6272 results about "Motion control" patented technology

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

The invention discloses a rescue robot path planning method and system under industrial vision assistance, and relates to the field related to industrial vision, and the method comprises the steps: collecting three-dimensional space data of a rescue environment in real time, generating a dynamic environment point cloud data set, and constructing a three-dimensional semantic map of a rescue area; thermal imaging data updated in real time are called, path analysis is carried out in combination with the three-dimensional semantic map, and a path planning strategy set is obtained; and predicting the motion track of the dynamic obstacle based on the local dynamic obstacle avoidance strategy, optimizing the global path planning strategy according to obstacle prediction track data, and generating a motion control instruction of the rescue robot. The technical problem of poor real-time performance and adaptability of path planning caused by insufficient perception of environment dynamic information in path planning of an existing rescue robot is solved, the strong perception capability depending on industrial vision is achieved, the environment dynamic information is accurately captured in real time, and the real-time performance of path planning is improved. And the real-time response speed of path planning and the adaptability to a dynamic environment are improved.

The embodiment of the invention relates to robot path planning, in particular to a self-adaptive robot trajectory planning method and system based on deep reinforcement learning, and the method can completely present the motion condition of a robot by obtaining a real-time trajectory data set covering an environment interaction state and a motion attitude sequence; key features can be accurately extracted through dynamic track feature extraction processing; a self-adaptive trajectory optimization strategy is generated based on a pre-trained deep reinforcement learning strategy network, so that the strategy can be flexibly adjusted according to dynamic characteristics; incremental trajectory correction processing is carried out on the robot motion posture sequence, the trajectory can be optimized step by step, and an optimized trajectory sequence conforming to dynamic environment adaptability is generated; and the optimized trajectory sequence is synchronized to the motion control system, so that the robot can quickly and accurately execute the optimized trajectory, and the motion planning capability and execution efficiency of the robot in a complex dynamic environment are remarkably enhanced.

The invention provides a trajectory control video generation method and device based on depth information and time-frequency optimization, and relates to the technical field of image processing, and the method comprises the steps: optimizing a 3D trajectory through multi-entity segmentation, depth estimation and time-frequency decomposition in combination with a user instruction, and generating a control signal through a multi-scale fusion network; finally, the signals and original images are input into an improved Stable Video Diffusion model to generate a video potential representation sequence, the problems that an existing video generation method is insufficient in dynamic entity motion control precision and poor in cross-frame consistency are solved, and through 3D trajectory modeling guided by depth information and a time-frequency joint optimization mechanism, the video potential representation sequence is generated. And the motion smoothness, the space authenticity and the time-frequency stability of the generated video are obviously improved.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

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

The invention relates to the technical field of robot autonomous navigation and control, and discloses a robot system based on a sensing system, which comprises a multi-mode sensing module for outputting data to a dynamic environment modeling module; the dynamic environment modeling module is used for transmitting the model to the path planning module; the path planning module outputs the path sequence to the neural motion control module; the neural motion control module outputs the control signal to the dynamic execution module; the dynamic execution module is used for receiving the pulse control signal and driving each joint of the robot to execute the path in combination with inverse kinematics solution and a feedback control mechanism; and the collaborative optimization module is in two-way communication with the modeling module, the path planning module and the neural motion control module. According to the method, the contradiction between real-time performance and integrity of environment modeling in a complex scene is solved by fusing cross-modal information complementary characteristics of binocular vision, millimeter-wave radar and inertial data and combining mathematical representation of a hypergraph topology model on a dynamic obstacle interaction relationship.

The invention relates to a power grid monitoring bionic robot with multi-mode sensing and voice interaction functions. The acquisition unit acquires sound, images, temperature and power grid equipment surface micro-vibration information, completes data preprocessing and time and space calibration, and generates a sensing data packet. And the identification unit performs cross judgment based on the abrupt change characteristics and correlation of different types of data, identifies electrical, structural or environmental anomalies, and generates abnormal region description information. The construction unit analyzes whether a multi-factor coupling risk exists in combination with a power grid environment type and a historical rule, and generates task description information including processing suggestions and development path prediction. The analysis unit supports speech enhancement and tone extraction in a high-interference environment, and extracts a user interaction intention in combination with task description information. And the control unit jointly judges a behavior correction strategy according to the task description information and the interaction instruction information, generates a corresponding motion control instruction and voice feedback, and realizes autonomous monitoring and interaction response in a complex power grid environment.

