13 results about "Environmental modeling" patented technology

The invention relates to the technical field of unmanned aerial vehicle automatic inspection, provides an unmanned aerial vehicle take-off and landing platform rapid deployment and path planning method for a building curtain wall, constructs a gradient risk field model fusing geometric structures, environmental risks, visual perception and task regulations, and realizes unified quantitative evaluation of operation environmental risks. And based on the risk field, generating a global safe energy-saving path on the curved surface of the curtain wall through mixed search and path smoothing optimization guided by a risk navigation potential function. Through real-time updating of a local risk field and model prediction control, autonomous obstacle avoidance and smooth return flight of the unmanned aerial vehicle to sudden obstacles are realized. Meanwhile, a dynamic scheduling mechanism considering multi-target cost is introduced, and collaborative optimization of multiple unmanned aerial vehicles and take-off and landing platform resources is achieved. According to the method, full-link closed loop from environment modeling, global planning, real-time control to system scheduling is realized, and the safety, continuity and overall operation efficiency of automatic routing inspection of the super high-rise building curtain wall are improved.

This invention discloses a multi-objective path planning method and system for unmanned surface vessels (USVs) in complex marine environments, belonging to the field of USV path planning technology. The method includes the following steps: environmental modeling is performed using a voxelized raster method based on a terrain grayscale map; distance transformation is used to calculate the Euclidean distance from each free-space grid to the nearest obstacle, providing a quantitative basis for path safety assessment; an improved multi-objective particle swarm optimization method is used to complete a global path search under safety constraints; a three-dimensional constraint dual-level processing mechanism is designed to process the initial path solution set, generating a set of paths to be selected; based on the designed cost function and according to the Pareto dominance principle, the generated set of paths to be selected is filtered to obtain the Pareto front. This invention considers both path length and path safety, and provides a wider range of random paths for multi-objective path planning, offering more and more reasonable path selections.

This invention belongs to the technical field of agricultural environmental control and UAV applications, and relates to a UAV-based microclimate precision control system for forest understory cultivation areas. The system includes: an environmental modeling and initialization module, a prediction and decision-making module, a command execution and observation module, a data verification and extraction module, an error calculation and correction source term generation module, a correction source term propagation and field update module, and a closed-loop iterative scheduling module. By constructing a three-dimensional voxel grid and generating an initial microclimate prediction field, identifying target voxels based on prediction trends and generating predictive control frames, the UAV executes intervention actions and collects observation data. After verification and error calculation, correction source terms are generated. The microclimate prediction field is updated based on predicted airflow propagation, achieving closed-loop iterative control. This invention solves the problems of lagging control and insufficient intervention targeting caused by the lack of dynamic prediction of three-dimensional microclimate fields and digital twin linkage capabilities in existing fixed sensor networks and traditional UAV operation methods.

This invention discloses an adaptive compliant force control method for robotic arms based on reinforcement learning, belonging to the fields of robotics and automation. This method is based on an integrated framework of "reinforcement learning-adaptive compliant force control" for a robotic arm-dexterous hand-six-dimensional force / torque sensor system. It aims to completely eliminate existing problems such as the need for manual parameter tuning during model changes, difficulties in contact dynamics modeling, model parameter estimation, and the frequent need to prove the stability of adaptive laws through intelligent control strategies. It achieves self-learning and self-adaptation of upper-level task planning and lower-level compliant force / position control. After deployment, no repeated manual parameter tuning is required for different working conditions. The same control strategy maintains a high assembly success rate and low peak contact force for workpieces with small gaps, easy deformation, and multiple materials, eliminating bottlenecks in manual calibration, environmental modeling, and online parameter estimation, and enabling rapid "zero-downtime" switching in flexible production lines.

This invention provides a multi-machine collaborative scheduling system for a human-machine controlled container, belonging to the field of multi-machine collaborative scheduling and control technology. The system includes: a task semantic understanding and planning module; an environmental feature intelligent classification and surface modeling module connected to the task semantic understanding and planning module; a task semantic enhancement and risk assessment module connected to the environmental feature intelligent classification and surface modeling module; and a multi-machine collaborative decision-making module connected to the task semantic enhancement and risk assessment module. This invention achieves this by closely cooperating through task semantic understanding, environmental modeling, risk assessment, and decision-making functions to form an organic whole. Under the unified scheduling of the system, different types of machines can efficiently and collaboratively complete complex tasks, breaking the limitations of isolated module operation in traditional systems and meeting the stringent requirements for multi-machine collaborative operations in multiple fields and scenarios.

This patent application introduces a novel and comprehensive method for assessing novelty in quantum physics and related technical fields using the McGinty Equation (MEQ). The McGinty Equation, represented as Ψ(xt)=ΨQFT(xt)+ΨFractal(xtDmqs)+ΨGravity(xtG), uniquely integrates quantum field theory (QFT), fractal geometry, and gravitational theory into a unified mathematical framework. This integration enables the equation to capture the complex interactions and behaviors of particles and fields at quantum scales, offering a versatile tool for a broad spectrum of scientific and technological applications. Key applications of the MEQ include advancements in quantum computing, material science, environmental modeling, energy generation, and medical imaging. The invention's novelty lies in its unique approach to amalgamating disparate physical theories, providing significant insights into the quantum realm and facilitating technological innovations.

