20 results about "Collision probability" patented technology

The invention discloses a space target collision early warning method based on GPU parallel computing and a J2 perturbation item, aims to solve the problems of high computing complexity and poor real-time performance of existing CPU processing space target collision risk assessment, and provides a multi-stage screening method based on GPU parallel computing and the J2 perturbation item. The method comprises the following steps: reading a TLE and calculating near / distant points and a reference check point at a GPU (Graphics Processing Unit); combining according to C (N, 2) and screening according to epoch, height and geocentric distance; taking a check point state as an initial value in a check interval, and adopting single-precision J2-RK iterative screening to obtain events NJ2pre and tJ2pre; the events are screened by using double-precision J2-RK in front and back check sections, and minimum distances distJ2 and tJ2 are output; and finally, SGP4 / SDP4 is used for parallel actuarial calculation to obtain a minimum distance distDC, a relative speed, a collision probability and the like. The method gives consideration to real-time performance and precision, significantly reduces the O (n) calculation cost, and is suitable for large-scale in-orbit target early warning.

The application belongs to the technical field of robot control, and discloses a mobile robot dynamic obstacle avoidance planning system and method based on visual navigation, real-time motion parameters such as obstacle position, speed and acceleration are extracted based on pose semantic perception results, which are input into a social LSTM network together with obstacle semantic types and robot motion parameters, interaction behaviors such as avoidance and following among multiple obstacles are captured through a social pooling layer, the influence of robot motion on obstacle trajectory is quantified through an interaction modeling layer, and a trajectory probability distribution and a prediction confidence of 3-5 time steps are output, while a collision probability, a minimum collision time and a minimum control amount required for collision avoidance are accurately calculated; through a lightweight Transformer double-branch shared network, a pose solution and obstacle semantic segmentation share feature extraction backbones, and robot motion constraints are integrated to realize multi-task parallel output of single-frame data, and perception link delay is shortened.

The application relates to a satellite collision probability estimation method, device and readable storage medium. The satellite collision probability estimation method comprises the following steps: forward propagation of a non-Gaussian relative velocity probability density function to obtain a reference velocity histogram, so as to determine an initial parameter set of a Gaussian mixture model; every first preset time interval, a plurality of sets of distance residuals and angle residuals of a first satellite and a second satellite in a running state are updated, so as to update the parameter set of the Gaussian mixture model; every second preset time interval, the relative positions of the first satellite and the second satellite are updated, and the instantaneous collision volume of the first satellite and the second satellite is determined by using the separation axis theorem; the mean values of Gaussian components in the updated parameter set are substituted into a static spherical harmonic coefficient table for query, so as to obtain the relative position probability density of the first satellite and the second satellite; and the collision probability is obtained based on the instantaneous collision volume and the relative position probability density, so that fast and accurate estimation is realized.

The invention discloses a multi-form robot motion control system based on AI perception decision. The multi-form robot motion control system comprises a perception fusion module, a multi-mode prediction module, a form management module, a motion control module and a control optimization module. The invention relates to the technical field of robot intelligent control, in particular to a polymorphic robot motion control system based on AI perception decision-making, according to the scheme, discrete morphologies are linearly combined into unified hybrid dynamics through micro-hybrid weights, and a multi-modal predicted safety violation probability and entropy regularization learning morphological strategy are combined to obtain a multi-modal robot motion control system. Smooth low-frequency switching and continuous balancing of energy consumption and risks are achieved, and coupling with model prediction control is achieved; the multi-modal collision probability is merged into model prediction control cost and constraint, hybrid dynamics is adopted for solving, high-frequency whole-body inverse dynamics is mapped into constrained joint torque, safety filtering is combined, priority avoidance and smooth executable control are guaranteed, energy consumption is reduced, and the task success rate and safety are improved.

