A heterogeneous robot control system driven by embodied intelligence and multimodal perception

Abstract

The present application relates to the field of robot control technology and discloses a heterogeneous robot control system driven by embodied intelligence and multimodal perception, including: a quantum clock reference module for generating a globally synchronized picosecond time reference signal and synchronizing multimodal sensor clocks through microwave pulses; a multimodal perception module comprising an event camera with a dynamic range ≥ 120dB, a six-dimensional force sensor with a noise density ≤ 0.01N / √Hz, and an inertial measurement unit with a zero-bias stability ≤ 0.8° / h; a random differential manifold module using a four-layer fully connected neural network, with the input layer receiving multimodal data. The present invention uses the diamond NV color center system and microwave pulse coupling technology of the quantum clock reference module to achieve picosecond global signal synchronization. Compared with the existing solutions that rely on traditional crystal oscillators or GPS clocks, this solves the shortcomings of being susceptible to electromagnetic interference and having large signal jitter, ensuring high-precision timing alignment of multi-module collaboration.

Description

Technical Field

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

[0002] With the widespread application of robots in complex scenarios such as industrial automation and medical surgery, multimodal perception and real-time collaborative control have become core requirements for improving system performance. Such systems typically rely on the combined data acquisition of heterogeneous sensors (such as event cameras, force sensors, and IMUs) and high-precision actuator control. The timing synchronization accuracy, data fusion reliability, and real-time command transmission between modules directly determine the robot's dynamic response capability and operational safety.

[0003] Existing technologies often use crystal oscillator clock sources or GPS timing solutions for clock synchronization in multimodal robotic systems. For example, local clock synchronization based on oven-controlled crystal oscillators (OCXOs) achieves microsecond-level time alignment through phase-locked loops (PLLs); GPS synchronization relies on satellite signals to provide a global time reference. For data transmission, mainstream systems utilize protocol conversion middleware between the Robot Operating System (ROS) and industrial buses (such as EtherCAT) to distribute commands, employing traditional PID or MPC (model predictive control) algorithms to generate control commands.

[0004] However, existing technologies have significant drawbacks: crystal oscillator clocks are susceptible to temperature drift and electromagnetic interference, and GPS signals fail indoors or in shielded environments, leading to cumulative timing deviations (typically >1μs) in multi-module coordination. This synchronization error can cause distortion in multimodal data fusion, reduced manifold modeling accuracy, and control command delays, leading to safety hazards such as robot arm trajectory jitter and force control overshoot. Achieving high-precision timing synchronization across the entire system in complex environments has become a key bottleneck restricting robot performance improvements. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, 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 implemented 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 globally synchronized picosecond time reference signal and synchronize multimodal sensor clocks via microwave pulses;

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

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

[0010] Predictive control module, which solves the Hamilton-Jacobi-Bellman equations in parallel on the GPU with an update frequency of 1kHz;

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

[0012] System middleware supports ROS2 and EtherCAT protocol conversion, with a delay of ≤10μs and data verification using the CRC-32 algorithm.

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

[0014] ;

[0015] in:

[0016] is the electron gyromagnetic ratio;

[0017] is the clock deviation compensation amount, in μs;

[0018] is the time-varying magnetic field strength in Tesla;

[0019] is the NV color center coherence time;

[0020] is the proportional gain;

[0021] is the differential gain;

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

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

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

[0025] The six-dimensional force sensor allocates 0x100-0x1FF interval on the CANFD bus to transmit raw data, with a transmission interval of ≤100μs;

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

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

[0028] Curvature regularization weighting coefficient 0.2-0.3;

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

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

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

[0032] When the manifold curvature change rate is greater than 0.05rad / ms, the time domain is shortened to 50ms;

[0033] When the joint angular velocity is less than 5° / s and the manifold diffusion coefficient is less than or equal to 0.01, the time domain is extended to 200ms.

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

[0035] The inverse calculation of the inertia matrix uses Cholesky decomposition with an accuracy of ≤0.01%;

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

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

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

[0039] The timestamp field accuracy is 0.1μs;

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

[0041] The data frame format includes a manifold status 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 is ≥30°, the ZMP deviation of the legged robot is ≤2cm;

[0044] Recovery time under 50N·s impact ≤0.5s;

[0045] The average power consumption is ≤20W when working continuously for 8 hours.