The invention discloses a mechanical arm motion control method based on multi-agent cooperation, and the method comprises the steps: firstly, receiving an RGB image through a sub-task generation agent, and generating a structured sub-task sequence according to a natural language task instruction of the RGB image; secondly, performing joint modeling on a task text and a scene image through a 3D sensing intelligent body, positioning specific coordinates of a target object in a three-dimensional space, reasoning dynamic characteristics of a current environment based on historical state information of a robot by combining an environment sensor, and generating an environment sensing vector; and finally, the action generation agent performs fusion modeling according to the subtask text, the subtask target coordinates, the current state of the robot and the environment perception vector, generates a continuous action vector, drives a mechanical arm to complete each subtask action, and constructs closed-loop feedback by a controller and a discriminator to realize task execution state judgment and automatic circulation. The precise action control instruction can be effectively generated, and the task execution fineness of the mechanical arm is remarkably improved.

The invention provides a servo motor real-time adaptive control method and system based on artificial intelligence, and the method comprises the steps: injecting a tiny excitation signal into a servo motor, collecting temperature and electromagnetic response data under different working conditions, and constructing a multi-dimensional information data set; performing real-time reasoning on the relationship between the temperature gradient and the magnetic path characteristics by using deep transfer learning to obtain multi-dimensional parameter deviation vectors such as stator flux linkage unbalance degree, permanent magnet flux weakening degree and torque coefficient change; generating a multi-scale compensation strategy through a physical enhancement reinforcement learning method and the multi-dimensional parameter deviation vector; and carrying out Lie group transformation processing on the control law under a differential geometric framework, and realizing accurate compensation on the temperature gradient effect through a dual-channel-dual-time scale framework. According to the method, heterogeneous sensing and multi-dimensional compensation strategies are adopted, the problem that the control precision of a traditional method is insufficient under the complex temperature gradient condition is solved, and the high-precision motion control performance is maintained under the dynamic working condition that the temperature distribution of the motor is rapidly changed.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention relates to the technical field of surgical robot motion control, and discloses a surgical robot motion planning method based on a diffusion model, and the method comprises the steps: obtaining obstacle point cloud data, an initial posture and a target posture in a surgical environment; encoding the obstacle point cloud data into a potential space, and combining the encoded information with the initial attitude and the target attitude into a condition code; a Transform structure of an encoder is adopted to replace a U-Net structure in a traditional diffusion model, and the diffusion model is trained through forward diffusion and reverse denoising processes; in training, using a comprehensive loss function to optimize model performance, combining configuration space loss, geometric task space loss and collision loss, and introducing physical constraints to ensure that the trajectory conforms to kinematic characteristics and avoid collision; and in the operation task, generating a motion track based on the trained diffusion model. According to the invention, the motion planning efficiency and safety of the surgical robot in a complex environment can be improved.

The invention discloses a mechanical arm precision control method based on a neural network, and relates to the field of mechanical arm precision control, and the method comprises the steps: S1, constructing a multi-source sensing data collection system of a mechanical arm system, and obtaining a joint control instruction, tail end track feedback, an environment state and stress data in real time; s2, constructing a time sequence prediction model based on a mask encoder, processing high-dimensional dynamic error data by adopting a mask encoder structure, adaptively dividing time sequences of different sampling frequencies through a multi-patch projection module, and introducing an arbitrary variable attention mechanism to capture time sequence dependence and heterogeneity between variables; s3, training the model by adopting a composite loss function; and S4, the mechanical arm target pose error point estimation output by the trained model is injected into a mechanical arm control instruction, the motion track of the mechanical arm is compensated in real time, and the mechanical arm is driven to execute high-precision motion control. The problems that in the prior art, efficient and accurate modeling of a multivariable time sequence is difficult, and scheduling optimization is insufficient are solved.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention provides an automatic driving-oriented kinematics priori guided vehicle trajectory generation method, and belongs to the technical field of trajectory generation and motion control in automatic driving. Comprising the following steps: constructing a vehicle kinematics differential equation, performing nonlinear compensation and control correction based on a double-flow mechanism to obtain a vehicle explicit physical state, and converting the vehicle explicit physical state into a vehicle implicit physical state; feature extraction, target detection and multi-mode fusion are carried out on the collected point cloud data and the vehicle surrounding image data, and environment information of a scene where the vehicle is located is provided; anchoring Gaussian distribution to simulate a feasible noise track of a vehicle in a current scene, generating a noise track candidate sample through sampling and noise adding, performing reverse denoising reasoning on the noise track candidate sample, generating a track anchor point, and generating a reasoning noise track around the track anchor point; environment information of a scene where the vehicle is located, a reasoning noise track and an implicit physical state are input into a diffusion decoder for iterative training, and kinematic prior guides generation of a future track of the vehicle in the denoising process.

