. The time dimension of the dilation manifold is extended Linked with the time domain adjustment rules of the predictive control module, for example When it is shortened to 50ms, the predictive control module switches to fast response mode to optimize the control instructions under high-frequency disturbances.

[0242] The HJB equations of the predictive control module are constructed based on the manifold state evolution model:

[0243] ;

[0244] Parameter definition:

[0245] : manifold state tracking error;

[0246] : By manifold metric tensor The norm of the definition;

[0247] : Energy weight coefficient, balancing trajectory tracking accuracy and energy consumption;

[0248] : Dynamically adjusted prediction time domain.

[0249] In some embodiments, the numerical solution of the HJB equation is discretized using the spectral method:

[0250] Basis function selection: Chebyshev polynomial basis, order , truncation error ;

[0251] Parallel computing architecture: NVIDIA A100 GPU, 5120 CUDA cores enabled, thread block size 256×1;

[0252] Single step solution time , meeting the 1kHz control instruction update frequency.

[0253] In some embodiments, the prediction time domain The dynamic adjustment rules are:

[0254] Shortening condition: When the manifold curvature changes at a rate or joint angular acceleration When ;

[0255] Extended condition: When the joint angular velocity and the manifold diffusion coefficient at this time, set ;

[0256] Transition region: In other cases, Smooth switching according to the exponential decay model, time constant .

[0257] In some embodiments, the optimal control instruction generation formula is:

[0258] ;

[0259] Parameter definition:

[0260] To clarify that it is the optimal control vector, unit N·m (Newton·meter);

[0261] : Value function, obtained by solving the HJB equation;

[0262] : Manifold state drift term, modeled by a stochastic differential equation;

[0263] : Energy weight coefficient consistent with the HJB equation.

[0264] Real-time optimization and stability guarantee In some embodiments, the real-time correction strategy of the control instruction includes:

[0265] Disturbance observer: Based on the manifold state residual Estimate the external disturbance, compensation formula:

[0266] ;

[0267] Parameter definition:

[0268] is the compensated control instruction, unit N·m;

[0269] is the manifold state residual, unit meter (m);

[0270] Unit N·m·s / m, consistent with the dimension of the damping coefficient;

[0271] Unit N·m / m, reflecting the force / displacement ratio;

[0272] is the time differential element (unit: s), representing an infinitesimal time interval;

[0273] , : proportional - derivative gain, determined by Lyapunov stability analysis;

[0274] Saturation processing: Limit the amplitude of the control command to ±300 N·m, and trigger gradient truncation during overload.

[0275] The online adaptive mechanism of the control parameters is as follows:

[0276] Energy weight coefficient Dynamically adjusted according to joint load:

[0277] ;

[0278] Parameter definition:

[0279] , ;

[0280] : Real - time joint torque feedback value. Hardware acceleration and interface design;

[0281] The solution of the HJB equation is hardware - accelerated on NVIDIA A100 GPU:

[0282] Computing resource configuration:

[0283] The number of streaming multiprocessors (SM) is 108, the base clock frequency is 1.41 GHz, and the memory bandwidth is 1.6 TB / s;

[0284] Use TensorCore to accelerate matrix operations, and the computing efficiency is increased by 4.3 times;

[0285] Data transfer interface:

[0286] The manifold state data is transmitted through the PCIe 4.0×16 bus, with a delay ≤50 μs;

[0287] The control command output is broadcast through EtherCAT frames, with a period of 1 ms and a jitter ≤10 ns.

[0288] Measures to ensure real - time performance include:

[0289] Priority scheduling: Bind the control thread to the exclusive computing unit of the GPU, with a preemption period of 1 μs;

[0290] Watchdog timer: The timeout threshold is 2 ms, and switch to the backup PID controller in case of an exception.

[0291] The data interaction between the predictive control module and the stochastic differential manifold module includes:

[0292] Input: manifold state and its metric tensor , update frequency 1 kHz;

[0293] Output: optimal control command , transmitted to the embodied control module through the system middleware.

[0294] The synchronization signal provided by the quantum clock reference module ensures that the timestamps of the control commands are strictly aligned with the sensor data, with a deviation

[0295] dynamically adjusted prediction horizon linked to the curvature change rate and diffusion coefficient of the manifold module to achieve an environment-adaptive control strategy.

[0296] The embodied control module receives the optimal control commands generated by the predictive control module and converts the abstract control quantities into physical joint torque signals through inverse dynamics calculation and real-time feedback adjustment. This module is tightly coupled with the protocol conversion interface of the system middleware to ensure the low-latency characteristic of command transmission and forms a closed loop with the force feedback of the multi-modal perception module to ensure the high-precision dynamic response of the actuator.