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

[0047] ;

[0048] in:

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

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

[0051] is the attenuation coefficient;

[0052] is the manifold embedding function gradient;

[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 multimodal sensing module are connected via a star topology with a line length of ≤10cm;

[0057] The predictive control module uses 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 multimodal perception. It has the following beneficial effects:

[0060] 1. This invention utilizes a quantum clock reference module with a diamond NV color center system and microwave pulse coupling technology to achieve picosecond-level global signal synchronization. Compared to existing solutions that rely on traditional crystal oscillators or GPS clocks, this approach overcomes the inherent susceptibility to electromagnetic interference and high signal jitter, ensuring high-precision timing alignment across multiple modules.

[0061] 2. This invention achieves zero-bias fusion of heterogeneous sensor data in dynamic environments through spatiotemporal data alignment and adaptive noise reduction in a multimodal sensing module. This overcomes the traditional problem of multi-sensor data fusion failure due to clock drift or noise interference.

[0062] 3. This invention, based on the nonlinear embedding and dynamic manifold optimization technology of the stochastic differential manifold module, significantly enhances the ability to model multimodal data in complex environments. This solution eliminates the modeling distortion defects caused by linear assumptions or fixed manifold structures in existing technologies.

[0063] 4. This invention, through the system middleware's multi-protocol real-time conversion and quantum clock-driven synchronization technology, establishes an ultra-low-latency command chain from perception to control. This solves the pain points of traditional systems, such as fragmented communication protocols and chaotic timing, and elevates the real-time response of the entire system to a new level. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. 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 making creative efforts are within the scope of protection of the present invention.

[0066] Please see the 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 serves as the global time reference source for the heterogeneous robotic control system. Its generated picosecond synchronization signal is coupled via microwave pulses to the sensor clock circuits of the multimodal perception module, addressing the signal delay and jitter issues in traditional clock distribution networks. The phase accumulation data output by this module further serves as the time-domain alignment reference for the stochastic differential manifold module, ensuring the spatiotemporal consistency of multimodal data within 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 resonant cavity is 2.87 GHz, which matches the ground state energy level transition frequency of the NV color center, and the quality factor Q value of the resonant cavity is ≥5000.

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

[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, unit radian (rad);

[0076] : electron gyromagnetic ratio;

[0077] : Time-varying magnetic field intensity, compensated by real-time feedback from a barium atomic magnetometer, with a compensation bandwidth of 10kHz;

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

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

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

[0081] ;

[0082] Parameter definition:

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

[0084] is the sensor's local clock phase, measured using a phase-locked loop circuit, and the unit is Stay consistent;

[0085] : No. The clock bias of each sensor;

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

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

[0088] The correction voltage generation circuit is integrated into the Xilinx Kintex UltraScale series FPGA: real-time calculation of spin state measurement results:

[0089] ;

[0090] Parameter definition:

[0091] : Correction voltage;

[0092] : Phase-to-voltage conversion coefficient;

[0093] : Take the real part operation;

[0094] : Spin operator In the quantum state Expected value under

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

[0096] : Pauli-Z spin operator.

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

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

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

[0100] Modulation bandwidth 100MHz, pulse rise time ≤5ns;

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

[0102] Parameter configuration and performance verification:

[0103] The synchronization accuracy of the quantum clock module was verified by experiments:

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

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

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

[0107] Using superconducting coplanar waveguide structure, impedance matching 50Ω, reflection coefficient ≤-20dB;

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

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

[0110] An XY-8 pulse sequence was used with a pulse interval of 200 ns;

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

[0112] The multimodal perception module and the quantum clock reference module achieve hardware-level synchronization via microwave pulse signals, ensuring the spatiotemporal consistency of visual, force, and inertial data. The raw data collected by this module undergoes noise reduction and coordinate transformation before being fed into the stochastic differential manifold module for nonlinear correlation modeling, providing a high-confidence representation of the environmental state for the predictive control module.

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

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

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

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

[0117] The data transmission of the event camera is realized through the MIPICSI-3 interface. The protocol configuration is as follows:

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

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

[0120] Trigger mode: quantum clock synchronous pulse rising edge trigger, trigger delay jitter ≤ 5ns.