The invention discloses a lineation robot control system based on a satellite positioning and orientation technology, which relates to the technical field of robot motion control and comprises a satellite positioning and orientation module, an inertial navigation module, a fusion positioning and orientation software module, a motor control software module, a motion control software module and a pattern planning software module. The satellite positioning and orientation module is used for acquiring latitude and longitude coordinates of the current position of the robot by receiving multi-frequency satellite signals; the inertial navigation module is used for collecting posture and motion data of the robot in real time through a gyroscope and an accelerometer. According to the lineation robot control system provided by the invention, through collaborative design of the multi-frequency-point GNSS receiver and the anti-multipath interference antenna array, centimeter-level continuous positioning capability in a complex urban environment is realized, and satellite signal reflection and shielding interference in scenes such as viaducts and avenues are effectively overcome; and satellite positioning and inertial navigation data are deeply fused through a tight coupling Kalman filtering algorithm.

A joint simulation and operation operability evaluation method based on wind power operation and maintenance ship motion dynamic response and active compensation gangway ladder motion control comprises the steps that an operation and maintenance ship frequency domain seakeeping model is built, and a hydrodynamic coefficient library is calculated; wave exciting force is reconstructed based on a Cummins time domain motion equation, and ship motion response prediction is realized by adopting implicit generalized alpha time domain composite integral; the dynamic positioning system extracts low-frequency motion through a Kalman filter and optimizes thruster thrust distribution to stabilize the position of the ship. A gangway ladder dynamic model is established based on a Lagrange equation, real-time control over displacement, speed and acceleration of the top end of the gangway ladder is achieved through a feedforward-feedback composite control strategy, and nonlinear output of a hydraulic actuator is optimized. And realizing joint simulation of the coupling system through time domain synchronization and self-adaptive PID (Proportion Integration Differentiation) control. And checking gangway ladder movement based on a preset threshold value and a space boundary condition, and evaluating the operability of the climbing operation. According to the method, the limitation of traditional single-disciplinary analysis is broken through, and the forecasting precision of wind power operation and maintenance operation operability is effectively improved.

The invention relates to the technical field of robots, and discloses a multi-source data fusion method of a tracked robot, which realizes dynamic environment adaptation and autonomous decision optimization through a multi-source sensing cooperation mechanism, and comprises the following steps: dynamically adjusting exposure parameters of a visual module based on environment illumination intensity, and avoiding overexposure or underexposure of an image through a feedback adjustment mechanism; fusing data of the accelerometer and the gyroscope to generate attitude angle compensation parameters; the heat dissipation system is intelligently regulated and controlled by comparing infrared thermal imaging and temperature sensor data; dynamic task priority distribution is realized; optimizing an advancing route to reduce complex terrain navigation deviation, in addition, establishing a multi-source sensing coordination mechanism, monitoring health indexes such as motor temperature and vibration frequency in real time, and triggering abnormal state judgment; track optimization is implemented in combination with pavement mechanical characteristics and a kinematic model; and executing the staged energy management strategy. According to the method, the environment sensing precision and the motion control reliability of the tracked robot in a complex environment are remarkably improved.

The invention relates to the technical field of orchard unmanned vehicles, in particular to an orchard unmanned vehicle intelligent navigation and control method based on multi-sensor fusion, and the method comprises the following steps: S1, global path planning: generating discrete plane path points, and then pre-fitting a prior navigation global path; s2, three-dimensional environment perception: extracting static obstacle and dynamic obstacle information in a working path in real time, detecting and identifying a target obstacle at the same time, and performing sensor information fusion; s3, path tracking control: controlling the orchard unmanned vehicle to run according to a priori navigation global path; and S4, dynamic path optimization: realizing dynamic planning of an obstacle avoidance trajectory, synchronously establishing a trajectory overlap ratio evaluation mechanism, and adjusting a driving mode to avoid the obstacle. According to the invention, the reliability of environment perception and path dynamic adjustment is improved, and the three-dimensional motion control model is constructed, so that the unmanned vehicle can safely and accurately complete autonomous navigation work.