[0297] The joint torque generation of the embodied control module is based on the inverse dynamics model:

[0298] ;

[0299] Parameter definition:

[0300] is the joint output torque vector (unit: N·m), acting on the joint drivers of the robotic arm;

[0301] : joint angle vector (n is the degree of freedom, typical value );

[0302] : joint space inertia matrix, pre-calculated through the CAD model;

[0303] : Coriolis force matrix, updated in real time with a frequency of 1 kHz;

[0304] : gravity compensation term, dynamically corrected based on the IMU attitude data;

[0305] : damping matrix, coefficient (time constant 0.05 s).

[0306] In some embodiments, the inverse operation of the inertia matrix is implemented through Cholesky decomposition:

[0307] Decomposition process: , where is a lower triangular matrix;

[0308] Solution accuracy: relative residual ;

[0309] Hardware acceleration: FPGA (Xilinx Kintex UltraScale) realizes parallel decomposition, and the single calculation time is .

[0310] In some embodiments, the overload protection mechanism includes a multi-level trigger strategy:

[0311] Software limiter: The joint torque command is limited to , and the truncated gradient is smoothed using the Sigmoid function;

[0312] Hardware protection: The current sensor (ACS730) monitors the motor phase current in real time. When it exceeds the limit, the MOSFET gate turn-off time is ;

[0313] Thermal management: When the IGBT junction temperature is , the operation is derated, and the radiator air volume is increased to 15 CFM.

[0314] Real-time feedback regulation:

[0315] The joint torque closed-loop control adopts a proportional-derivative (PD) strategy:

[0316] ;

[0317] Parameter definition:

[0318] is the feedback torque vector (unit: N·m), which is superimposed on the feedforward control command;

[0319] is the desired joint angle (unit: rad), coming from the trajectory planning module;

[0320] is the actual joint angle (unit: rad), measured in real time through the encoder;

[0321] is the desired joint angular velocity (unit: rad / s), generated by the first derivative of the trajectory;

[0322] is the actual joint angular velocity (unit: rad / s), estimated through the observer;

[0323] : Proportional gain matrix;

[0324] : Differential gain matrix;

[0325] The gain coefficient is calibrated by the frequency domain sweep method, the phase margin ≥ 45°, and the amplitude margin ≥ 6 dB.

[0326] The state observer design is based on the Luenberger observer:

[0327] ;

[0328] Parameter definition:

[0329] Reflects the energy evolution characteristics of the state estimation, and describes the product of the estimated angle and the estimated angular velocity changing rate with time;

[0330] is the estimated joint angle (unit: rad);

[0331] is the estimated joint angular velocity (unit: rad / s);

[0332] : Observer gain matrix;

[0333] The estimated error convergence time ≤ 50 ms, and the angle estimation accuracy ≤ 0.01°.

[0334] Hardware interface and protocol:

[0335] The control command transmission is realized through the EtherCAT industrial bus:

[0336] Data frame format: PDO (Process Data Object) is mapped to the 0x1A01 index, and contains fields of target torque, joint angle, and angular velocity;

[0337] Real-time performance index: period 1 ms, jitter ≤ 10 ns, transmission error rate < 10^{-12};

[0338] Redundancy design: Dual EtherCAT master stations for hot standby switching, and the fault switching time ≤ 500 μs.

[0339] In some embodiments, the actuator drive circuit includes:

[0340] Power module: Infineon FS800R07A6P3, withstand voltage 1200 V, rated current 800 A;

[0341] Gate driver: TIUCC5350, propagation delay 50 ns, common-mode transient immunity ≥ 100 kV / μs;

[0342] Current sampling: Δ-Σ ADC (AD7405), resolution 16 bits, bandwidth 1 MHz, non-linearity error ±2 LSB.

[0343] The interface between the embodied control module and the predictive control module includes:

[0344] Input: Optimal control instruction , converted to EtherCAT PDO format through the system middleware, with timestamp alignment error ≤ 0.3 ;

[0345] Feedback: Actual joint angle and angular velocity sent back to the predictive control module through the ROS2 topic ( / joint_states), update frequency The six-axis force sensor data of the multi-modal perception module is used for the dynamic correction of the gravity compensation term :

[0346] Force feedback value converted to joint torque through the inverse Jacobian matrix:

[0347] ;

[0348] : The Jacobian matrix of the robotic arm, with a real-time calculation update frequency of 100 Hz.

[0349] The system middleware serves as the communication hub of the heterogeneous robot control system, responsible for realizing the real-time data conversion and transmission between the ROS2 and EtherCAT protocols. This module receives the synchronization signal from the quantum clock reference module to ensure the spatio-temporal alignment of the multi-modal perception data, manifold state, and control instructions, and seamlessly connects with the drive interface of the embodied control module to ensure the low-latency closed-loop operation of the entire system instruction chain.