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

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

[0123] Message ID allocation: 0x100-0x1FF range, data field length 64 bytes;

[0124] Real-time guarantee: priority preemptive scheduling strategy, maximum transmission interval ≤ 100μs.

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

[0126] Trigger threshold: Batch transmission starts when angular acceleration ≥ 500° / s²;

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

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

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

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

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

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

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

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

[0135] Trigger mode: quantum clock synchronous pulse rising edge trigger, trigger delay jitter ≤ 5ns.

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

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

[0138] Message ID allocation: 0x100-0x1FF range, data field length 64 bytes;

[0139] Real-time guarantee: priority preemptive scheduling strategy, maximum transmission interval ≤ 100μs.

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

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

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

[0143] Preprocessing process: Installation error compensation uses affine transformation matrix;

[0144] Data Synchronization and Spatiotemporal Alignment In some embodiments, temporal alignment of multimodal data is achieved through the following mechanisms:

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

[0146] Delay compensation: Fiber transmission delay is compensated through ranging feedback. The compensation formula is:

[0147] ;

[0148] Parameter definition:

[0149] The clock signal time after compensation, unit: s, used for spatiotemporal alignment of multimodal data;

[0150] is the original clock signal transmission time, 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 optical fiber (1.467).

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

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

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

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

[0158] Data Noise Reduction and Preprocessing In some embodiments, event camera noise suppression uses spatiotemporal joint filtering:

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

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

[0161] Light intensity integral model:

[0162] ;

[0163] Parameter definition:

[0164] I To define instantaneous light intensity, the unit is lux·ms;

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

[0166] : Event trigger threshold.

[0167] In some embodiments, temperature drift compensation of the six-axis force sensor is achieved by 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 force value after compensation, in Newton (N);

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

[0174] : sensor temperature (unit: ℃);

[0175] , , .

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

[0177] State vector: (position, velocity, attitude quaternions);

[0178] Process noise covariance:

[0179]

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

[0181] Observation noise covariance:

[0182] ;

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

[0184] The stochastic differential manifold module receives the spatiotemporally aligned data preprocessed by the multimodal sensing module and maps the heterogeneous sensor data into a unified geometric space through nonlinear embedding and dynamic manifold optimization. The manifold state generated by this module serves as the input to the predictive control module, driving the energy-optimal control strategy. It also collaborates with the synchronization signal from the quantum clock reference module to ensure that the temporal evolution is consistent with the physical world.

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

[0186] The input layer has a dimension of 256 and receives the light intensity integral value of the event camera, the compensated force / torque vector of the six-dimensional 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 uses He normal distribution, and the initial bias is set to 0.01;

[0188] The output layer dimension is 128, generating manifold coordinates , characterizing the projection of multimodal data in the manifold space.

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

[0190] The computation of the manifold metric tensor combines covariance statistics with geometric curvature constraints:

[0191] ;

[0192] Parameter definition:

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

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

[0195] : The embedding function output of the kth sample, calculated by the forward propagation of the neural network;

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

[0197] : Curvature regularization weight coefficient, determined by grid search optimization;

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

[0199] : kernel function bandwidth;

[0200] : The number of neighboring points, used for local curvature estimation.

[0201] Expanded manifold construction for time-domain predictive control:

[0202] Space-time expansion: the time dimension Embedding manifold space to construct expanded manifold , time curvature adjustment coefficient ;

[0203] Dynamic adjustment rules in the prediction time domain:

[0204] When the manifold curvature changes at a rate When the prediction time domain is shortened to ;

[0205] When the joint angular velocity And the manifold diffusion coefficient When the prediction time domain is extended to .

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

[0207] ;

[0208] Parameter definition:

[0209] : Update the learning rate and determine the weight decay rate;

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

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

[0212] Dynamic Optimization and Stability Control In some embodiments, 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 pole placement method;

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

[0218] Online learning strategies include incremental training and data replay:

[0219] Trigger condition: When the manifold reconstruction error When , the network weight update is started;

[0220] Data buffer: capacity of 10,000 samples, using the Prioritized Experience Replay (PER) strategy to prioritize sampling data in high curvature areas;

[0221] Composite loss function:

[0222] ;

[0223] To predict the manifold metric tensor (dimensionless), output by neural network inference;

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

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

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

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

[0228] Hardware acceleration implementation

[0229] In some embodiments, manifold computing is hardware accelerated using Xilinx Versal ACAP chips:

[0230] AIEngine array: 8 computation units, each containing 4 floating-point multiplier-accumulators (FMACs) with a clock frequency of 1.25 GHz.

