363 results about "Autonomous control" patented technology

The invention relates to the field of power distribution networks, in particular to a power distribution network voltage collaborative autonomous method and system based on dynamic partition. The method comprises the following steps: acquiring electrical measurement data and network topology parameters of distributed nodes of a power distribution network, and generating a characteristic state set representing the operation state of a system; performing dynamic subarea division based on node voltage coupling strength and power balance constraint to obtain a dynamic subarea set with an autonomous boundary; each partition control main body independently solves a voltage regulation objective function of the partition according to an autonomous boundary, and generates a partition autonomous control strategy; and boundary interactive iterative coordination is carried out between adjacent partitions, and a global optimal voltage cooperative control instruction is generated and executed. According to the method, the problems that partition division is not matched with the operation state, and partition collaboration is insufficient are solved, unification of partition autonomy and global optimization is achieved, and the real-time performance and accuracy of voltage regulation and control of the power distribution network are remarkably improved.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

The invention discloses a reinforcement learning-based control modeling method for a water-air cross-medium unmanned aerial vehicle and a related device, and the method comprises the steps: carrying out the interaction of a strategy network and a simulation environment, and generating a series of trajectory data; predicting a state value according to the trajectory data through a value network, and calculating a corresponding dominant function and a target value function based on the state value; calculating a value loss item, a strategy loss item and a strategy regular item through a strategy optimization algorithm so as to construct a total loss function, and performing joint training update on the strategy network and the value network by taking minimization of the total loss function as a target to obtain the updated strategy network and the updated value network; and judging whether the updated strategy network and the updated value network meet a preset dual convergence condition, if so, taking the updated strategy network and the updated value network as a water-air cross-medium unmanned aerial vehicle control model, and guiding the unmanned aerial vehicle to realize smooth and stable autonomous control in air, underwater and transition stages. And the intelligent autonomous control level of the cross-medium unmanned aerial vehicle is improved.

The invention discloses a power grid dynamic reactive power cooperative control system and method based on multi-source data fusion, and the system comprises a cloud cooperative control platform and a plurality of edge autonomous control nodes, and the cloud cooperative control platform collects multi-source heterogeneous data of a main network, a distribution network, distributed energy and a load side through a multi-source data sensing module. A power grid panoramic state feature tensor is generated through a data fusion module, a power grid digital twin module constructs and dynamically updates a power grid digital twin based on the feature tensor, and a centralized optimization decision module performs collaborative optimization calculation based on a digital twin model to generate an adjustment instruction of reactive power equipment in each region; and the edge autonomous control node executes a cloud instruction when the communication is normal, and performs local autonomous control when the communication is abnormal. According to the method, power grid panoramic perception is realized through deep fusion of multi-source data, the system can perform local adjustment when communication is abnormal based on the cloud edge collaborative architecture, and the globality, the real-time performance and the reliability of power grid reactive power control are effectively improved.

A method for detecting and mitigating tailgating behavior in vehicle environment is disclosed. The method includes receiving real-time data from sensors integrated into a vehicle. The real-time data comprises information related to the surroundings of the vehicle. The method comprises analyzing the real-time data based on using Artificial Intelligence (AI) models extracted from a memory, to detect patterns and behaviors of the vehicle. The method includes identifying tailgating behavior based on the patterns and behaviors of the vehicle. The method includes validating accuracy of the identified tailgating behavior using the AI models. The method includes generating an alert to a user of the vehicle based on the validation of the accuracy of the identified tailgating behavior. The method comprises enabling an autonomous control system integrated with the vehicle to adjust speed and lane positioning of the vehicle based on the alert.