[0350] The protocol conversion interface of the system middleware is based on FPGA hardware to realize the mapping between ROS2 topics and EtherCAT PDOs:

[0351] Mapping table storage: The mapping relationship between ROS2 topics (such as / manifold_state) and EtherCAT object dictionary indexes (such as 0x1A01) is stored in the on-chip BRAM of the Xilinx Kintex UltraScale FPGA (capacity 4 KB, access latency ≤ 5 ns);

[0352] Data Frame Format: 128-byte frame structure, including a timestamp (8 bytes, precision 0.1 μs), manifold state (64 bytes), control instruction (32 bytes), and CRC-32 check field (4 bytes);

[0353] Real-time Guarantee: In the priority scheduling strategy, the clock synchronization task has the highest priority (level 99), the data conversion task has the second highest priority (level 80), and the transmission delay ≤ 10 μs.

[0354] The timestamp alignment mechanism is implemented through the following steps:

[0355] 1. Clock Synchronization: The synchronization pulse sent by the quantum clock reference module triggers the reset of the internal counter of the FPGA, and the counting resolution is 0.1 μs;

[0356] 2. Transmission Compensation: The optical fiber transmission delay is compensated through ranging feedback, and the calculation formula is:

[0357] ;

[0358] Parameter Definition:

[0359] is the timestamp after compensation (unit: s), used for synchronizing control instructions;

[0360] is the original acquisition timestamp (unit: s), from the FPGA clock counter;

[0361] : Optical fiber length (unit: meter);

[0362] : Effective refractive index of the optical fiber;

[0363] : Speed of light in vacuum.

[0364] In some embodiments, CRC-32 algorithm is used for data verification:

[0365] Generating Polynomial: CRC32 = ;

[0366] Hardware Acceleration: The FPGA built-in CRC engine calculates the check code, and the single calculation time ≤ 200 ns;

[0367] Error Handling: When the verification fails, the retransmission mechanism is triggered, and the maximum number of retries is 3 times, and the timeout threshold is 50 μs.

[0368] Hardware Architecture and Interface:

[0369] In some embodiments, the FPGA logic resource allocation is as follows:

[0370] EtherCAT slave controller: Occupies 6,000 LUTs, supports DC (Distributed Clock) synchronization mode, clock jitter ≤ 20 ns;

[0371] ROS2 node interface: Adopts the Micro-ROS framework, occupies 2 ARM Cortex-R5 cores, and the memory allocation is 512 KB;

[0372] Dual-buffer structure: The input / output buffer is 64 KB each, and the ping-pong switching period is 1 μs to avoid data conflicts.

[0373] In some embodiments, the EtherCAT frame processing flow includes:

[0374] PDO mapping: The control instruction field is mapped to the object dictionary index 0x1A01, and the data length is 32 bytes;

[0375] SDO upload: The manifold status data is asynchronously uploaded through SDO (Service Data Object), and the response time ≤ 100 μs;

[0376] Distributed clock: The deviation compensation formula for the slave clock and the master clock:

[0377] ;

[0378] Parameter definition:

[0379] represents the original clock deviation;

[0380] is the master station reference timestamp (unit: s), carried by the PTP protocol message;

[0381] is the slave station local timestamp (unit: s), read at the moment when the PTP message arrives;

[0382] is a dimensionless smoothing coefficient or damping factor, used to control the smoothing degree or response speed of clock correction;

[0383] is the compensation amount.

[0384] In some embodiments, the system middleware supports multi-protocol dynamic loading:

[0385] Protocol plug-in: The EtherCAT, PROFINET, and Modbus-TCP protocol mapping tables can be dynamically reconfigured and loaded through the FPGA, and the switching time ≤ 1 ms;

[0386] Bandwidth reservation: Reserve 50% bandwidth for critical control instructions, and non-critical data (such as logs) is transmitted using the CSMA / CA competition mechanism.

[0387] The fault tolerance and safety mechanisms include:

[0388] Link redundancy: Dual EtherCAT buses in hot standby, with a fault switching time ≤ 500 μs;

[0389] Data encryption: The control instruction field is encrypted using the AES-128 algorithm, and the key is stored in the FPGA eFUSE memory;

[0390] Exception isolation: When 3 consecutive CRC errors are detected, the faulty node is isolated and the system self-check is triggered.

[0391] The interaction relationships between the system middleware and each module are as follows:

[0392] Quantum clock reference module: The synchronization pulse signal is transmitted to the FPGA through the LVDS interface to drive the global timestamp counter;

[0393] Random micro differential manifold module: The manifold state data is transmitted to the FPGA BRAM through the AXI-Stream interface, with a writing period of 1 kHz;

[0394] Embodied control module: The EtherCAT PDO data frame is sent to the drive unit through a 100 Mbps PHY chip (TIDP83867), with a transmission period of 1 ms.

