596 results about "Autonomous control" patented technology

The invention discloses a double-arm robot autonomous control system and method based on teleoperation and visual features. The system comprises a teleoperation 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 track deviation detection and takeover module and a hybrid control interface module. Human demonstration is completed through teleoperation, multi-modal information of images, tracks, clamping jaws and muscle activation is collected, perception features are fused by adopting multi-view space-time alignment and a cross-view attention mechanism, strategy learning is completed in combination with an end-to-end large model, and in the autonomous operation stage, the multi-view space-time alignment and the cross-view attention mechanism are combined with the end-to-end large model. The risk of current operation is predicted and evaluated through track deviation detection and collision probability, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the whole system are improved, the robot control mode of seamless switching between man-machine cooperation and autonomous and teleoperation is achieved, and the method is suitable for object control tasks in complex and highly variable environments.

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.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

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.

Systems and techniques are described for implementing autonomous control of earth-moving construction and / or mining vehicles, including to automatically determine and control autonomous movement of part or all of one or more such vehicles (e.g., a vehicle's arm(s) and / or attachment(s), such as a digging bucket, claw, hammer, blade, etc.) to move materials or perform other actions in a manner that is based at least in part on data from simulated operation of the vehicle(s). For example, the systems / techniques may include using data from simulated operation of the earth-moving vehicle(s) in various manners, such as for use in training one or more machine learning models that are used in implementing the autonomous operations, determining optimal or otherwise preferred hardware component configurations to use, determining optimal or otherwise preferred implementation plans to use for one or more tasks and / or multi-task jobs, enabling user what-if experimentation activities, etc.

The invention discloses a robot image-free visual target docking method and system based on multi-modal perception, and the method comprises the steps: carrying out the recognition calculation of a preset target in the image data of the environment in front of a robot through a target detection model YOLOv4-tiny, and constructing a robot control feedback quantity; radial distance information of an object in the environment is obtained, a scanning range is divided through a regional strategy, straight line fitting calculation is conducted, and a deviation evaluation value of the robot relative to the yaw angle is constructed; and the robot accumulative mileage is obtained, finite-state machine hierarchical decision making and mistaken stop prevention judgment are conducted in combination with the robot control feedback quantity and the robot relative yaw angle deviation evaluation value, and robot graph-free visual target docking is achieved. According to the invention, full-process autonomous control of the robot from target discovery, navigation on the way, obstacle avoidance to end fine alignment can be realized. The robot graph-free visual target docking method and system based on multi-modal perception can be widely applied to the technical field of graph-free navigation.

The invention discloses a power distribution control method and device for an optical storage DC flexible power distribution system, and relates to the technical field of power distribution control methods, and the method comprises the steps: determining a power consumption path of each power distribution region based on a power load and a photovoltaic power generation path of each power distribution region, and an energy storage path corresponding to the optical storage DC flexible power distribution system; and the power distribution balance event of each power distribution area is determined according to each power consumption path, the power distribution level of each power distribution area and the power distribution control relationship among the power distribution areas, so that the accuracy of the power distribution balance event of each power distribution area is improved. Therefore, the dynamic restoration event of the power distribution area is determined based on the autonomous management and control system and the abnormal power distribution position of the power distribution area, and the flexible regulation and control event of the optical storage direct-current flexible power distribution system is determined based on each dynamic restoration event, the working process of the optical storage direct-current flexible power distribution system and the flexible management and control path. And the autonomous flexible regulation and control effect of the optical storage direct-current flexible power distribution system is ensured.