The invention relates to a high sea condition unmanned ship dynamic anti-interference control method based on intelligent body.The method comprises the following steps that information is collected, multi-source data are integrated through a data fusion algorithm, and multi-mode sensing data are obtained; constructing a body-equipped intelligent model, taking the multi-modal sensing data as input and the unmanned ship control instruction as output, and performing offline training; constructing an interference prediction model to predict the change trend of interference factors under the high sea condition, and performing adaptive interference compensation according to an interference prediction result; a hierarchical control structure is designed, a task planning layer makes a global navigation plan based on risk assessment, a motion control layer adopts a model prediction control and adaptive sliding mode control combined algorithm to convert instructions, and an execution mechanism layer drives an execution mechanism; meanwhile, online self-adaptive optimization is carried out, and the intelligent model with the body is updated and optimized according to feedback information. Compared with the prior art, the dynamic anti-interference capability, the operation precision and the reliability of the unmanned surface vehicle under the high sea condition are remarkably improved, the environment adaptability and the autonomous operation level of the unmanned surface vehicle are enhanced, and the unmanned surface vehicle is suitable for various high sea condition ocean operation scenes.

The invention discloses a quadruped robot robust motion control method based on deep reinforcement learning, and belongs to the technical field of robot motion control, and the method comprises the steps: constructing a deep reinforcement learning model which comprises a state estimation network, a strategy network and a value network; interaction between the quadruped robot and the simulation environment is carried out, and standard observation information, historical observation information and privileged observation information of the quadruped robot at all moments are obtained; inputting the standard observation information, the historical observation information and the privilege observation information of the moment into a deep reinforcement learning model, and training based on a total loss function until convergence is carried out to obtain a trained deep reinforcement learning model; inputting standard observation information and historical observation information at corresponding moments in an actual scene into the trained deep reinforcement learning model to obtain output features of a corresponding strategy network; and the target position of each joint motor is calculated to complete the motion control of the quadruped robot. And efficient training and robust motion on various complex and unstructured terrains can be realized.

The invention discloses an industrial robot intelligent control system which comprises a master control terminal, the master control terminal is provided with an industrial-grade multi-core processor and is integrated with a motion control subsystem and an environment sensing subsystem, the motion control subsystem is connected with the master control terminal through a first communication interface, and the environment sensing subsystem is connected with the master control terminal through a second communication interface. According to the motion control subsystem, a distributed axis controller array and reinforcement learning strategy switching algorithm is adopted, so that the trajectory tracking precision and the energy consumption efficiency are improved; and the environment sensing subsystem realizes real-time anomaly detection through multi-mode sensor fusion and an LSTM-GAN fault prediction model. The system supports containerized deployment and edge computing collaboration and integrates AR human-computer interaction, and the adaptive capacity and safety of the industrial robot are remarkably improved.

The invention provides an intelligent barn automatic inspection system and method, and relates to the technical field of intelligent inspection, the system comprises an inspection robot, a 5G communication transmission module and a remote computer, the inspection robot comprises an environment sensing module for generating and updating an environment map and identifying obstacle information; the autonomous navigation control module comprises a path planning unit for generating an inspection path and performing local path adjustment; the obstacle avoidance unit is used for generating an obstacle avoidance strategy; the motion control unit controls the movement of the inspection robot; the data acquisition module is used for acquiring information in the granary; the data processing and analyzing module is used for processing and analyzing the collected multi-source data and outputting a monitoring report and early warning information; wherein the 5G communication transmission module is connected with the inspection robot and the remote computer, and is used for transmitting data output by each module in the inspection robot and receiving an instruction sent by the remote computer. According to the invention, real-time sensing, intelligent obstacle avoidance and autonomous inspection of the granary environment are realized.

The invention discloses a lower limb exoskeleton gait track prediction method based on an LSTM-KAN fusion model, and the method comprises the steps: collecting human motion data through a sensor assembly, and carrying out the filtering, missing value processing and normalization of the human motion data; then constructing an overall architecture of a prediction model based on an LSTM-KAN network, optimizing parameters of the prediction model by using a particle swarm optimization (PSO) algorithm, extracting key features in the preprocessed data as a training data set, and inputting the training data set into the prediction model for training; and finally, collecting current human body motion data, pre-processing the current human body motion data, inputting the pre-processed current human body motion data into the trained prediction model, and outputting future human body gaits and tracks by the prediction model. And the controller takes a future gait track generated by the prediction model as a reference track to generate a driving signal and sends the driving signal to the actuator so as to realize accurate control of the actuator. By adopting the gyroscope sensor, the acceleration sensor and the pressure sensor for gait estimation, exoskeleton motion control can be effectively improved, and man-machine interaction experience is effectively improved.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a hierarchical reinforcement learning strategy. The unmanned aerial vehicle autonomous navigation system is suitable for a three-dimensional flight task in an unknown environment. The system comprises a state sensing module, a hierarchical strategy network module, a control execution module, a data classification module and a data playback module. The state sensing module extracts obstacle position information based on a deep neural network, and fuses the target, the obstacle position and the flight state to generate a state vector and a time sequence. The hierarchical strategy network adopts a high-layer DQN to generate a navigation intention, and a low-layer LSTM and PPO are combined to output a continuous control action; the control execution module adjusts the attitude of the unmanned aerial vehicle according to the instruction and performs closed-loop correction. The system introduces a double dynamic memory mechanism (DDM), improves strategy training efficiency and stability through experience classification and proportional sampling, and adopts a multi-target award function guide strategy to optimize convergence among task completion, obstacle avoidance safety and flight rationality. The system has good environmental adaptability and generalization ability, and is suitable for autonomous navigation tasks in complex scenes.