Some implementations described herein relate to a system for generating identifiers. The system receives, from a device, a request to generate a plurality of identifiers. The system determines, based on receiving the request and by performing a parameter locking operation, a compute region parameter associated and a timestamp parameter. The system generates, based on the compute region parameter and the timestamp parameter, a set of one or more identifiers of the plurality of identifiers. Respective portions of each identifier, of the set of one or more identifiers, may indicate the compute region parameter, a version parameter associated with the parameter locking operation, the timestamp parameter, a counter value parameter, and an action parameter. The system processes, using a scrambling technique, the set of one or more identifiers and provides the set of one or more identifiers to the device.

This invention provides a collision avoidance method and system for the reclaimer arm of a stacker-reclaimer, relating to the field of data processing technology. The method includes acquiring first, second, and third information; performing three-dimensional modeling based on the first information to obtain a three-dimensional model; simulating the stacker-reclaimer's operating scenario based on the second, third, and three-dimensional model to obtain the simulation results; obtaining fourth information; and obtaining a target collision avoidance scheme. This invention, through operating scenario simulation and risk simulation processing of the stacker-reclaimer, can pre-simulate operational conditions, accurately identify potential collision risks, effectively reduce the probability of collisions between the reclaimer arm and surrounding equipment, and utilizes a deep learning model for multi-objective optimization. The resulting target collision avoidance scheme not only avoids operational interruptions caused by frequent collision avoidance or accidental collisions but also enables the stacker-reclaimer to complete reclaiming tasks more smoothly and efficiently, reducing waiting time and operational adjustments, and improving operational efficiency.

Some implementations described herein relate to a system for generating identifiers. The system receives, from a device, a request to generate a plurality of identifiers. The system determines, based on receiving the request and by performing a parameter locking operation, a compute region parameter associated and a timestamp parameter. The system generates, based on the compute region parameter and the timestamp parameter, a set of one or more identifiers of the plurality of identifiers. Respective portions of each identifier, of the set of one or more identifiers, may indicate the compute region parameter, a version parameter associated with the parameter locking operation, the timestamp parameter, a counter value parameter, and an action parameter. The system processes, using a scrambling technique, the set of one or more identifiers and provides the set of one or more identifiers to the device.

A data transmission method and a device thereof are provided for tag access. The provided method includes: An answer device receives first signaling sent by a read / write device. The first signaling carries target flag information. The target flag information is used to determine the answer device. First flag information of the answer device matches the target flag information. The answer device determines a first slot, and sends first access information to the read / write device in the first slot. In the provided method, the first slot is determined, to avoid collision of different slots in a same slot in a next round, reduce a collision probability, and improve tag access efficiency.

A collision avoidance system (1) for a ground vehicle is described, comprising a first transceiver device (4) associated with an obstacle (B), configured to generate and transmit one or more wireless signals (SG_B_i) at a pre-established time interval, the first wireless signal (SG_B_1) carrying an obstacle identification code (ID_1); a second transceiver device (3) associated with the ground vehicle (A), configured to transmit one or more wireless signals (SG_A_i) and receive the one or more wireless signals (SG_B_i) including the first wireless signal (SG_B_1) carrying the obstacle identification code (ID_1); a processing unit (20) configured to process collision data of the vehicle (A), comprising: a first calculation module (21) configured to determine, from the wireless signals (SG_B_i) and (SG_A_i), a trajectory (D_TRJ) of the ground vehicle (A) and the obstacle (B); a second calculation module (23) configured to determine, based on the trajectory (D_TRJ), a collision probability between the ground vehicle (A) and the obstacle (B); an alert module (24) configured to generate and send a collision probability signal between the ground vehicle (A) and the obstacle (B) according to a high collision probability between the ground vehicle (A) and the obstacle (B). The invention further describes a corresponding method and computer program.