The invention relates to a coastal area composite network stability and carbon neutralization capability cooperative improvement method, and belongs to the field of ecological informatics and environment modeling. According to the method, firstly, metabolism subjects are divided based on multi-stage land utilization data, artificial carbon emission and natural carbon sink of each metabolism subject are evaluated, a carbon metabolism path is determined according to the area difference of land types before and after land utilization change and the carbon income and expenditure level difference of unit area, a carbon metabolism network is constructed, and key regulation and control nodes are identified; and by simulating different development scenes, dynamically evaluating the change rule of the stability and the carbon neutralization capability of the composite network, and finally forming a space planning and policy intervention scheme for synergistically improving the stability and the carbon neutralization capability of the composite network. According to the method, the crossing from static evaluation to dynamic optimization regulation is realized, the perspectiveness and scientificity of management decision are effectively improved, and the method is of great significance to guarantee the sustainable development of the coastal area.

This invention relates to the field of robotics technology and discloses a large-model-driven method and system for navigation and autonomous path planning of a composite robot. The method includes: collecting environmental data through multimodal sensors; inputting the data into a visual-language large model (VLM) to construct a holistic environmental model containing geometric, semantic, and dynamic object information; parsing high-level natural language navigation instructions using the VLM and generating navigation target points based on the holistic environmental model; and finally, coordinating the robot's body movement trajectory and end effector action sequence based on the model and target points, and driving their execution. This invention, by introducing a large model for holistic environmental modeling and task parsing, solves the problems of weak environmental understanding and unnatural human-computer interaction in traditional robot navigation technologies. It achieves deep perception of complex dynamic environments and autonomous planning of high-level instructions, significantly improving the intelligence level and operational efficiency of composite robots in real-world scenarios.

This invention discloses a robot movement path planning method based on ant colony optimization algorithm. It includes the following steps: environmental modeling of the robot's movement space using a grid method to obtain a grid map, with grid coordinates representing the robot's starting and ending positions; generating an initial path on the grid map using a jump-point search algorithm, and calculating the non-uniform initial pheromone concentration distribution along the initial path; searching for the optimal path on the grid map using the ant colony optimization algorithm; eliminating redundant nodes on the optimal path and performing smoothing processing to obtain the robot's movement path. This invention improves convergence speed and search efficiency by using a non-uniform distribution of the initial pheromone concentration in the grid map, and shortens the planned path length by eliminating redundant nodes on the optimal path and performing smoothing processing.

This invention discloses a UAV gas inspection system and method based on dynamic route planning, relating to the field of gas inspection technology. The environmental modeling module acquires basic spatial data and building structure data within the inspection area, constructs a three-dimensional spatial environment model, identifies flyable spaces, and generates a flight space constraint model. The dynamic route planning module acquires a list of inspection targets, filters inspection targets based on the list, and generates a set of candidate inspection waypoints. Based on the candidate waypoint set and the flight space constraint model, a UAV inspection path is generated through coverage optimization and path cost optimization. The gas detection module collects and analyzes gas concentration data in real time, identifies abnormal concentrations, and triggers an alarm mechanism. The privacy protection module performs real-time analysis of the collected inspection images, identifies sensitive areas in the images, and automatically occludes or blurs these sensitive areas, achieving both gas safety detection and user privacy protection.

The invention discloses an AI-supported environment-friendly robot. According to the technical scheme, the AI-supported environment-friendly robot is characterized by comprising a data sensing module, an AI intelligent processing module, a task decision module, an execution control module, a remote interaction and cooperation module and a data storage and iteration module; the data sensing module is used for collecting multi-source environment data and robot operation state data; the AI intelligent processing module carries out fusion analysis, intelligent identification and environment modeling on the collected data; the task decision module generates a dynamic operation strategy based on an AI analysis result; the execution control module executes the operation strategy and corrects the action deviation; the remote interaction and cooperation module realizes man-machine interaction and multi-machine cooperation; the data storage and iteration module stores data and updates an algorithm model; according to the invention, the recognition accuracy is improved through AI-driven multi-source fusion.

This invention discloses a real-time wireless communication system for mine shafts, belonging to the field of wireless communication technology. It includes a distributed wireless communication node set, a sparse observation node set, an interference reference acquisition node, and a controller. The distributed wireless communication node set is deployed along the shaft depth to form a multi-hop wireless communication network and carries communication services including at least alarm services. By setting up a sparse observation node set within the shaft and introducing a structured electromagnetic interference reference sequence output by the interference reference acquisition node, the controller can construct and continuously update a shaft propagation parameter field that varies with depth. This allows key propagation characteristics such as propagation attenuation, delay spread, and interference intensity to have quantifiable, alignable, and reproducible expressions. Therefore, compared to schemes relying solely on fixed configurations or single-point empirical measurements, this system can more accurately reflect the differentiated propagation environment along the shaft depth and improve the effectiveness of environmental modeling.

In various examples, an environment visualization pipeline may determine whether to generate or otherwise enable a visualization using an environmental modeling pipeline that models an environment as a 3D bowl or using an environmental modeling pipeline that models the environment using some other 3D representation, such as a detected 3D surface topology. The determination may made based on various factors, such as ego-machine state, (e.g., one or more detected features indicative of a designated operational scenario, proximity to a detected object, speed of ego-machine, etc.), estimated image quality of a corresponding environment visualization, and / or other factors. Accordingly, an environment around an ego-machine, such as a vehicle, robot, and / or other type of object, may be visualized in systems such as parking visualization systems, Surround View Systems, and / or others.