1446 results about "Man machine" patented technology

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention discloses a cooperative steering control method considering man-machine mutual reliability, and aims to solve the problem of man-machine conflict in an existing man-machine co-driving system. The invention relates to the field of intelligent driving. The system specifically comprises an expected path module, a man-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a man-machine co-driving vehicle. The expected path module generates an expected path between the driver and the controller; the man-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected path of the driver and the controller; the driver experience evaluation module outputs the driver trust degree of the controller based on the vehicle state; the weight distribution module is used for outputting a driver control weight; and the non-cooperative game module outputs an optimal turning angle in combination with the expected path of the driver and the controller and the control weight of the driver, and controls the man-machine co-driving vehicle to complete the cooperative steering action.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a multi-machine cooperative scheduling system of a man-machine control shelter, and belongs to the technical field of multi-machine cooperative scheduling control. The environment feature intelligent classification and curved surface modeling module is connected with the task semantic understanding and planning module; the task semantic enhancement and risk assessment module is connected with the environment feature intelligent classification and curved surface modeling module; and the multi-machine collaborative decision module is connected with the task semantic enhancement and risk assessment module. An organic whole is formed through tight cooperation of functions such as task semantic understanding, environment modeling, risk assessment and decision making, different types of machines can efficiently and cooperatively complete complex tasks under unified scheduling of the system, the limitation of isolated operation of modules in a traditional system is broken through, and the reliability of the system is improved. And the harsh requirements on multi-machine collaborative operation in multiple fields and multiple scenes are met.

The invention provides a robot social adaptive navigation knowledge learning and migration method and system, and relates to the field of mobile robot navigation. Aiming at the problems that an existing path planner lacks time sequence memory and neglects pedestrian social intent, a man-machine co-fusion scene is constructed, a training set containing an expert demonstration path is made, and a recursive generation model is input; designing a recurrent neural network embedded RRT, generating an RNN-RRT planner, and fusing historical information and pedestrian convergence probability in training; new scene loading training parameters are finely adjusted to realize knowledge migration, loss convergence or output RNN final parameters after reaching a preset round number. According to the method, the path anthropomorphism and generalization ability are improved, and the method is suitable for complex human-computer interaction scenes.

The invention discloses a human-computer interaction method and system based on a vision-language-action model, and belongs to the field of human-computer interaction. According to the method, an anchoring ring strategy is adopted to collect human teaching data to finely adjust the VLA model, and then the trained model is applied to an actual human-computer interaction scene. In the data acquisition stage, a first operator guides a master robot to execute task actions, and a slave robot synchronously moves and interacts with a second operator, and returns to a predefined initial position after each interaction; a teaching sample is formed by recording a robot state, an environment image and an instruction text, and a high-quality data set is generated through data enhancement. In the application stage, the real-time robot state, the environment image and the instruction text serve as input, an action instruction is generated through the VLA model, and the robot is driven to complete a cooperation task. According to the method, the data utilization efficiency and the model generalization ability are remarkably improved, the difference between simulation and the real environment is effectively overcome, and efficient, safe and natural man-machine cooperation is achieved.

The invention discloses a man-machine cooperation task allocation method based on reinforcement learning, and the method comprises the steps: constructing a reinforcement learning model which comprises a task definition, a resource definition, a state space, an action set and a reward function, and carrying out the modeling of a cooperation relation among a task, a worker and a robot in man-machine cooperation through a heterogeneous graph neural network, and extracting high-order features of the state space. Based on the characteristics, a strategy network and a value network are trained through a near-end strategy optimization algorithm, and learning of an efficient task allocation strategy is achieved. The reward function combines the task completion duration and the resource utilization rate, and gives consideration to the production efficiency and resource balance. In the system operation process, workshop state data are collected in real time to dynamically update the state space, and the reinforcement learning model generates an optimal allocation action according to the latest state and issues an instruction to a worker or a robot for execution. The method has the advantages of high adaptability, high distribution efficiency, high scheduling intelligence degree and the like, and is suitable for task scheduling optimization in complex dynamic environments such as intelligent manufacturing and the like.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The present invention belongs to the field of multi-equipment safety management and control for personnel and equipment clusters at an underground coal mine driving working face, and solves the problem of safety hazards caused by human-machine collaborative operations as the mechanization level of underground equipment increases. Provided are a system and method for safety monitoring and early warning of human-machine collaborative operation at a working face. The system comprises a safety management and control system for mobile equipment at a working face, which is used for performing in real time position, orientation and environmental sensing of individual equipment and behavior analysis of drivers; working-face collaborative intelligent wearable devices, which are used for environmental sensing of operating personnel and individual equipment and acquiring in real time the motion postures of the operating personnel; and a working face output control subsystem, which is used for identification and management of operating personnel entry and exit, position monitoring of personnel and individual equipment, operating personnel behavior identification and monitoring and working environment monitoring, and generating corresponding alarm instruction information. The present invention can realize human-equipment and equipment-equipment safety management and control during multi-equipment collaborative operation at a driving working face.

