40407results about "Vehicle position/course/altitude control" patented technology

The invention provides an automatic obstacle avoidance point selection and obstacle avoidance method for a photovoltaic station polled by an unmanned aerial vehicle, and the method comprises the steps: carrying out the real-time analysis of the image definition through image processing for a polling image captured by the unmanned aerial vehicle carrying different sensors, carrying out the classification and recognition of an obstacle according to the high-low difference of the definition, and optimizing the detection process of the obstacle; under the condition of different obstacle recognition speeds, the flight speed and height of the unmanned aerial vehicle are dynamically adjusted to obtain higher patrol image definition and obstacle recognition efficiency; an obstacle is recognized from the inspection image, and a recognition result is used for updating an obstacle avoidance database in the flight of the unmanned aerial vehicle; according to the obstacle avoidance database updated in real time and the current position of the unmanned aerial vehicle, an optimal obstacle avoidance path is planned, and the optimal obstacle avoidance path is transmitted to the unmanned aerial vehicle in real time for execution; and when the optimal obstacle avoidance path is executed, the unmanned aerial vehicle system continuously monitors the change of the surrounding environment, and if a new obstacle or obstacle movement is detected, the obstacle avoidance path is adjusted in real time.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The present invention provides an automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway line, including an unmanned aerial vehicle (UAV), a movable ground base station, a remote server and a working computer, wherein the working computer includes an image processing module that is configured to detect an obtained image by using a deep learning network model trained with a database in advance, obtain any object that is suspected to involve a defect by analysis, and transmit the object for manual review while calculating for the specific geographic coordinate information of the defect, report for early warning after the defect is confirmed by the manual review, inform the operation and maintenance personnel, and store and record the defect. The present invention further provides a method for inspection by using the automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway.

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention relates to a heterogeneous multi-unmanned aerial vehicle cooperative path planning method based on multi-agent deep reinforcement learning, and solves the problem that a traditional path planning algorithm is difficult to deal with a dynamic planning problem and an agent cooperation problem compared with the prior art. And the existing multi-unmanned aerial vehicle cooperative path planning algorithm has the defects of insufficient feature extraction capability, low experience learning efficiency and poor cooperative strategy flexibility in a dynamic uncertain environment. The method comprises the following steps: analyzing a multi-unmanned aerial vehicle cooperative path planning task; building an airspace reinforcement learning environment; introducing an unmanned aerial vehicle kinetic equation; modeling a multi-unmanned aerial vehicle decision model in multi-unmanned aerial vehicle cooperative path planning as a POMDP model; designing an MASAC-SEPR algorithm model to carry out path planning on multiple unmanned aerial vehicles, and generating a multi-agent collaborative path optimization network model; and training the multi-agent collaborative path optimization network model. According to the method, the efficiency and the accuracy of multi-unmanned-aerial-vehicle cooperative path planning are remarkably improved, and a reliable solution is provided for heterogeneous multi-unmanned-aerial-vehicle cooperative operation in a dynamic uncertain environment.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle autonomous obstacle avoidance decision-making method and system based on multi-source sensor fusion, and the method comprises the steps: achieving the time-space synchronization of a laser radar, a visual camera and a millimeter-wave radar through timestamp alignment and coordinate mapping, and constructing a dynamic obstacle grid map; fusing multi-source data based on a dynamic Bayesian network, and dynamically adjusting the confidence coefficient weight of the sensor in combination with the environment illumination intensity and the barrier surface material; and adopting a reinforcement learning model to generate an incremental obstacle avoidance strategy, and triggering a grading response instruction according to the risk assessment grade. The problem of fusion errors caused by spatial-temporal asynchronization of multi-source sensor data is solved, and the real-time obstacle avoidance success rate of dynamic obstacles is increased.

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention discloses a space-time adaptive matching system based on multi-objective reinforcement learning, and the system comprises a data fusion cleaning module, an intelligent path planning module, a multi-objective scheduling optimization module, an adaptive task optimization module, and a reinforcement learning driving module. Self-adaptive scheduling and path planning of the unmanned aerial vehicle in a three-dimensional space are achieved, intelligent aircraft task management is provided, operation safety in a complex airspace is guaranteed, multiple requirements of the low-altitude industry for safety and efficiency are met, meanwhile, dependence on static rules and expert experience is reduced, and the unmanned aerial vehicle route planning method is suitable for popularization and application. The method can dynamically adjust the strategy according to real-time feedback and environmental change, has high adaptability, and meets the core technical requirements of future low-altitude equipment in operation service, supervision and whole industry chain safety standardization systems.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

An agricultural vehicle may include a propulsion system, a steering system, and a vehicle controller. An agricultural vehicle may include a camera sensor having a camera field of view (CFOV), a light detection and ranging (LiDAR) sensor having a LiDAR field of view (LFOV), and an imaging controller in operable communication with the camera sensor and the LiDAR sensor. The imaging controller may include a processor and at least one non-transitory computer-readable storage medium having instructions store thereon that, when executed by the at least one processor cause the imaging controller to, at least, receive image data, segment the image data, fuse LiDAR data and the image data, create a fused instance including depth information; and provide at least the fused instance to the vehicle controller, wherein the vehicle controller is configured to provide at least one navigational command based at least partially on the fused instance.