The invention relates to a method for operating an autonomously controlled vehicle (2), comprising at least one automated driving system (8) for controlling a vehicle journey, a vehicle interior (4) for a vehicle occupant (6), with at least one comfort system (10) for providing a comfort function for the vehicle occupant (6), and with at least one safety system (12) for protecting the vehicle occupant (6), an interior sensor (14) for detecting an interior situation (16) of the vehicle occupant (6), and an environment sensor (18) for detecting an environment situation (20), wherein a current actual driving situation is detected on the basis of system data (36) of the driving system (8), the comfort system (10) and / or the safety system (12) as well as sensor data (38) of the interior sensor (14) and the environment sensor (18).a key parameter (40) relating to the vehicle occupants (6) for a future driving situation is predicted and evaluated based on the system data (36) and the sensor data (38), based on the evaluation a vehicle behavior to realize a target driving situation is determined, the specific vehicle behavior is adapted taking into account an interaction between the interior situation (16) and / or the exterior situation (20), and the adapted vehicle behavior (42) is set by controlling the driving system (8), the comfort system (10) and / or the safety system (12).

A substrate processing system comprises an edge computing device including processor that executes instructions stored in a memory to process an image or video captured by camera(s) of at least one of a substrate and a component of the substrate processing system. The component is associated with a robot transporting the substrate between processing chambers of the substrate processing system or between the substrate processing system and a second substrate processing system. The cameras are located along a travel path of the substrate. The instructions configure the processor to transmit first data from the image to a remote server via a network and to receive second data from the remote server via the network in response to transmitting the first data to the remote server. The instructions configure the processor to operate the substrate processing system according to the second data in an automated or autonomous manner.

The invention provides an intelligent unmanned aerial vehicle autonomous control system and method based on multi-sensor fusion, and relates to the technical field of unmanned aerial vehicle attitude control. Determining an attitude decision instruction of the intelligent unmanned aerial vehicle during autonomous flight path decision, and determining a swing control quantity of the intelligent unmanned aerial vehicle during autonomous flight attitude adjustment according to the attitude decision instruction; performing compensation identification on the calibrated obstacle avoidance condition and the attitude planning information of the intelligent unmanned aerial vehicle to obtain an attitude compensation strategy expected to be allocated by each actuator in the intelligent unmanned aerial vehicle, and determining a planned attitude angle of the intelligent unmanned aerial vehicle during autonomous switching of a flight mode according to the attitude compensation strategy; and according to the swing control quantity and the planned attitude angle, carrying out deviation correction control on the course of the intelligent unmanned aerial vehicle when the intelligent unmanned aerial vehicle executes the autonomous flight task. According to the invention, full-process cooperative control can be carried out on the attitude stability of the unmanned aerial vehicle under the support of multi-sensor data, so that the accuracy of autonomous flight of the intelligent unmanned aerial vehicle is improved.

The present disclosure provides a method for controlling a humanoid robot using a hierarchical bipedal action model (BAM), the method comprising obtaining a base controller by training in simulation with reinforcement learning, instantiating an initial BAM including a Gamma model configured to generate intermediate goals, a Beta model configured to translate the intermediate goals into task-space actions, and an Alpha model configured to translate the task-space actions and robot state into motor commands, deploying the initial BAM such that at least the Alpha model executes on-board the humanoid robot, causing the humanoid robot to perform an initial task and logging sensor and control data to form a first dataset, based on the first dataset, training at least one policy of the BAM to generate a refined BAM, and deploying the refined BAM to control the humanoid robot autonomously.

In some implementations, the EMV uses a calibration to inform autonomous control over the EMV. To calibrate an EMV, the system first selects a calibration action comprising a control signal for actuating a control surface of the EMV. Then, using a calibration model comprising a machine learning model trained based on one or more previous calibration actions taken by the EMV, the system predicts a response of the control surface to the control signal of the calibration action. After the EMV executes the control signal to perform the calibration action, the EMV system monitors the actual response of the control signal and uses that to update the calibration model based on a comparison between the predicted and monitored states of the control surface.