The invention discloses a complex aerospace product man-machine cooperation assembly-oriented body-equipped agent packaging method and device. The method comprises the following steps: a sensing layer realizes multi-modal intention and environment sensing by using a large language model; the reasoning layer analyzes a structured assembly plan sequence through an assembly scene map; and the execution layer converts the target coordinate into a rotation angle to generate an action code. According to the method, the assembly plan sequence is generated according to the sensing result and is automatically converted into the robot execution code through the combination of the body agent packaging and the large language model, so that the self-adaptive capability of the man-machine cooperative assembly process is improved; therefore, the problems of high system integration difficulty, insufficient task adaptability and high-efficiency cooperation difficulty in man-machine cooperation assembly of complex aerospace products are solved.

The invention relates to the technical field of man-machine cooperation, and aims to solve the problems that the traditional man-machine cooperation process mainly depends on a preset flow and timing rhythm control, and is difficult to dynamically capture personnel misoperation, machine response delay or cooperation rhythm imbalance, so that response strategy lagging and low regulation and control efficiency are caused in the face of complex and variable man-machine interaction situations. According to the enterprise intelligent man-machine cooperation method and system, through comprehensive analysis with behavior response offset, cooperation rhythm consistency and a personnel fatigue state as core indexes, a set of intelligent man-machine cooperation process which is distinct in hierarchy, logic closed-loop and sustainable iterative optimization is constructed; the real-time response capability of man-machine cooperation and the production takt time stability are improved, the cooperation efficiency fluctuation can be dynamically monitored, the precise regulation and control strategy is output in time, quantitative evaluation and actual improvement of man-machine cooperation efficiency are achieved, and efficient production of a production line and the quality level of products are guaranteed.

The invention relates to an intelligent robot active cooperation assembling method based on multi-mode large model cooperation driving. The method is used for improving the perceptual understanding, autonomous reasoning and action execution capabilities of the robot in a dynamic complex industrial environment, so that a more efficient, safer and more flexible man-machine autonomous cooperation assembly mode is realized. According to the method, based on brain-like structure design, a multi-center system including a sensing center, a reasoning center and a motor center is constructed, multi-modal large model sensing understanding and semantic reasoning capabilities are fused, and multi-modal large model sensing understanding and semantic reasoning capabilities are obtained through information collaboration and autonomous decision-making among the centers. The robot is driven to realize active perception, semantic understanding, task reasoning and autonomous action execution in an assembly scene, and the adaptive cooperation capability of the robot in a dynamic environment is improved. The method provided by the invention is finally applied to a case of assembling the engine through man-machine cooperation, and the effectiveness of the method is verified.

The invention relates to the field of robot intelligent control and man-machine collaboration, in particular to a multi-sensing collaborative robot control method, which comprises the following steps: synchronously filtering mechanical, tactile, visual and physiological signals to generate a unified observation frame, and calculating power flow and energy in combination with a joint state to form an execution vector; extracting a topological descriptor by using persistent coherence, and outputting an interaction potential field and a human body intention by using the topological descriptor as a conditional driving diffusion model; then, power density is constructed based on the potential field gradient and intention, joint torque and impedance are generated through entropy regular optimal transmission and Jacobian and microoperator kernel mapping, and an energy Lagrange multiplier is updated in real time; and if the consistency error continuously exceeds a threshold value, estimating a Koopman lifting model on line by using a causal weight, completing model prediction control under the energy, conformal security and topology consistency constraint, outputting an update instruction and performing closed-loop feedback. The method improves the cooperation precision and safety, reduces the energy consumption, and is suitable for a high-dynamic man-machine cooperation scene.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention relates to the technical field of vehicles, in particular to an intelligent cabin man-machine cooperative control method and device, a vehicle, a medium and a product. Performing fusion processing on the voice signal data, the visual signal data and the tactile signal data of the current driver by using a preset dynamic weight distribution model to obtain a multi-modal fusion control instruction; and under the condition that the multi-mode fusion control instruction meets a preset sight-voice dual-mode condition, synchronously issuing the multi-mode fusion control instruction to a plurality of target execution devices in the intelligent cabin so as to drive each target execution device to perform cooperative control. Therefore, by constructing a cooperative control mechanism of multi-mode sensing and dynamic weight distribution, the problems that the false wake-up rate is high, the voice and graphical interface cooperative efficiency is low and the interaction mode cannot adapt to a complex driving environment due to a single or separated interaction mode in the related technology are effectively solved.