[0231] Data flow architecture: Input data is transferred to AIEngine via the programmable logic (PL) side DMA channel with a throughput of 512Gbps;

[0232] Real-time indicators: Single manifold state update delay ≤ 500μs, power consumption ≤ 5W, supporting real-time control requirements.

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

[0234] Computing unit: 32×32 floating-point multiplication and addition unit array, supporting parallel calculation of 100 sets of covariances;

[0235] Storage allocation: Input buffer 64KB (storage Data), output buffer 128KB (storage result);

[0236] Calculation period: covariance window The total time consumed is 10μs, which meets the real-time requirements.

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

[0238] Event camera: light intensity integrated data As input layer features, it participates in manifold coordinate generation;

[0239] Six-axis force sensor: force value after compensation Participate in covariance calculation and affect 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 provided by the quantum clock reference module (deviation ≤ 0.3μs) 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 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 the 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 curvature of the manifold changes at a rate or joint angular acceleration When setting ;

[0255] Extension condition: When the joint angular velocity And the manifold diffusion coefficient When setting ;

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

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

[0258] ;

[0259] Parameter definition:

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

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

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

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

[0264] In some embodiments, the real-time modification strategy for control instructions includes:

[0265] Perturbation Observer: Based on Manifold State Residual Estimate external disturbances and compensation formula:

[0266] ;

[0267] Parameter definition:

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

[0269] is the manifold state residual, in meters (m);

[0270] The unit is N·m·s / m, which is consistent with the dimension of the damping coefficient;

[0271] The unit is N·m / m, reflecting the force / displacement ratio;

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

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

[0274] Saturation processing: The control command amplitude is limited to ±300N·m, and gradient truncation is triggered when overload occurs.

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

[0276] Energy weight coefficient Dynamic adjustment according to joint load:

[0277] ;

[0278] Parameter definition:

[0279] , ;

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

[0281] HJB equation solution is hardware accelerated on NVIDIA A100 GPU:

[0282] Computing resource configuration:

[0283] 108 stream processors (SMs), base clock frequency 1.41GHz, memory bandwidth 1.6TB / s;

[0284] Use TensorCore to accelerate matrix operations, increasing computing efficiency by 4.3 times;

[0285] Data transmission interface:

[0286] Manifold state data is transmitted via the PCIe4.0×16 bus with a latency of ≤50μs;

[0287] Control command output is broadcast via EtherCAT frames with a period of 1ms and jitter ≤ 10ns.

[0288] Real-time safeguards include:

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

[0290] Watchdog timer: timeout threshold 2ms, switch to backup PID controller in case of abnormality.

[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 1kHz;

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

[0294] The synchronization signal provided by the quantum clock reference module ensures that the timestamp of the control instruction is strictly aligned with the sensor data, and the deviation

[0295] Dynamically adjusted prediction horizon It is linked with the curvature change rate and diffusion coefficient of the manifold module to realize the 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 variables into physical joint torque signals through inverse dynamics calculations and real-time feedback adjustments. This module is tightly coupled with the system middleware's protocol conversion interface to ensure low-latency command transmission. It also forms a closed loop with the force feedback from the multimodal perception module, guaranteeing high-precision dynamic response of the actuators.

[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 from CAD model;

[0303] :Coriolis force matrix, real-time calculation update frequency 1kHz;

[0304] : Gravity compensation item, dynamic correction based on IMU attitude data;

[0305] : Damping matrix, coefficients (time constant 0.05s).