The invention discloses an AI multi-agent layered hierarchical emergency response system supporting an elastic power grid, and relates to the technical field of substation power response, the AI multi-agent layered hierarchical emergency response system comprises a global situation awareness module, a regional autonomous control module and an equipment toughness execution module, the global situation awareness module integrates multi-source disaster monitoring information, establishes a disaster chain deduction model, and sends the disaster chain deduction model to the regional autonomous control module; quantifying regional disaster intensity and generating a risk map; the regional autonomous control module extracts a high-risk region to divide a balanced island, realizes second-level splitting and black start through an intelligent agent, and constructs an autonomous stability coefficient to evaluate the stability of the island; and the equipment toughness execution module identifies a potential fault area, drives an intelligent agent to dynamically reconstruct a power grid topology, links distributed energy to perform differential energy supplementation according to a load gap, and calculates a recovery speed coefficient to judge the core load recovery quality. The system improves disaster pre-judgment precision and emergency response efficiency, ensures reliable power supply of medical and industrial core loads, and enhances disaster resistance elasticity of a power grid.

The invention relates to the technical field of satellite attitude control, in particular to a satellite-borne laser semi-autonomous pointing control method, electronic equipment and a storage medium. The method is completed by cooperation of a ground system and an on-satellite system, the ground system calculates a top target passing moment T * in advance, generates a unified laser pointing control instruction and injects the instruction into a satellite; and the satellite-borne system receives and analyzes the instruction, obtains parameters such as T * and beam selection, and then autonomously controls a specified load for work preparation. And the satellite-borne system autonomously calculates the optimal moment T1 and the target attitude when passing through the calibration field through high-precision orbit extrapolation based on the latest navigation data, finally autonomously executes attitude maneuver and laser pointing tasks, and autonomously returns after the tasks are completed. Instructions in a uniform format are generated and injected from the ground, ground operation is greatly simplified, efficient cooperation of ground decision triggering and on-satellite autonomous execution is achieved, pointing errors caused by orbit extrapolation are greatly reduced, the pointing precision and the task success rate are improved, and universality is good.

The invention provides a micro-grid control method based on edge side AI control equipment and a program product, and relates to the field of power supply and consumption system control. The micro-grid control method comprises the following steps: deploying a lightweight large model on edge side control equipment, wherein the lightweight large model is obtained through compression based on a pre-selected universal large model; micro-grid operation demand information input in a natural language form is acquired by the lightweight large model, and a control target is analyzed from the micro-grid operation demand information; generating a control strategy according to the control target; and controlling the operation state of the micro-grid according to the control strategy. According to the scheme of the invention, the lightweight large model is deployed at the edge side control equipment, the microgrid operation demand information is obtained through a natural language, the convenience and flexibility of control are improved, and the control demand is better met. And a light-weight large model accurately generates a control target and a strategy, so that more accurate operation control is realized, the adaptive capability is enhanced, and intelligent autonomous control is realized.

The invention discloses a mixed cooling system multi-target parallel control method based on virtual-real migration. The method comprises the following steps: coupling characteristic analysis and cooperative control logic construction; a data-driven multi-step rapid prediction model is constructed and corrected online; and virtual-real migration feedback and multi-target parallel control optimization are carried out. Multi-step prediction is realized by constructing a multi-parameter coupled agent model and fusing an LSTM technology; and a virtual-real double-closed-loop collaborative optimization mechanism is constructed under an ACP parallel control framework, so that real-time self-adaption and multi-target rapid adjustment of control logic are realized. The invention further discloses a mixed cooling system multi-target parallel control system based on virtual-real migration. Compared with a traditional method, the technology breaks through the modeling bottleneck and realizes rapid prediction; virtual-real parallel control is initiated, and strong anti-interference and high maneuverability are achieved; carrying out online multi-target collaborative optimization; intelligent cooperative execution control; the application platform is efficiently verified; the dynamic performance is obviously improved; the comprehensive performance is excellent, and the system operation stability and the autonomous control level can be remarkably improved.

