8 results about "Terrain classification" patented technology

The application provides a terrain recognition method, device and equipment for a legged robot and a medium. The method comprises the following steps: acquiring pose data collected by a body sensor of a target legged robot, and calculating state parameters of the target legged robot; inputting the pose data and the state parameters into a foot-ground contact force model to determine three-dimensional foot-ground contact force; generating a contact mask sequence representing a foot-end ground contact state of the target legged robot based on the three-dimensional foot-ground contact force, and segmenting the pose data to obtain a segmented pose data set; inputting the pose data set into a pre-trained terrain classification model to determine a confidence vector of the current ground under multiple terrain attributes, and determining a terrain category of the current ground according to multiple confidence vectors. Through the method and device, the autonomous adaptation capability and environmental perception reliability of the legged robot in complex terrain are significantly improved.

The application relates to a photovoltaic pile driver terrain identification method and system, a terminal and a storage medium, and relates to the technical field of intelligent equipment. The application comprises the following steps: analyzing camera pixel data, laser radar data, a camera internal parameter matrix, a camera radar external parameter matrix and a radar satellite external parameter matrix to determine pixel camera data, radar world data and camera world data; fusing the radar world data and the camera world data to determine simultaneous space fusion data and coordinate point gray values; analyzing the coordinate point gray values and the simultaneous space fusion data to determine spatial terrain features and pile driving terrain classification; and analyzing the radar world data, the camera world data, the spatial terrain features and the pile driving terrain classification to determine real-time terrain modeling. The application has the effects of improving the photovoltaic pile driving terrain identification accuracy and terrain updating efficiency.

This invention discloses a spatial downscaling method for weather forecast models based on a terrain classification super-resolution model. The method first obtains nested data at each layer through dynamic downscaling of the weather forecast model, then trains a super-resolution model using data at different resolutions of the target area. Following the above process, super-resolution models are established for different terrains, and a terrain classification model is trained using terrain data. The super-resolution model and the terrain classification model are combined to obtain a fused prediction result. This fused model is the SRBTC model, which considers spatial correlation. The model outputs the similarity probability between the area to be predicted and various different terrains. The prediction results are weighted and summed to obtain the fused prediction result of the super-resolution model. During model training, a specified value scaling method is proposed to consider the differences between high- and low-resolution meteorological simulation data. When applying the model, a splitting and merging method is proposed to consider the differences between multi-scale simulation data, and the applicability of the model is specified.

Disclosed is a robot perception based terrain recognition and gait control method, system, medium, and device. The method includes: acquiring pitch angle data and foot force data of a bipedal robot based on optimal foot forces; inputting the pitch angle data and the foot force data into a trained k-nearest neighbor (KNN) model to recognize a terrain on which the bipedal robot is currently walking, and obtaining a terrain recognition result; a training process of the KNN model including: driving the bipedal robot to pass through a rough terrain and a flat terrain with a fixed step frequency gait, inputting collected pitch angle sample data and foot force sample data from different terrains into the KNN model, and using the KNN model to perform terrain classification; receiving the terrain recognition result, and adjusting step frequency and gait based on the terrain recognition result according to a preset gait control strategy.

A computerised system for environment-based accident, incident, or risk detection for human subjects in outdoor environments comprises at least one user device configured to be carried or worn by a human subject. The at least one user device comprises a location determination module; a movement detection module comprising at least one of an accelerometer, gyroscope, or other motion sensor; and a communication module configured to transmit data via non- cellular-dependent protocols. The system further comprises a central server comprising at least one processor and memory. The central server is configured to receive, from the at least one user device, subject-specific data comprising: current location data indicating a current location of the human subject in an outdoor environment; and movement data relating to the human subject, including at least one of: speed, direction, acceleration, or stationary duration. The central server is configured to receive or access environment-specific data indicating environmental conditions for the outdoor environment. Said environment-specific data comprises terrain characteristic data indicating terrain characteristics, said terrain characteristic data comprising one or more of: geospatial data including topographical and geographical features; terrain classification data. Said environment-specific data further comprises meteorological data indicating localised weather conditions for the outdoor environment including at least one of: temperature, precipitation, wind, or visibility. The central server is configured to generate a digital geospatial representation of the outdoor environment, comprising: a plurality of geospatial polygons defining one or more active monitoring zones and one or more non-active monitoring zones; wherein active monitoring zones designate outdoor areas where accident, incident or risk detection monitoring is enabled; and wherein non-active monitoring zones designate areas where accident, incident or risk detection monitoring is suspended or not enabled. The central server is configured to determine, for the current location of the human subject, whether the location falls within an active monitoring zone or a non-active monitoring zone by performing geospatial polygon intersection analysis. If the location falls within an active monitoring zone, the central server is configured to perform behavioral anomaly analysis comprising: retrieving historical movement data for the human subject from a historical movement database, said historical movement data comprising prior movement data of the human subject under the same or similar terrain characteristics and the same or similar weather conditions as those indicated by the environment-specific data; calculating expected movement data for the human subject at the current location based on: (i) the historical movement data, (ii) the terrain characteristics indicated by the environment- specific data, and (iii) the weather conditions indicated by the environment-specific data; and comparing the movement data of the human subject against the expected movement data to generate an anomaly score. If the anomaly score exceeds a predetermined threshold, the central server is configured to generate an output alert signal to at least one recipient device.

This invention proposes a terrain classification method based on SVM hard sample mining and CNN weighted training, belonging to the field of robot environmental perception technology. The method includes the following steps: collecting multimodal monitoring data of a robot moving on various terrains; preprocessing the multimodal monitoring data to generate multimodal feature vectors at different times; and forming a training sample set from these multimodal feature vectors. This invention uses a support vector machine to perform prior difficulty assessment on the training samples, quantifying the distance from the sample to the classification decision boundary as a difficulty index, and generating dynamic training weights accordingly. Then, during the training of the convolutional neural network, these weights are used as weighting factors to construct a loss function, allowing the model optimization process to focus on learning the features of samples with higher classification difficulty, thereby effectively improving the system's accuracy in overall terrain classification and the model's robustness in real-world complex scenarios.

This invention provides a land classification method and apparatus based on space-frequency coordination and cross-modal fusion. The method includes: acquiring cross-modal remote sensing image data to be classified; preprocessing and classifying the cross-modal remote sensing image data to obtain first modality data and second modality data; inputting the first modality data and second modality data into a trained land classification model, and predicting the land classification result based on the model. The land classification model includes: a first encoder, a second encoder, and a decoder; at each level of the first encoder and the second encoder, a semantic feature focusing module (SFFM) and a cross-modal feature fusion module (CCMF) are embedded, respectively. The method provided in this application effectively improves the accuracy of land classification.

This invention relates to the field of robotics, and more particularly to a dynamic task allocation system for a wheeled-legged hybrid robot based on model predictive control. The system includes: an environmental perception and terrain classification module, which acquires 3D environmental information through the fusion of stereo vision and LiDAR, and generates a structured environmental description containing traversable areas, obstacle locations, and terrain categories based on a point cloud segmentation algorithm; an online parameter identification and state estimation module, which estimates robot dynamic parameters and outputs the system state using an extended Kalman filter; a task decision and energy management module, which generates motion sequences and configures optimization weights based on a fuzzy inference system; and a multi-objective predictive controller, which constructs a cost function based on a dynamic model and solves for the first element of the control sequence using a sequential quadratic programming algorithm. This invention enables dynamic behavior optimization under complex terrain and variable task requirements, effectively balancing motion performance and energy consumption, and improving the robot's autonomous adaptability and task sustainability.