[0306] In some embodiments, the inverse inertia matrix is performed by Cholesky decomposition:

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

[0308] Solution accuracy: relative residual ;

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

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

[0311] Software limit: joint torque command Restricted to , the truncated gradient is smoothed using the Sigmoid function;

[0312] Hardware protection: Current sensor (ACS730) monitors motor phase current in real time, and MOSFET gate shutoff time is exceeded when the current exceeds the limit. ;

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

[0314] Real-time feedback adjustment:

[0315] The joint torque closed-loop control adopts the 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), which comes from the trajectory planning module;

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

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

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

[0323] : proportional gain matrix;

[0324] : differential gain matrix;

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

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

[0327] ;

[0328] Parameter definition:

[0329] Reflects the energy evolution characteristics of state estimation and describes the estimated angle in the observer and estimated angular velocity The rate of change of the product of 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 estimation error convergence time is ≤50ms and the angle estimation accuracy is ≤0.01°.

[0334] Hardware interface and protocol:

[0335] Control command transmission is achieved through the EtherCAT industrial bus:

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

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

[0338] Redundant design: Dual EtherCAT master station hot standby switching, fault switching time ≤ 500μs.

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

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

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

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

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

[0344] Input: Optimal control instructions , converted to EtherCATPDO format through system middleware, timestamp alignment error ≤ 0.3 ;

[0345] Feedback: actual joint angle and angular velocity The update frequency is sent back to the predictive control module through the ROS2 topic ( / joint_states) The six-dimensional force sensor data of the multimodal perception module is used for gravity compensation Dynamic correction of:

[0346] Force feedback value Convert to joint torque via inverse Jacobian matrix:

[0347] ;

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

[0349] The system middleware serves as the communication hub for the heterogeneous robot control system, enabling real-time data conversion and transmission between ROS2 and EtherCAT protocols. This module receives synchronization signals from the quantum clock reference module, ensuring the spatiotemporal alignment of multimodal sensory data, manifold states, and control commands. It also seamlessly connects with the driver interface of the embodied control module, ensuring low-latency closed-loop operation of the entire system's command chain.

[0350] The protocol conversion interface of the system middleware implements the mapping between ROS2 topics and EtherCATPDO based on FPGA hardware:

[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 Xilinx Kintex UltraScale FPGA on-chip BRAM (capacity 4KB, access latency ≤ 5ns);

[0352] Data frame format: 128-byte frame structure, including timestamp (8 bytes, accuracy 0.1μs), manifold state (64 bytes), control instructions (32 bytes) and CRC-32 checksum field (4 bytes);

[0353] Real-time performance 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 is ≤10μs.

[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 FPGA internal counter, with a counting resolution of 0.1μs;

[0356] 2. Transmission compensation: Fiber transmission delay is compensated through ranging feedback. The calculation formula is:

[0357] ;

[0358] Parameter definition:

[0359] The timestamp after compensation (unit: s) is used for synchronization control instructions;

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

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

[0362] : effective refractive index of optical fiber;

[0363] :speed of light in vacuum.

[0364] In some embodiments, data verification uses the CRC-32 algorithm:

[0365] Generator polynomial: CRC32= ;

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

[0367] Error handling: A retransmission mechanism is triggered when verification fails, with a maximum retry count of 3 times and a timeout threshold of 50μs.

[0368] Hardware architecture and interface:

[0369] In some embodiments, FPGA logic resources are allocated as follows:

[0370] EtherCAT slave controller: occupies 6,000 LUTs, supports DC (distributed clock) synchronization mode, and clock jitter ≤ 20ns;

[0371] ROS2 node interface: uses the Micro-ROS framework, occupies two ARMCortex-R5 cores, and allocates 512KB of memory;

[0372] Double buffer structure: input / output buffer is 64KB each, ping-pong switching cycle is 1μs 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: Manifold status data is uploaded asynchronously via SDO (Service Data Object), with a response time of ≤100μs;

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

[0377] ;

[0378] Parameter definition:

[0379] To represent the original clock bias;

[0380] The reference timestamp of the master station (unit: s), carried in PTP protocol messages;

[0381] The local timestamp of the slave (unit: s), which is read when the PTP message arrives;

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

[0383] is the compensation amount.