The invention relates to an intelligent sensing, predicting and tracking method for a dynamic target of an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle autonomous control and path planning. According to the method, a target trajectory is predicted through a Kalman filter, unmanned aerial vehicle dynamics is accurately modeled by using a physical information neural network, and an optimal control sequence of a future time domain is solved through online rolling optimization by taking target trajectory prediction and an unmanned aerial vehicle dynamics model as constraints based on a model prediction control framework. And an autonomous closed loop of perception-prediction-planning-execution-updating is formed. The method effectively solves the problems of environment uncertainty, calculation real-time performance, complex scene multi-target tradeoff and the like of an existing method, provides a high-precision and low-delay dynamic target intelligent sensing prediction tracking method, and remarkably improves the precision and efficiency of dynamic target tracking of an unmanned aerial vehicle in a complex environment.

The invention provides a multi-agent-based unmanned aerial vehicle autonomous flight control method and a server, and relates to the technical field of unmanned aerial vehicle control. The method comprises the following steps: receiving an original task instruction for the unmanned aerial vehicle input by a user by adopting a central scheduling agent; analyzing the original task instruction by adopting a central scheduling agent to generate a task execution scheme; a central scheduling agent is adopted, and according to the time sequence dependency relationship, instructions of the multiple sub-tasks are sequentially sent to the corresponding sub-agents respectively; a plurality of sub-agents are adopted, the sub-tasks are executed according to the instructions of the respective sub-tasks, execution results of the respective sub-tasks are obtained, and the execution results of the respective sub-tasks are fed back to the central scheduling agent; and adopting the central scheduling agent to generate a target task execution result according to the execution results of the plurality of sub-tasks, and feeding back the target task execution result to the user. The sensing and understanding capability of the unmanned aerial vehicle to a complex environment is remarkably enhanced, and the autonomous control level of the unmanned aerial vehicle is further improved.

The invention relates to the technical field of satellite attitude control, in particular to a satellite-borne laser full-autonomous pointing control method, electronic equipment and a storage medium. The method comprises the following steps: firstly, presetting the position of a ground fixed calibration field, a laser light emitting phase and pointing information on a satellite; the on-satellite system autonomously carries out long-term orbit extrapolation, judges whether a satellite is about to pass through a calibration field, calculates to obtain a rough target moment T * and a day and night state thereof, and autonomously notifies a ground user. And the satellite autonomously controls work preparation of laser and camera loads according to T *. And at the moment before T *, the satellite-borne system autonomously solves the optimal moment T1 and the target attitude of passing through the calibration field through iterative extrapolation calculation and preferential selection based on high-precision navigation data. And finally, the satellite autonomously maneuvers to a target attitude, laser is controlled to accurately point to the calibration field at the T1 moment, and the satellite autonomously returns after the task is completed. According to the method, on-satellite autonomy from task planning to execution of the whole process is realized, and the autonomy, the working efficiency and the pointing precision of the satellite are greatly improved.

Geophysical anomalies in an area or region can be detected, characterized, or otherwise mapped using a combination of environmental and geophysical sensor data acquired using a vehicle, which may be a remotely operated or autonomously controlled unmanned vehicle. Anomalies are detected by processing the environmental sensor data and geophysical sensor data using artificial intelligence algorithms, programs, or models. As an example, the artificial intelligence algorithms, programs, or models can include machine learning algorithms, programs, or models. The environmental sensor data are processed to generate control parameters for controlling the acquisition and / or processing of the geophysical sensor data optimized for environmental conditions in the area.

In some embodiments, techniques are provided for autonomously backing a vehicle (such as a Class 8 truck) to a trailer. In some embodiments, environment sensors such as image sensors and range sensors mounted to the vehicle detect a trailer and determine distances between the trailer and the vehicle as well as relative angles of the trailer and the vehicle. In some embodiments, the vehicle determines a path to the trailer based on this information, and autonomously controls braking, torque, and / or steering of the vehicle to autonomously back the vehicle along the determined path.