The invention relates to the technical field of power system operation and control, in particular to a safety operation and maintenance scheduling device and method for a wind, light and storage integrated system, and the device comprises a field sensing unit which is used for collecting the real-time operation data of wind, light and storage equipment; the edge calculation and autonomous control unit is used for local data processing, short-time prediction and emergency control when communication is abnormal or a safety index is out of limit; the central optimization and safety management platform is used for fusing and standardizing the data, evaluating the safety state of the system based on a double-domain safety redundancy index, evaluating energy storage life consumption through a coupling model, generating a scheduling instruction considering economical efficiency, safety and life by adopting opportunity constraint optimization, and sending the scheduling instruction to the central optimization and safety management platform; security verification and stable execution of the instruction are guaranteed through shadow simulation, strategy progressive switching and a security instruction shielding module; and the man-machine interaction application unit is used for visual monitoring, strategy recommendation and history redisk.

The application discloses a non-invasive brain-computer interface rehabilitation robot and relates to the technical field of intelligent rehabilitation equipment, and solves the technical problem that the existing rehabilitation robot mainly adopts a set program to perform repetitive rehabilitation training, and patients are not actively involved, the passive training mode limits the autonomous control and participation of the patients, and the rehabilitation effect is limited, especially in the aspect of neural injury repair, and the effect is poor; the application obtains motion data of the patient; an artificial intelligence model is trained based on historical motion data to obtain a motion recognition model; the motion data is recognized based on the motion recognition model to obtain a recognition result; the patient is subjected to rehabilitation training through the rehabilitation robot based on the recognition result; a rehabilitation effect evaluation coefficient of the patient is calculated based on the electromyographic characteristic data; and the recovery progress of the patient is judged based on the rehabilitation effect evaluation coefficient and a preset rehabilitation effect evaluation threshold value, so that the above technical problem is solved.

The invention discloses an unmanned aerial vehicle autonomous strike decision self-adaptive adjustment system based on multi-source information fusion, and particularly relates to the technical field of unmanned aerial vehicle autonomous control and self-adaptive decision, the system comprises an information fusion and decision inner ring and a meta-cognitive self-adaptive adjustment outer ring, the inner ring executes multi-source information fusion and generates a strike decision, and the outer ring executes a meta-cognitive self-adaptive adjustment. And the outer ring evaluates the environment and information quality in real time, drives the adjustment strategy generator based on reinforcement learning by comparing the expected efficiency and the actual efficiency of the decision, and dynamically adjusts the fusion weight and the decision parameter of the inner ring or switches the decision model. The problems that an existing system is poor in environmental adaptability and cannot achieve online self-optimization are solved, autonomous, closed-loop and continuous optimization of decision-making performance in a dynamic battlefield environment is achieved, and the autonomous combat effectiveness, the environmental adaptability and the task robustness of the unmanned aerial vehicle are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses an unmanned aerial vehicle cluster formation control method and system based on a multi-level intelligent algorithm, and the method comprises the steps: obtaining the external environment monitoring data and motion state data of an unmanned aerial vehicle cluster, constructing a multi-target comprehensive cost function, and carrying out the optimization of a cluster formation initial track, obtaining an optimal trajectory set meeting dynamic constraints; executing collision risk assessment on the sampling points on the optimal trajectory set, and outputting a trajectory safety judgment result; performing mathematical characterization and eigenvalue modulation on the formation topological structure through a Laplacian matrix, and generating target formation parameters adapted to environmental constraints; and finally, converting the optimal track set and the target formation parameters into a bottom layer flight control instruction, driving the unmanned aerial vehicle to execute a flight action, and collecting real-time motion state data at the same time. Therefore, the problems that in the prior art, trajectory optimization convergence is slow, collision detection real-time performance is poor, formation dynamic adaptation is insufficient, and system module collaboration is weak are solved.