The invention relates to the technical field of robot training. The robot self-adaptive training method based on reinforcement learning comprises the steps that task sub-target information is generated through a high-level strategy network, the task sub-target information is input into a low-level execution network, an action control instruction is generated according to the task sub-target information, interaction feedback information is collected in the execution process, and the action control instruction is sent to a robot through a robot. Calculating a reward value according to the interaction feedback information, carrying out association processing on the reward value and the scene complexity parameter, executing a dynamic reward shaping operation, generating an adjusted reward signal, generating a strategy model optimized by meta-learning based on the adjusted reward signal, loading the strategy model in a simulation environment, and carrying out dynamic reward shaping. A target strategy model optimized through simulation training is generated, the target strategy model is loaded to the robot, and the robot is controlled to execute task operation in the actual interaction scene. The method has the effect of realizing adaptive task learning of the robot in a multi-interaction scene.

The embodiment of the invention provides an automatic driving method and device based on a multi-dimensional reward function, and the method and device achieve the control of a driving motion through the combination of an imitation learning framework and a reinforcement learning framework, collection of environment information through a plurality of cameras, and construction of a strategy generation network and a discriminator network. A multi-dimensional reward function model is designed, behaviors such as red light running, line pressing, lane departure and collision are detected and evaluated in real time, and a driving behavior reward and punishment matrix is constructed. Based on an actor evaluation network architecture, environment information and a navigation instruction are input into an actor network to generate an optimal driving action, and the action value is evaluated through the evaluation network to realize dynamic parameter optimization. According to the method, the defects of the traditional technology in the aspects of driving behavior evaluation, action value judgment and the like are effectively overcome, and the safety and reliability of the automatic driving system are remarkably improved.

The invention discloses a hierarchical multi-agent robot cluster control method and system, and aims to solve the problems of partial observability and environment non-stability of a multi-agent system in a complex environment. According to the method, a three-layer layered reinforcement learning architecture is constructed, a high-layer strategy is responsible for global task decomposition and role allocation, a middle-layer strategy converts tactical intention into a cooperative behavior mode, and a low-layer strategy executes accurate motion control; a graph neural network is adopted for cluster modeling, global graph representation and local neighborhood features are extracted in parallel through graph convolution and an attention mechanism, and hierarchical decision making is supported; a centralized graph enhancement evaluation network is designed to be combined with an MAPPO algorithm for collaborative optimization, and dynamic adversarial training is introduced to improve strategy robustness. According to the method, effective decoupling of global planning and local control is realized, and the cluster cooperation efficiency, the strategy interpretability and the adaptive capacity in a dynamic environment are improved.

The invention relates to the technical field of mechanical arm control, and particularly discloses a multi-axis motion control method based on a six-axis hydraulic mechanical arm, and the method comprises the following steps: establishing a kinetic model and a hydraulic system model of the six-axis hydraulic mechanical arm, generating a motion track in a Cartesian space or a joint space based on a preset task demand, and generating a multi-axis motion model of the six-axis hydraulic mechanical arm; the track smoothness is optimized through an S-type acceleration and deceleration algorithm; a deviation coupling synchronous control strategy is adopted, and position, speed and pressure signals of all hydraulic cylinders are collected in real time. According to the multi-axis motion control method based on the six-axis hydraulic mechanical arm, through multi-layer modeling, multi-algorithm fusion and intelligent optimization, the core problems of synchronous errors, non-linear interference, high energy consumption and the like in multi-axis cooperative control of the hydraulic mechanical arm are solved, high-precision, high-robustness and low-energy-consumption industrial-grade motion control is achieved, and the multi-axis motion control method is suitable for industrial production. The method is suitable for heavy-load carrying, precision assembling and other complex scenes.