[0395] The timestamp alignment error ≤ 0.1 μs to ensure the timing consistency of multi-modal perception data and the manifold state. The protocol conversion delay ≤ 10 μs to meet the real-time control requirements.

[0396] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An embodied intelligence and multi-modal perception-driven heterogeneous robot control system, characterized in that, It includes: A quantum clock reference module, which is used to generate a globally synchronized picosecond-level time reference signal and synchronize the clocks of multi-modal sensors through microwave pulses; A multi-modal sensing module, which includes an event camera with a dynamic range ≥ 120dB, a six-axis force sensor with a noise density ≤ 0.01N / √Hz, and an inertial measurement unit with a zero-bias stability ≤ 0.8° / h; A stochastic micro-differential manifold module, which uses a four-layer fully connected neural network. The input layer receives multi-modal data, the number of hidden layer nodes is 256, the activation function is ReLU, and the output layer generates a metric tensor through covariance operations; A predictive control module, which parallelly solves the Hamilton-Jacobi-Bellman equation on the GPU with an update frequency of 1kHz; An embodied control module, which generates joint torque signals with a bandwidth ≥ 100Hz and an overload protection threshold of ±300N·m; A system middleware, which supports the protocol conversion between ROS2 and EtherCAT, with a delay ≤ 10μs, and the data verification uses the CRC-32 algorithm.

2. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that, The clock deviation compensation of the quantum clock reference module satisfies: ； Where: is the electron gyromagnetic ratio; is the clock deviation compensation amount, with the unit of μs; is the time-varying magnetic field intensity, in units of Tesla; is the coherence time of the NV color center; is the proportional gain; is differential gain; The module is coupled to the sensor clock circuit through a 2.87 GHz microwave resonator, and the synchronization accuracy .

3. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that The data transmission architecture of the multi-modal sensing module includes: The event camera transmits data at 8Gbps through the MIPI CSI-3 interface, and each data packet header contains a 64-bit timestamp; The six-axis force sensor allocates the interval of 0x100 - 0x1FF on the CANFD bus to transmit raw data, and the transmission interval ≤ 100μs; The DMA channel trigger condition of the inertial measurement unit is that the angular velocity change rate ≥ 500° / s².

4. A heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that, The metric tensor calculation of the stochastic micro-differential manifold module includes: The curvature regularization weighting coefficient is 0.2 - 0.3; The expansion manifold time curvature adjustment coefficient is 0.1 - 0.2; The dynamic prediction time domain adjustment step size is 10 - 15ms.

5. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, wherein The prediction time domain adjustment rule of the predictive control module is: When the manifold curvature change rate > 0.05rad / ms, shorten the time domain to 50ms; When the joint angular velocity < 5° / s and the manifold diffusion coefficient ≤ 0.01, extend the time domain to 200ms.

6. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, wherein, The torque generation of the embodied control module includes: The inverse operation of the inertia matrix uses Cholesky decomposition, and the accuracy ≤ 0.01%; The damping term coefficient has a negative exponential relationship with the joint velocity, and the decay constant is 0.05s; When the overload protection is triggered, cut off the drive power supply ≤ 100μs.

7. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that The protocol conversion interface of the system middleware includes: The accuracy of the timestamp field is 0.1μs; The mapping table from ROS2 topics to EtherCAT PDOs is stored in the on-chip RAM of the FPGA; The data frame format includes a manifold state field, a control instruction field, and a CRC check field.

8. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, wherein, The dynamic performance verification indicators of the system include: When the ramp terrain adaptation angle ≥ 30°, the ZMP deviation of the legged robot ≤ 2cm; The recovery time under a 50N·s impact ≤ 0.5s; The average power consumption during continuous operation for 8 hours ≤ 20W.

9. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that, The stability control of the stochastic micro-differential manifold module satisfies: ； Where: is the time derivative of the Lyapunov function; is the i-th dimensional component of the manifold state vector; is the attenuation coefficient; is the gradient of the manifold embedding function; is the second norm; The optimization process is implemented on the AI engine of the Xilinx Versal ACAP chip.

10. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that, The hardware deployment architecture of the system is: The quantum clock module and the multi-modal sensing module are connected through a star topology, and the wire length ≤ 10cm; The predictive control module uses the NVIDIA Jetson AGX Orin platform and is connected to the manifold module through a PCIe 4.0 x16 interface; The embodied control module and the actuator form a closed loop with a feedback delay ≤ 50 μs.

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