The invention provides a high-performance WebGL industrial-grade three-dimensional visualization engine, a mixed rendering framework is constructed through fusion of Babylon.js and Three.js, 60FPS scene graph management is supported, and physical rendering is realized by integrating a PBR material and HDR illumination. The core innovation comprises an intelligent function module (ray selection, dynamic explicit and implicit, layer control, space sectioning, real-time point measurement and collaborative annotation); a rapid retrieval method of semantic rule injection and Hash indexing; a real-time synchronous annotation associated with the instruction difference and the father node; and batch explicit-implicit control of multi-dimensional rule compilation. A decoupling architecture (an application layer, a core layer, a base layer and a communication layer) is adopted, and modular expansion is supported. The technical breakthrough solves the problems of model bearing, rendering efficiency and function customization in an industrial scene, realizes real-time interaction and accurate analysis of a complex model, promotes digital twinning intelligent application, and has the advantages of autonomous controllability and light weight.

The invention discloses an unmanned ship autonomous control method and system based on a standard world model with a body, and relates to the technical field of intelligent ships and autonomous control. The method comprises the following steps: receiving and analyzing a natural language task instruction; driving a standard automaton to run based on the task target, the constraint condition and the maritime affair rule, and generating a standard embedding vector; constructing a body specification world model, and combining the physical hidden state of the multi-source observation code with the specification embedding vector to form a unified body specification hidden state; the hidden state is used as input, a control strategy is solved by adopting a safety reinforcement learning algorithm, and the algorithm strictly meets a safety threshold value while maximizing long-term task return by converting a maritime rule into an independent constraint cost function and performing Lagrange optimization; and finally executing a control instruction and updating the model and the automaton in a closed loop manner. According to the method, standard knowledge is deeply embedded into a world model and a decision process, and safe, efficient and explainable autonomous control of the unmanned ship in a complex maritime environment is realized.

The invention relates to the technical field of medical robots and image guidance, and discloses a target area operation method, device, equipment and medium based on a perceptual image, and the method comprises the steps: obtaining the perceptual image data of a target area, recognizing the boundary of the target area based on the perceptual image data, and planning and generating an operation path according to the boundary of the target area, and controlling the execution component to execute operation along the operation path, acquiring a contact feedback signal of the operation tool in the execution process, adjusting the operation state of the execution component, detecting interference information of the execution component in real time, dynamically correcting the operation path according to a detection result, and realizing autonomous perception and operation control of the target area. According to the method, a closed-loop autonomous control mechanism from sensing to operation is constructed by fusing sensing image recognition, path planning, execution control and real-time feedback correction processes, and accurate recognition and path adaptive adjustment of a target area in a dynamic environment are realized.

The invention relates to the technical field of autonomous control of unmanned vehicles, in particular to an autonomous obstacle avoidance method for an outdoor unmanned vehicle, which comprises the following steps of: acquiring multi-modal sensing information of traffic participants, processing the multi-modal sensing information, and generating potential intention vectors representing internal motivations of the traffic participants; based on the potential intention vector, for each candidate behavior in the candidate behavior set, predicting future state information of the traffic participant and future intention information of the traffic participant; and performing second social negotiation value evaluation on the future state information of the traffic participant and the future intention information of the traffic participant, and determining a driving behavior based on an evaluation result. The method has the beneficial effects that the normal form transformation from passive avoidance to active social game is realized, and the decision-making intelligence, behavior personification and driving safety of the unmanned vehicle in a complex interaction scene are remarkably improved.

The invention provides a rotary waste heat drying furnace system, and relates to the field of drying furnaces, the rotary waste heat drying furnace system comprises an outer furnace body, a rotary inner cylinder, a rotary driving device and a feeding device, the rotary inner cylinder, the rotary driving device and the feeding device are arranged in the outer furnace body, a discharging hopper is hermetically connected with the discharging end of the outer furnace body, a hot air inlet is formed in the upper part of the discharging hopper, and the discharging end of the rotary inner cylinder is communicated with the discharging hopper; a hot air outlet is formed in the feeding side of the outer furnace body; the air inlet detection device is used for detecting air inlet parameters of the hot air inlet; the air outlet detection device is used for detecting air outlet parameters of the hot air outlet; the air inlet parameter adjusting device is used for adjusting air inlet parameters of the hot air inlet. The discharging detection device is used for detecting the water content of materials discharged by the rotary waste heat drying furnace; the control system is electrically connected with the rotation driving device, the feeding device, the air inlet detection device, the air outlet detection device, the air inlet parameter adjusting device and the discharging detection device. The system accurately collects multi-dimensional data, intelligently quantifies efficiency loss, gets rid of experience dependence and realizes autonomous regulation and control.