The embodiment of the invention provides a software development method based on artificial intelligence and related equipment. In the embodiment of the invention, an AI software development method and mode are provided, and a user and a technical design document generation agent cooperate to complete a technical design document of a programming task; and the programming agent autonomously performs task planning based on the technical design document, and autonomously executes the programming task based on the planned execution plan. In at least one of the technical design stage or the task execution stage of the programming task, a man-machine cooperation interface is provided, and the man-machine cooperation interface is used for a user to participate in the software development process for the programming task. Therefore, the software development efficiency and the code quality are greatly improved through division cooperation and man-machine cooperation interaction among the intelligent agents.

The invention discloses a CRITIC-based man-machine cooperation system trust degree evaluation method, and belongs to the technical field of man-machine interaction and human factor engineering, and the method comprises the steps: S1, obtaining the physiological data and flight data of a pilot when the pilot executes a manned and unmanned system task; s2, preprocessing and standardizing the physiological data and the flight data respectively; s3, weight calculation is carried out based on a CRITIC method, and standardized multi-dimensional feature data is used as input of a CRITIC model and is used for calculating objective weights of all indexes; and S4, calculating a credibility value by using the generated weight matrix. By adopting the CRITIC-based man-machine cooperation system trust degree evaluation method, objective real-time quantitative evaluation is realized; the weight is adaptively determined according to the data features, the method is more scientific than artificial experience, physiological and behavior data are fused to capture psychological changes, the trust level can be monitored, the cooperation strategy is adjusted accordingly, and the task safety and efficiency are improved.

The invention relates to the technical field of humanoid robot control. The invention provides a humanoid robot bilateral force feedback teleoperation method of a force compensation mechanism. According to the method, a 5G private network is used for deployment, the characteristics of low delay, high reliability and large bandwidth of the 5G private network are utilized, real-time interaction of high-dimensional haptic data in a remote and cross-domain environment is effectively supported, cross-domain teleoperation can be achieved, high-fidelity feedback transmission of environment contact force is effectively achieved, and the real-time interaction of the high-dimensional haptic data in the remote and cross-domain environment is achieved. The precision, the operation efficiency and the overall safety of remote operation of the humanoid robot in complex industrial scenes such as nuclear power maintenance, precise assembly and high-risk environment operation are remarkably improved, the gap between traditional automation and low-efficiency remote operation is filled up, and reliable technical support is provided for advanced industrial application needing man-machine cooperation fine force control.

The invention relates to the technical field of unmanned aerial vehicle cluster navigation, and discloses an unmanned aerial vehicle cluster navigation method based on man-machine interaction. The method comprises the steps that cluster task parameters and environment constraint conditions input by an operator are efficiently received through a multi-modal interaction interface, the cluster task parameters comprise a target point coordinate set and formation form identification, and the environment constraint conditions cover no-fly zone coordinates and meteorological interference levels. And the system calls a pre-stored topological relation template according to the formation form identifier to generate an initial track, completes space conflict detection in combination with the no-fly zone coordinates, and outputs a corrected track point sequence. And inputting the meteorological interference level into a dynamic anti-interference model to calculate a course correction amount, and superposing the course correction amount to a corrected track point sequence to generate an anti-interference navigation instruction set. And acquiring position data of the unmanned aerial vehicle in real time to calculate a trajectory deviation degree, triggering a manual intervention request if the trajectory deviation degree exceeds a dynamic threshold value, calling a correction instruction, fusing to generate a hybrid control strategy, and distributing the hybrid control strategy to each node of the cluster to realize reliable man-machine collaborative navigation.

The invention discloses an intelligent manufacturing workstation safety control system oriented to man-machine cooperation, and belongs to the technical field of intelligent manufacturing and man-machine cooperation. The system comprises a multi-modal sensor array, a personnel state sensing module, a robot state monitoring module, a dynamic safety area adaptive calculation module, an intention prediction and risk assessment module, a cooperative control decision module and an execution feedback module. The dynamic safety area self-adaptive calculation module constructs geometric representation of a man-machine interaction space based on a topological manifold theory, and calculates a dynamic safety boundary in real time; the intention prediction and risk assessment module is fused with multi-source information to predict a future trajectory of a person and assess a collision risk; the cooperative control decision module performs multi-objective optimization with safety and efficiency as double objectives to generate an optimal adjustment strategy, the system realizes prospective safety control based on intention prediction, the cooperative efficiency is improved by 25%-45% on the premise of guaranteeing safety, and the working safety accident rate is reduced to be close to zero.