[0384] In some embodiments, the system middleware supports dynamic loading of multiple protocols:

[0385] Protocol plug-in: EtherCAT, PROFINET, and Modbus-TCP protocol mapping tables can be loaded through FPGA dynamic reconfiguration, with a switching time of ≤1ms;

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

[0387] Fault tolerance and safety mechanisms include:

[0388] Link redundancy: dual EtherCAT bus hot standby, fault switching time ≤ 500μs;

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

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

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

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

[0393] Random differential manifold module: Manifold state data is transmitted to FPGA BRAM via the AXI-Stream interface, with a write cycle of 1kHz;

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

[0395] Timestamp alignment error is ≤ 0.1μs, ensuring temporal consistency between multimodal perception data and manifold states. Protocol conversion latency is ≤ 10μs, meeting real-time control requirements.

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

Claims

1. A heterogeneous robot control system driven by embodied intelligence and multimodal perception, characterized by: include: The quantum clock reference module is used to generate a globally synchronized picosecond time reference signal and synchronize multi-modal sensor clocks via microwave pulses. The clock deviation compensation of the quantum clock reference module satisfies the following requirements: in: γ e =28GHz / T is the electron gyromagnetic ratio; Δt corr is the clock deviation compensation amount, in μs; B(t) is the time-varying magnetic field intensity in Tesla; τ coh ≥1ms is the NV color center coherence time; K p ∈[0.5,2.0] is the proportional gain; K d =0.1 is the differential gain; The module is coupled to the sensor clock circuit via a 2.87GHz microwave resonant cavity, with a synchronization accuracy of ≤0.3μs; Multimodal perception module, including an event camera with a dynamic range of ≥120dB, a six-axis force sensor with a noise density of ≤0.01N / √Hz, and an inertial measurement unit with a zero-bias stability of ≤0.8° / h; The random differential manifold module uses a four-layer fully connected neural network. The input layer receives multimodal data, the hidden layer has 256 nodes, the activation function is ReLU, and the output layer generates a metric tensor through covariance operation. The stability control of the random differential manifold module satisfies: in: is the time derivative of the Lyapunov function; x i is the i-th dimension component of the manifold state vector; λ i =0.5 is the attenuation coefficient; is the manifold embedding function gradient; ||·||2 is the two-norm; The optimization process is implemented on the AI engine of the Xilinx Versal ACAP chip; Predictive control module, which solves the Hamilton-Jacobi-Bellman equations in parallel on the GPU with an update frequency of 1kHz; The embodied control module generates joint torque signals with a bandwidth ≥ 100 Hz and an overload protection threshold of ± 300 N·m; System middleware supports ROS2 and EtherCAT protocol conversion, with a delay of ≤10μs and data verification using 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 data transmission architecture of the multimodal perception module includes: The event camera transmits data at 8Gbps via the MIPI CSI-3 interface, and each frame packet header contains a 64-bit timestamp; The six-dimensional force sensor allocates 0x100-0x1FF interval on the CAN FD bus to transmit raw data, with a transmission interval of ≤100μs; The DMA channel trigger condition of the inertial measurement unit is angular velocity change rate ≥ 500° / s 2 .

3. The 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 differential manifold module includes: Curvature regularization weighting coefficient 0.2-0.3; The time curvature adjustment coefficient of the expansion manifold is 0.1-0.2; The dynamic prediction time domain adjustment step is 10-15ms.

4. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that: The prediction time domain adjustment rule of the prediction control module is: When the manifold curvature change rate is greater than 0.05rad / ms, the time domain is shortened to 50ms; When the joint angular velocity is less than 5° / s and the manifold diffusion coefficient is less than or equal to 0.01, the time domain is extended to 200ms.

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

6. 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 timestamp field accuracy is 0.1μs; The mapping table from ROS2 topics to EtherCAT PDOs is stored in the FPGA on-chip RAM; The data frame format includes a manifold status field, a control instruction field, and a CRC check field.

7. The heterogeneous robot control system driven by embodied intelligence and multimodal perception according to claim 1, characterized in that: The dynamic performance verification indicators of the system include: When the slope terrain adaptation angle is ≥30°, the ZMP deviation of the legged robot is ≤2cm; Recovery time under 50N·s impact ≤0.5s; The average power consumption is ≤20W when working continuously for 8 hours.

8. 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 multimodal sensing module are connected via a star topology with a line length of ≤10cm; The predictive control module uses the NVIDIA Jetson AGX Orin platform and connects to the manifold module via a PCIe 4.0×16 interface; The embodied control module and the actuator form a closed loop, and the feedback delay is ≤50μs.

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