This invention discloses an integrated assembly structure and operating method for a multi-rotor unmanned aerial vehicle (UAV) used for rescue reconnaissance. The structure includes: a main frame unit, a hierarchical integration unit, a mission execution unit, and an autonomous control and coordination unit. The main frame unit adopts an integrated structure to support various functional modules and ensure flight and landing stability. The hierarchical integration unit employs a layered layout design to achieve efficient integration and interference isolation of various functional modules. The mission execution unit includes a payload delivery mechanism for reliable mounting and precise release of standard rescue payloads. The autonomous control and coordination unit enables autonomous execution of the entire "identification-deployment-return" process, establishing a collaborative link between structure and function through algorithm deployment and protocol adaptation. This invention features a compact structure, convenient assembly and disassembly, high integration of core modules, and stable support for autonomous deployment and target identification tasks in rescue reconnaissance, demonstrating strong adaptability and practicality.

An autonomous control system combines sensor data from multiple sensors to simulate sensor data from high-capacity sensors. The sensor data contains information related to physical environments surrounding vehicles for autonomous guidance. For example, the sensor data may be in the form of images that visually capture scenes of the surrounding environment, geo-location of the vehicles, and the like. The autonomous control system simulates high-capacity sensor data of the physical environment from replacement sensors that may each have lower capacity than high-capacity sensors. The high-capacity sensor data may be simulated via one or more neural network models. The autonomous control system performs various detection and control algorithms on the simulated sensor data to guide the vehicle autonomously.

The application relates to the technical field of unmanned aerial vehicle autonomous control, in particular to a method for unmanned aerial vehicle route intelligent planning and obstacle avoidance based on deep learning, which comprises the following steps: environment sensing and multi-scale feature fusion, data collection through an unmanned aerial vehicle on-board multi-sensor, input of specific convolutional neural network, generation of a dense grid map containing static obstacle elevation information and dynamic obstacle predicted trajectory probability, initial path generation, weighted fusion of obstacle information, construction of a cost function, generation of a flyable initial route by an improved A algorithm, closed-loop deep reinforcement learning control, realization of route tracking and dynamic obstacle avoidance by an Actor-Critic network, and online meta-reinforcement learning for adjusting strategies by a meta-learner to realize self-adaptation. The application improves the environment sensing precision of the unmanned aerial vehicle, the controllability and stability of the route and the environment adaptability, and is suitable for complex dynamic environments.

The invention belongs to the technical field of unmanned aerial vehicle autonomous control, and relates to a multi-source information-based unmanned aerial vehicle autonomous return flight control method in an interference environment, which comprises the following steps of: constructing a navigation credibility time sequence evaluation set by acquiring original observation data from a multi-source sensor and an unmanned aerial vehicle self-inspection motion state in real time so as to carry out uncertainty quantification; generating a confidence space boundary representing a potential deviation range of the position and the speed of the unmanned aerial vehicle in a future preset time window, adaptively constructing a return channel of the unmanned aerial vehicle in combination with the position of a return target point and real-time environment obstacle information, and integrating the motion law of the unmanned aerial vehicle to set a target function of which the weight is dynamically adjusted by an uncertainty quantification result; and solving a return flight control instruction sequence taking a future preset time window as an optimized time domain, and performing loop execution until return flight, thereby realizing adaptive cooperation of a return flight channel and a control strategy in an interference environment, and facilitating improvement of environment adaptability and task reliability of a return flight process of the unmanned aerial vehicle in a complex interference scene.