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

The invention provides an autonomous navigation path planning method, device and equipment for a humanoid robot in a complex environment. A navigation task is determined by obtaining the current position and path requirement of the robot; constructing a constraint preposition map based on body size constraint, joint motion constraint and dynamic balance constraint, and calculating a feasible motion space of the robot in advance; acquiring environment sensing data for variation trend analysis, and predicting a future movement track of the dynamic obstacle to generate a dynamic environment prediction sequence; establishing a bidirectional planning mechanism, performing forward and reverse path search at the same time, and generating a plurality of candidate path hypotheses through meeting point detection and path fusion; analyzing a human behavior pattern to identify a potential passage conflict area, establishing a social negotiation strategy, and taking an intermediate meeting point as a social key node to adaptively adjust a candidate path; the optimal safety path is determined through multi-dimensional safety evaluation of the falling risk, the collision risk and the energy consumption risk, and intelligent autonomous navigation of the humanoid robot in a complex environment is achieved.

The invention provides a patrol route self-adjusting method and system based on unmanned aerial vehicle patrol, and the method comprises the steps: firstly obtaining a multi-source environment perception data set of a target area, then carrying out the feature extraction of the multi-source environment perception data set, inputting a dynamic route planning model, generating an obstacle avoidance correction vector set and an environment adaptation parameter set, and carrying out the feature extraction of the multi-source environment perception data set; and carrying out spatial position offset compensation on the reference route based on the obstacle avoidance correction vector set to generate an initial correction path node set, carrying out path smoothness optimization processing on the initial correction path node set according to the environment adaptation parameter set to generate a final dynamic patrol route, and finally, carrying out dynamic patrol. The dynamic patrol route is converted into a waypoint control instruction stream which can be executed by the unmanned aerial vehicle flight control system, and the waypoint control instruction stream is transmitted to the airborne controller in real time to drive the unmanned aerial vehicle to execute the patrol task, so that dynamic self-adjustment of the unmanned aerial vehicle patrol route is realized, and the patrol flexibility and safety are improved.

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

The invention provides a disaster area unmanned aerial vehicle cluster dynamic task allocation and cooperative control method and system. The method comprises the steps that S1, original data of a disaster area scene are collected in real time through ground IoT equipment; s2, performing dynamic task priority marking on the original data, and integrating multi-dimensional information of the unmanned aerial vehicle; s3, tasks are allocated to the unmanned aerial vehicle based on a hybrid allocation strategy, and an initial obstacle avoidance path is generated; s4, when the unmanned aerial vehicle executes the task, the flight path is updated in real time, temporary obstacles are avoided, path conflicts are coordinated through a conflict detection algorithm, and task redistribution or degradation is carried out if necessary; s5, periodically, globally and dynamically adjusting task priorities or correcting paths; and S6, constructing a self-adaptive closed-loop control mechanism, and tracking multi-dimensional information and task attributes of the unmanned aerial vehicle in real time. According to the method, the global resource utilization rate is maximized while the second-level response of the high-priority task is guaranteed, and finally, the robustness and the adaptive ability of the unmanned aerial vehicle cluster in a complex scene are realized.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

Techniques for determining robotic step timing and sequencing using reinforcement learning are provided. In one aspect, a method includes receiving a target trajectory for a robot and receiving a state of the robot. The method further includes generating, using a neural network, a set of gait timing parameters for the robot based, at least in part, on the state of the robot and the target trajectory and controlling movement of the robot based on the set of gait timing parameters.

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

Disclosed herein are a system and a method for controlling multi-UAV cooperative tracking of a ground-moving target. The system includes UAVs, a processor, and a storage device. The UAVs are wirelessly communicatively connected to the processor. The processor is configured to generate a UAV command transmitted to the UAVs. The UAVs are configured to transmit tracking data to the processor. The processor includes a terrain building module, a flight simulation module, a UAV parameter generation module, a flight trajectory prediction module, a flight trajectory determination module, and a flight trajectory control module. The system provided in the embodiments of the present disclosure integrates Model Predictive Control (MPC) with an Improved Whale Optimization Algorithm (IWOA) to enhance the algorithm's convergence speed and precision, thereby improving the accuracy and stability of target tracking.