The application discloses a kind of space debris on-orbit real-time autonomous avoidance method, system and spacecraft, belong to spacecraft on-orbit safety control technical field.The method is solidified and burned in on-board chip by light-weight AI model of knowledge distillation, structured pruning and 8-bit quantization;Adopt "standard strategy library+AI fine-tuning" architecture, call pre-stored orbit lag / advance / out-of-plane maneuver template, generate candidate trajectory by on-orbit fine-tuning of template parameter by solidified AI;Safety check is carried out based on simplified dynamics model, to ensure that meet off-target distance, fuel and attitude stability constraint;Build double-threshold emergency redundancy mechanism, select fuel optimal trajectory generation instruction execution in normal mode, when collision probability is too high or chip is abnormal, immediately call pre-stored emergency instruction sequence, directly execute after being quickly analyzed by propulsion controller.The application shortens response time from hour level to minute level, significantly improves the autonomous survival ability and mission reliability of spacecraft under extreme working conditions.

This invention relates to the field of space optimization technology and discloses a method for optimizing container loading space based on robot collaboration. This method constructs a multi-dimensional physical model of the package by acquiring point cloud data and weight distribution. A loading scheme including target pose, pressure tolerance threshold, and loading sequence is generated based on a reinforcement learning algorithm. The robotic arm generates motor current-position composite control commands based on a pre-trained grasping dynamics model to achieve precise grasping. During loading, the package pose is monitored in real time using a Kalman filter algorithm. When the deviation exceeds a threshold, a local spatial KD tree is constructed and the collision probability gradient field is calculated to generate a compensation scheme that satisfies stability constraints. Finally, the quadtree index is updated and fed back to the reinforcement learning algorithm for online optimization. This method effectively solves the efficiency decline problem caused by inaccurate grasping force control and accumulated execution deviations during robot loading, achieving efficient and precise automated loading.

This application relates to a method, apparatus, computer device, and storage medium for determining collision probability. The method includes: determining the number of collision detection operations based on model parameters in a discretized three-dimensional model of the propeller; performing collision detection operations based on the number of detections and recording the number of collision events; and determining the collision probability of an object colliding with the propeller based on the number of collision events and the number of detections. This quantitatively assesses the probability of an object colliding with the propeller, thereby determining the probability of the object passing through the propeller. Using this probability provides data support for the analysis of the rejection characteristics of aircraft propellers and the design of aircraft propellers, enabling researchers to design aircraft more accurately. This is of great significance for improving the overall resistance of aircraft to foreign object impacts and engine ingestion capabilities, and enhancing aircraft flight safety.

The invention relates to the technical field of computers, and discloses an electronic tag online detecting and marking method and system. The method comprises the following steps: modeling a software defined radio detection node as a cellular automaton cell; constructing a distributed non-cooperative game model based on neighborhood information, and optimizing conflict probability and energy consumption by taking frequency and power as strategy space; dynamically adjusting radio frequency parameters and broadcasting the state; executing self-organizing anti-conflict detection, and identifying effective tags through matching of signal intensity and timestamps; and generating and writing a unique mark instruction. The system is composed of a plurality of detection nodes with state sensing and game decision-making capabilities. According to the scheme, high throughput, low conflict rate, high robustness and energy efficiency optimization in an ultra-high density scene are realized.

The invention discloses a random access transmission method, a terminal and network side equipment, and belongs to the technical field of communication, and the random access transmission method comprises the steps that first equipment obtains first information based on an artificial intelligence (AI) model; performing random access channel (RACH) transmission based on the first information; wherein the first information comprises at least one of the following items: beam information, RACH collision probability, RACH resource information and a transmitting power parameter.