The present application relates to the field of aerospace technology, in particular to a kind of spacecraft autonomous control system, including autonomous navigation and return module, autonomous docking module and close-range autonomous rendezvous docking module, by setting autonomous navigation and return module, autonomous docking module and close-range autonomous rendezvous docking module, to realize the full-automatic intelligent navigation of spacecraft in the process of space flight and the autonomous docking of spacecraft and space station, further not need manual intervention, by the autonomous control of spacecraft in the process of space flight, to greatly shorten the working time of astronauts in the small space of spacecraft, improve the work efficiency of astronauts, further solve the technical problems that the control system of existing spacecraft cannot realize automatic control, need manual intervention, to increase the working time of astronauts in the small space of spacecraft, reduce the work efficiency of astronauts.

Disclosed is a safety shared control system based on performance of teleoperator, including a master teleoperation system, a slave robotic manipulator system and a communication module; where the master teleoperation system includes EEG signal measurement of a teleoperator, hand controller operation input, and upper computer software, and the upper computer software includes a graphical user interface (GUI), safety simulation for protecting the safety of a robot, and a PoT model; and the slave robotic manipulator system includes a robotic manipulator, a vision camera and lower computer software, and the lower computer software includes a target recognition algorithm, an autonomous controller, and a shared controller for dynamically allocating human-robot control weights. A safety control system coefficient of the teleoperator is identified through safety simulation prior to actual operation, such that a safety control method for different operating experience is implemented, and safety operation of a teleoperation robot is realized.

The invention discloses a control system and method for an oral surgery robot, and relates to the technical field of medical robot control, and the method comprises the steps: collecting preoperative medical image data of a laryngeal cavity region, and generating a static laryngeal cavity three-dimensional structural body through anatomical segmentation, contour extraction and three-dimensional reconstruction; according to the static laryngeal cavity three-dimensional structural body, zero calibration is carried out on the oral surgical robot, and RCM constraints are established; under the constraint of RCM, a three-dimensional endoscope is used for collecting an operation area image in real time, a real-time laryngeal cavity local three-dimensional point cloud is generated through stereoscopic vision reconstruction, geometric registration is conducted on the real-time laryngeal cavity local three-dimensional point cloud and a static laryngeal cavity three-dimensional structural body, and the real-time tail end pose is determined; and the primary control quantity and the three types of real-time monitoring data are fused together, a comprehensive risk index is calculated, the autonomous control proportion of the robot is adjusted by using the comprehensive risk index, and a risk guidance control quantity is generated. The safety, accuracy and flexibility of the operation are improved, and finally more efficient and safer oral operation is achieved.

The application discloses a dual-arm robot autonomous control system and method based on remote operation and visual features, comprising a remote operation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a trajectory deviation detection and takeover module and a hybrid control interface module. The application completes human demonstration through remote operation, and acquires multi-modal information such as images, trajectories, grippers and muscle activations. The perception features are fused by using multi-view space-time alignment and cross-view attention mechanism, and strategy learning is completed in combination with an end-to-end large model. In the autonomous operation stage, the risk of current operation is evaluated through trajectory deviation detection and collision probability prediction, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the overall system are improved, a robot control mode of seamless switching of human-machine cooperation, autonomy and remote operation is realized, and the application is suitable for object operation tasks in complex and high-variable environments.

A method for aircraft trajectory planning includes computing an updated global flight trajectory for an aircraft from a current position to a second location using an objective function. The computing system repeats computes the data corresponding to the updated global flight trajectory at a frequency no less than fifty millihertz as the aircraft flies towards the second location. The method also includes computing autonomous control instructions for the aircraft to fly along the updated global flight trajectory.

The invention discloses an important data error correction method suitable for an aerospace power supply and distribution single machine. According to the method, the Hamming code principle and the binary coding principle are utilized, and the characteristics of a microcontroller of the aerospace power supply and distribution stand-alone are combined, so that an error correction coding and decoding method is designed. According to the method, the single-machine micro-control system can correct a certain degree of data bit flipping errors under the condition that the data bit flipping errors occur under the influence of space environments such as space single particles, errors of important data caused by the space environments are reduced, and then part of autonomous control operations threatening spacecraft safety are prevented from occurring.