A path planning method for a multi-unmanned-aerial-vehicle collaborative maritime search. The method comprises: acquiring and analyzing related ocean current information of a region to be searched, and establishing an ocean current model on the basis of the obtained ocean current information (step 1); on the basis of the region to be searched, a command and control center establishing a grid map by taking a grid cell as a unit, and then establishing a target probability map on the basis of the related ocean current information of the region to be searched, prior target information and the grid map, wherein the target probability map provides the probability of a prior target being present in any grid cell (step 2); if there is an obstacle in the region to be searched, the obstacle being represented by an irregular polygon, generating a segmentation line for region division by taking a vertex of the irregular polygon as one end point of the segmentation line and also meeting a limiting constraint condition for region division, so as to divide the region to be searched into Nr sub-regions, and the command and control center allocating a sub-region to each unmanned aerial vehicle on the basis of the performance of the unmanned aerial vehicle by using a static allocation strategy (step 3); after the sub-region is allocated to each unmanned aerial vehicle, the command and control center planning a cross-regional shortest path for each unmanned aerial vehicle and using a path planning method to plan an unmanned aerial vehicle coverage path, so as to implement a single-region shortest-time coverage path (step 4); and monitoring the endurance state of each unmanned aerial vehicle during the process of executing a search task, and if it is found that the endurance capability is insufficient, recalling the unmanned aerial vehicle, and if a target is found or the search task is completed, recalling the unmanned aerial vehicle and returning to target coordinates (step 5).

The invention relates to an AI real-time intelligent guidance and self-adaptive obstacle avoidance method for multi-source sensing fusion of an unmanned aerial vehicle, and the method comprises the steps: collecting flight data through a visual sensor and an infrared sensor, obtaining a timestamp and space coordinate information, calculating a sampling frequency difference, and generating a fusion data set of time-space alignment through a self-adaptive interpolation algorithm; image and heat source features in the fusion data set are extracted, and a multi-dimensional feature set is generated, so that whether an obstacle exists or not is judged, and related information is extracted; and analyzing environment data, calculating an environment complexity index, generating a risk level in combination with obstacle information, predicting a dynamic weight of the sensor, and adjusting a working mode of the sensor. And then based on the obstacle condition, calculating a comprehensive risk value and predicting a motion trend through a path risk model, and finally generating an optimal obstacle avoidance path and a flight instruction, thereby realizing high-efficiency processing of sensor data, accurate risk assessment and real-time obstacle avoidance planning, and ensuring stable and safe flight of the unmanned aerial vehicle in a complex environment.

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

The invention discloses an unmanned aerial vehicle trajectory planning and tracking method, system and device based on deep reinforcement learning and adaptive nonlinear model predictive control, and a medium. The method comprises the following steps: constructing various static multi-obstacle and dynamic multi-obstacle simulation environments; constructing a kinetic model of the unmanned aerial vehicle; constructing an adaptive nonlinear model predictive control (ANMPC) algorithm; constructing a reward function of the tracking performance of the unmanned aerial vehicle to the reference trajectory generated by the adaptive nonlinear model predictive control algorithm; constructing a network framework based on deep reinforcement learning and an adaptive nonlinear model predictive control algorithm; setting network parameters; training a network framework, and selecting an optimal weight file; outputting a test result; the system, the device and the medium are used for realizing the unmanned aerial vehicle trajectory planning and tracking method. The method can effectively cope with changes of targets and environments, shows strong obstacle avoidance capability and anti-interference performance when facing dynamic obstacles and wind noise interference, and embodies a high intelligent decision-making level.

Provided are an attitude planner-containing trajectory tracking control method and system for a quadrotor unmanned aerial vehicle (UAV). The attitude planner-containing trajectory tracking control method for a quadrotor UAV includes: acquiring a barycentric coordinate, a yaw angle, a pitch angle, a roll angle, a linear velocity along a coordinate direction, and an angular velocity of a corresponding turning angle of the quadrotor UAV in real time; calculating a quaternion-based quadrotor UAV dynamic model; constructing a smooth attitude planner; calculating an attitude error dynamic system model of the quadrotor UAV according to a planned attitude angular velocity with a Rodrigues parameter; in combination with a position dynamic system model and the attitude error dynamic system model based on the Rodrigues parameter, constructing an outer-loop trajectory tracking control module and an inner-loop attitude control module with a hierarchical control technology; obtaining an attitude planner-containing trajectory tracking control result for the quadrotor UAV.