The invention discloses a multi-agent collaborative dynamic target interception decision-making method based on reinforcement learning. The method comprises the following steps: initializing an environment and agents; predicting the state of the intercepted target; distributing a global optimal task based on the predictive interception cost of the multi-agent; constructing an autonomous control model based on a near-end strategy optimization algorithm PPO for each agent, and designing a state representation vector of future collision perception; inputting the state representation vector into a neural network of the intelligent agent, and outputting an action instruction; designing a multi-objective reward function for guiding the intelligent agent to perform behavior learning in a training process; the intelligent agent provided by the invention has a higher-level strategy. Through finer state representation and more complex reward function design, the intelligent agent trained through PPO not only can accurately execute a pursuit task on a target, but also can learn advanced strategies such as autonomous obstacle avoidance, energy consumption reduction, efficiency improvement and the like.

The invention relates to the technical field of intelligent industry, and discloses a visual communication system based on weak current engineering and an operation method thereof, and the system comprises a multi-mode signal collection module, an intelligent data processing module, a dual-channel communication transmission module, an interactive visualization module and an autonomous control system module. The method comprises the following steps: synchronously acquiring video, audio and environmental parameters through a distributed sensor array; eliminating multi-source signal deviation by adopting a space-time alignment algorithm; feature fusion and quality evaluation are realized based on a deep neural network; dynamically selecting a wired and wireless hybrid transmission mode; an AR enhanced visual human-computer interface is constructed; and implementing a fault self-diagnosis and fault-tolerant recovery mechanism. According to the invention, the bit error rate of the communication system is reduced, the continuity and reliability of the communication process are ensured, and the man-machine interaction efficiency and the emergency response speed are improved.

The invention relates to the technical field of unmanned aerial vehicle autonomous control or cloud-side cooperative robot control, in particular to an unmanned aerial vehicle harmful gas distribution monitoring system based on a cloud platform, and the system comprises a communication quality analysis unit which generates a communication quality comprehensive index; the decision authorization migration unit generates a decision authorization factor; the cloud fusion updating unit generates a cloud planning waypoint; the airborne data processing unit generates airborne autonomous waypoints and data retention factors; the collaborative path planning unit is used for performing weighted fusion on the cloud planning waypoint and the airborne autonomous waypoint when the decision authorization factor is lower than an autonomous mode activation threshold value, and generating a final execution waypoint; when the decision authorization factor is greater than or equal to the autonomous mode activation threshold, setting the cloud planning waypoint as a final execution waypoint; according to the invention, the inherent contradiction between cloud centralized control and airborne autonomous detection is solved, and the overall toughness and efficient execution of a monitoring task are ensured.

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.

The invention relates to a construction site dynamic inspection system based on man-machine-dog cooperation and an autonomous control method, and relates to the technical field of intelligent construction and robot cooperative control, and the method comprises the following steps: S1, multi-modal data collection and fusion: through a fixed collection device and a dynamic inspection device arranged at a construction site, carrying out multi-modal data collection and fusion; static information and dynamic information of a construction site are collected at the same time, the static information comprises environment data, and the dynamic information comprises motion trails of objects, personnel and equipment in the construction environment. A cooperative control mechanism among the ground quadruped robot, the aerial unmanned aerial vehicle and the wearable terminal is constructed, and a space partition and path network is generated in combination with a building information model, so that comprehensive coverage of a multi-layer space and a complex structure area on a construction site can be realized; the problem that high-altitude blind areas and sheltered areas are difficult to inspect in a traditional scheme is effectively solved, and the space integrity and environment adaptability of inspection tasks are improved.

In order to solve the problems of insufficient compatibility, low compiling efficiency, weak management capability, limited expansibility and the like due to the fact that existing satellite information processing technologies are mostly designed for single satellites or satellites of the same type, the invention provides a differentiated information compiling management system and management method suitable for batch satellite clusters. Belongs to the technical field of satellite information processing and management. The system takes the ground management subsystem as a management control center; the on-satellite management subsystem is used for receiving and executing instructions and management contents from the ground management subsystem; the full-life-cycle standardization process is used for defining collaborative specifications of the ground management subsystem and the satellite-borne management subsystem in each stage of software from development, testing, issuing, on-orbit updating to decommissioning; wherein the ground management subsystem and the satellite-borne management subsystem cooperate through a whole life cycle standardized process to form a space-ground cooperative, autonomous and controllable closed-loop management ecology. The method is suitable for on-orbit management of large-scale different types of constellation satellite software.