The invention provides a man-machine marshalling intention recognition method and system based on skeleton trajectory and graph convolution. The method comprises the steps of obtaining real-time personnel behavior video stream data in a man-machine marshalling scene; human skeleton sequence information is extracted from the real-time personnel behavior video stream data, and a personnel skeleton spatial-temporal feature sequence diagram is constructed; and performing joint-limb-behavior three-level feature coding on the personnel skeleton spatial-temporal feature sequence diagram by using a hierarchical graph convolutional network, and outputting a predicted personnel behavior intention.

The invention discloses an RFID-based railway construction man-machine information intelligent management method, and relates to the technical field of railway construction safety management, and the method comprises the steps: collecting original radio frequency signals and environment data of personnel and mechanical tags in a construction region; constructing and updating a digital twin atlas in real time based on the original radio frequency signal and the environmental data, and performing adaptive calibration on the tag signal by using the atlas to obtain a high-precision spatio-temporal trajectory; inputting the high-precision spatio-temporal trajectory into a spatio-temporal relation inference engine, and outputting a quantized safety risk level; and performing iterative optimization on the digital twin map and the physical rule model based on a feedback result of risk early warning. According to the invention, accurate calibration and compensation of RFID signals in a complex construction environment are realized by constructing a dynamically updated wireless signal digital twin map; a time-space relation inference engine is fused with multi-dimensional data to carry out prospective risk deduction, a quantitative safety risk level is output, and conversion from passive alarm to active early warning is realized.

The invention belongs to the field of robots, and particularly discloses a mechanical arm man-machine cooperation self-adaptive admittance control method and device, a terminal and a medium, a man-machine interaction model omitting virtual stiffness is constructed, and admittance parameters in the man-machine interaction model comprise virtual inertia and virtual damping; designing a stability observer of the physical man-machine interaction system, monitoring an external force applied by an operator by the stability observer, and performing spectral analysis on a force signal to obtain a system instability index; constructing an admittance parameter adaptive controller through a system instability index, wherein the admittance parameter adaptive controller comprises a virtual inertia adaptive controller and a virtual damping adaptive controller; and the virtual inertia and the virtual damping are adjusted based on the admittance parameter self-adaptive controller, so that the physical man-machine interaction system returns to a stable state. The method realizes self-adaptive adjustment of controller parameters, adapts to a complex environment, and keeps stability of physical man-machine interaction.

The invention provides an intelligent optimized arrangement method for a shield construction site. The method comprises the steps that S1, component parameter modeling is conducted on various components of a shield construction site, a knowledge rule base is established, and a component semantic map is established; s2, spatial modeling of a shield construction site and component matching are carried out; s3, on the basis of parameter modeling and rule modeling, constructing a composite target function fusing multiple types of engineering attributes, and outputting a component arrangement scheme meeting a global optimization target, spatial constraint and engineering logic; s4, setting a man-machine collaborative arrangement interface to support a user to participate in adjustment; and S5, based on the component arrangement scheme and the user feedback behavior, calling an arrangement inference engine fusing a historical sample and a feedback learning mechanism, and optimizing the arrangement strategy again. According to the method, the arrangement efficiency and the space utilization rate are improved, the arrangement requirements of complex and irregular sites can be met, multi-target collaborative optimization and conflict automatic detection are achieved, and the method has the arrangement man-machine collaborative adjustment and strategy self-learning capabilities.

The invention discloses a rescue material transportation following robot based on oil-electric hybrid power and a control method, and aims to solve the problems of endurance, carrying and energy complementation of a pure electric rescue material following robot under the conditions of long-time heavy load, complex terrain and inconvenience in charging. The method comprises the following steps: acquiring multi-source state, motion and environment data, and predicting a power demand; dynamically selecting a pure electric charging mode, a range extending charging mode, a parallel charging mode or a parking charging mode; and generating parallel power and a motion instruction to drive the robot to follow operation. The system comprises a hybrid power unit, a following positioning module, an environment sensing module, a main controller, an energy management unit, a motion control unit and a driving execution unit. The problems of endurance and energy complementation of the pure electric robot are effectively solved, the operation continuity, the environment adaptability, the carrying efficiency and the man-machine cooperation safety are remarkably improved, and the system energy efficiency is optimized.

The invention belongs to the technical field of man-machine cooperation. The invention provides a self-adaptive man-machine cooperation task scheduling method for an intelligent manufacturing scene. According to the embodiment of the invention, an extensible context state encoder is designed, scene features, task dependency relationships, resource states and the like are fused based on a graph neural network, and multi-dimensional constraint state representation is generated; an experience-driven task distributor is constructed, a cross-scene cooperation mode is mined through historical scheduling knowledge, and an initial distribution scheme is generated to reduce the search space dimension; an autonomous learning optimizer based on element reinforcement learning is developed, and field differences and scale elastic changes are quickly adapted through multi-scene element strategy pre-training and online lightweight fine tuning.