The invention discloses an improved MESO collision detection method based on collaborative robot joint torque real-time difference, and belongs to the field of robot collision detection. According to the method, firstly, through single-joint friction force identification, Stribeck model parameters capable of accurately fitting friction force characteristics of a low-speed area are obtained; in the offline stage, an improved extended state observer is adopted to estimate an external torque, the torque and the speed are differentiated, non-collision data sets and collision data sets are collected, the mean value and the covariance of the non-collision data sets and the collision data sets are counted, the mahalanobis distance of the two types of data sets is calculated to divide sub-domains, and then an index basis function coefficient is determined. In the online stage, the measurement quantity is obtained in real time, the mahalanobis distance and the primary function value of the current period are calculated, the current confidence coefficient is updated by combining the confidence coefficient of the previous period, and whether collision happens or not is judged by comparing the collision probability with the non-collision probability. According to the method, the problems of false detection caused by a joint reversing moment peak and external moment estimation noise, detection blind areas of a traditional threshold method and the like are effectively solved, and the accuracy and reliability of collision detection are remarkably improved.

The invention relates to the technical field of metal plate bending processing, in particular to a real-time collision detection method based on self-adaptive bounding boxes and machine learning, which comprises the following steps: firstly, carrying out geometric feature analysis on each three-dimensional model, and calculating the fitting degree between the volume of the model and the volumes of different bounding boxes (sphere, AABB and OBB); and adaptively selecting and constructing an optimal bounding box with the highest space utilization rate. In a real-time detection stage, aiming at an object pair to be detected, 24-dimensional geometrical relationship feature vectors between bounding boxes of the object pair to be detected are extracted, and the 24-dimensional geometrical relationship feature vectors are input into a pre-trained lightweight multilayer perceptron model. The model outputs continuous collision probability values, and three-level intelligent classification (high-probability collision, critical state and safety) is performed according to a preset threshold value, so that scheduling of accurate detection resources is guided. According to the method, the package tightness of the bounding box can be improved from the source, and the boundary condition is accurately pre-judged by utilizing machine learning, so that the efficiency of real-time collision detection is improved while the detection precision is ensured.

An exemplary embodiment of the present disclosure relates to a communication method, the method including: determining, at a first device, sounding reference signal (SRS) parameters used to determine one of a cyclic shift value and a comb offset value based at least in part on frame information; and performing SRS communication with a second device based on the SRS parameters. In this manner, different hopping values / patterns between different frames can reduce a collision probability between different frames.

The application relates to the technical field of path planning, in particular to an end-to-end real-time path planning method and system, which comprises the following steps: based on a global depth image, a preset ViT backbone network is used for feature extraction to obtain global features of an environment; target information and the global features are spliced to obtain spliced features, a key point sequence and a collision probability are obtained; and a path is planned according to the key point sequence. The global environment features are extracted through the ViT backbone network, and the spliced features are generated in combination with the target information, so that the planning network can simultaneously perceive the environment structure and the task intention, can adaptively focus on the navigation key area, and can thus exhibit stronger scene understanding capability in an unknown environment; the generated trajectory is more efficient and smoother, the control cost is significantly reduced, the robot shakes less in the movement process, the movement is more coherent and smooth, the navigation efficiency is improved, and the mechanical wear of the robot hardware is reduced.

This invention provides a collaborative collision avoidance method and system for multiple tower cranes based on digital twins and AI trajectory prediction. The method establishes a global digital twin model aligned with real-world coordinates; employs a Kalman filter algorithm that dynamically expands based on wind speed and boom vibration frequency to denoise multi-source operating data and drive scene reconstruction; utilizes network outputs with fused physical kinematics penalty terms to predict trajectories, constructs a nonlinearly expanding spatial probability distribution heatmap, and calculates a dynamic safety envelope threshold that amplifies exponentially with real-time dynamic load; when predicted trajectories overlap within the envelope and the collision probability exceeds the limit, priority is calculated based on angular momentum and tower height characteristics, guiding lower-priority tower cranes to actively avoid collisions along the steepest descent gradient vector; finally, scheduling instructions and other related data are generated into Merkle root hash values ​​and permanently stored on the blockchain. This application overcomes the shortcomings of traditional passive responses and static thresholds, achieving efficient, intelligent, and proactive collision avoidance and end-to-end tamper-proof traceability for multiple tower crane operations.