The invention discloses a space robot compliance control method and device based on multi-dimensional visual constraints, and belongs to the technical field of space robots and autonomous control. The method comprises the following steps: collecting and fusing data through a heterogeneous visual sensor group, and carrying out uncertainty quantification to generate a perception confidence index; constructing and dynamically adjusting a visual constraint function set based on the index, solving a motion feasible region according to the visual constraint function set, and generating an expected motion track; inputting the track into an impedance controller, and adjusting the rigidity and damping parameters in real time according to the sensing confidence so as to output an instruction; the instruction is verified through digital twin simulation, and if the instruction does not pass the verification, a fault-tolerant mechanism is triggered for optimization; and finally, updating the system parameters on line according to the deviation between the actual execution data and the simulation data. According to the method, the technical problems of poor operation safety, low control precision and insufficient autonomy of the space robot in a complex environment caused by single sensing information dimension, lack of uncertainty quantization and fixed control strategy are solved.

The invention belongs to the technical field of unmanned aerial vehicle cluster autonomous control and path planning, and particularly discloses an unmanned aerial vehicle cluster three-dimensional collaborative coverage planning method based on multi-subgroup grey wolf optimization, which comprises the following steps: establishing a digital elevation map of a three-dimensional multi-source threat environment, and carrying out environment preprocessing by adopting dynamic Poisson disk sampling and a Voronoi diagram; based on the preprocessed environment, performing balanced task distribution of the unmanned aerial vehicle cluster by using a clustering strategy of sector migration; performing unmanned aerial vehicle cluster global coverage path planning by using an improved multi-subgroup grey wolf optimization algorithm according to a balanced task allocation result, and performing local path optimization based on an adaptive elastic window local greedy method; and performing unmanned aerial vehicle cluster communication and path smooth output based on differential communication to complete unmanned aerial vehicle cluster three-dimensional collaborative coverage planning. According to the invention, the problems of difficulty in reflecting a real working environment, serious track overlapping and non-uniform task load in the prior art are solved.

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).

The invention discloses a ground mobile robot path planning method and system, and relates to the technical field of intelligent robot autonomous control. The method comprises the following steps: constructing a grid map model of a robot moving environment; based on the grid map model, establishing a kinematic model of the robot and setting a movement rule, and initializing to generate a plurality of paths from a starting point to an ending point to form an initial path set; iteratively merging and optimizing the paths in the initial path set by adopting a group merging strategy, and screening out a current global optimal path; based on the current global optimal path guidance, a derivative path is updated based on an improved pigeon inspired optimization map and a compass operator, and a first-stage optimization set is obtained; and through iterative optimization of a landmark operator, dynamically calculating a landmark center according to a cost value and updating a path position to obtain a second-stage optimization set. And selecting an optimal path and carrying out connection smoothing processing to generate an optimal complete path. According to the method, the global optimal path can be efficiently and stably planned in a complex environment.

A method of autonomously maneuvering a vehicle using environment sensors comprises calculating first coordinate data based on information received from environment sensors mounted on a first portion of the vehicle; determining, based on the first coordinate data, a first target at a first location; determining a path to maneuver the vehicle to the first location; determining and transmitting commands to autonomously control the vehicle to maneuver to the first location; calculating second coordinate data based on information received from environment sensors mounted on a second portion of the vehicle; determining based on the second coordinate data, a second target at a second location, determining a path to maneuver the vehicle from the first location to the second location; and determining and transmitting commands to autonomously control the vehicle to maneuver from the first location to the second location. Suitably configured vehicles (e.g., tractor units) are also described.

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.