761 results about "Sensor fusion" patented technology

The invention belongs to the technical field of artificial intelligence and intelligent sensing, particularly relates to a multi-source sensor fusion sensing system based on adaptive noise suppression, and aims to solve the problems of noise interference, modal mismatch and insufficient robustness in multi-source sensor fusion in a complex dynamic environment. The system comprises a front-end preprocessing module, an adaptive noise suppression engine, a multi-modal feature alignment unit, a credibility-driven fusion reasoning core and a closed-loop feedback optimization mechanism. Through real-time noise modeling and dynamic weight adjustment, high-precision alignment and fusion of multi-source signals are realized, and the sensing stability and real-time performance in an extreme scene are significantly improved.

The invention discloses a multi-modal collaborative security monitoring method, device and equipment and a storage medium, and relates to the technical field of computer vision and sensor fusion, the method comprises the following steps: collecting multi-modal data of a monitoring area, the multi-modal data comprising image data, infrared temperature data and environmental parameter data; based on the target detection model, detecting personnel security features and environment security features in the image data, and outputting a visual identification result and visual identification confidence; and when the visual identification confidence coefficient is lower than a preset threshold value and the consistency of the multi-modal data meets a synchronization condition, performing association verification of the multi-modal data by adopting weighted fusion in combination with the infrared temperature data and the environmental parameter data to perform secondary identification of a target so as to output the multi-modal fusion confidence coefficient. Through combination of visual identification and a multi-sensor fusion technology, accuracy and robustness of personnel and environment safety identification in a complex industrial environment are improved.

The invention discloses a multi-sensor fusion heat pump full life cycle AI maintenance early warning system, and relates to the technical field of new energy utilization, and the early warning system comprises a data collection module which obtains operation parameters in a heat pump full life cycle based on a sensor array, and constructs a data set after preprocessing the parameters; the operation parameters comprise temperature, pressure, flow and micro vibration; the data fusion module is used for extracting trend correlation characteristics and parameter coupling characteristics from temperature, pressure and flow parameters by adopting a dynamic sliding window adaptive to a working condition, and preserving core nonlinear information through KPCA dimension reduction; the micro-vibration signal extraction comprises frequency domain and time domain features. According to the method, features are extracted through a working condition adaptive dynamic sliding window, then through cross-space mapping and a life cycle-working condition double-attention mechanism, the analysis and early warning module depends on a core feature mapping library and a two-dimensional dynamic baseline, through instantaneous and accumulated deviation double judgment, abnormal accurate recognition and stage division are achieved, and early warning perspectiveness is high.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

A collaborative autonomous vehicle sensor fusion system enables multiple vehicles to share multimodal sensor data for enhanced perception capabilities beyond individual vehicle limitations. Each autonomous vehicle captures multimodal sensor data, identifies safety-critical objects, applies priority-based compression based on safety criticality, and shares compressed data via vehicle-to-vehicle communication. An enhanced multi-vehicle AI deblocking network receives the compressed sensor data and enhances perception data for each vehicle using sensor data from multiple vehicles in the collaborative network. The system prioritizes reconstruction quality for safety-critical objects over non-safety-critical objects and enables detection of safety-critical objects occluded from individual vehicles through collaborative sensor fusion. The network fuses multimodal sensor data by identifying cross-modal correlations between different sensor types and uses these correlations to reconstruct sensor information that is degraded or occluded in individual vehicles, providing improved situational awareness for autonomous vehicle operation.

The invention discloses a centimeter-level positioning and control system for a low-altitude unmanned aerial vehicle, relates to the technical field of high-precision positioning, and solves the problems that firstly, centimeter-level positioning precision is difficult to realize; secondly, a safe flight path is difficult to generate and adjust in a complex environment; thirdly, it is difficult to optimize the global optimal path by comprehensively considering multiple factors such as energy consumption, obstacles and geo-fences; and finally, the technical problem that it is difficult to generate a control instruction and execute the control instruction under centimeter-level positioning in combination with the obstacle avoidance sensitivity, the hovering stability coefficient and real-time environment perception is solved. According to the invention, centimeter-level real-time positioning is realized through multi-source sensor fusion and Kalman filtering; constructing a geofence and a three-dimensional obstacle avoidance path, and combining obstacle detection to prevent boundary-crossing collision; path planning is optimized by utilizing machine learning and risk assessment, so that the unmanned aerial vehicle dynamically adapts to a complex environment; and optimizing a low-altitude flight control strategy through positioning error compensation and control strategy optimization.

The invention belongs to the technical field of ocean detection, belongs to a multi-sensor fusion intelligent anti-collision method and system, comprises a sensing layer, a processing layer and an application layer, and provides an intelligent anti-collision and evidence recording ocean monitoring floating system integrating computer vision, target ranging, satellite positioning and ship automatic recognition system multi-sensor fusion. According to the invention, YOLOv8 target detection, Transform data fusion and a Kalman filtering algorithm are adopted, so that accurate detection and anti-collision early warning of ships and floating objects on the sea are realized. The system has an AIS failure processing mechanism and an evidence encryption storage function, and ensures reliable operation and data compliance under complex sea conditions.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention relates to the technical field of structure health monitoring, in particular to an immovable cultural relic structure health detection system based on multi-source sensor fusion, which comprises a multi-source texture response acquisition module, a curved surface drift path identification module, a crack region extraction module, a stress response trend offset module and a health level region output module. According to the method, the continuous coding of the structure texture is combined with the micro-strain response characteristics, so that the accurate judgment of the synchronous drift of the surface texture of the cultural relic structure is realized, the identification sensitivity of potential micro deformation is improved, a multi-period track set is constructed according to the center deviation trend in a time sequence, and the evolution tracking capability of space behaviors is enhanced; the crack linkage area is subjected to consistency screening through image gray abrupt change and strain direction, the accuracy of crack boundary identification is improved, the safety of an immovable cultural relic structure is guaranteed, and meanwhile collaborative improvement of real-time monitoring, the response speed and the automation level is considered.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention provides an automatic operation process management method and device based on multi-sensor fusion, and relates to the technical field of automatic processes, and the method comprises the steps: constructing an instrument pose flow and operation field geometric model under a unified coordinate system through unified timestamp alignment and spatial geometry solution of multi-source sensor data; semantic segmentation and three-dimensional reprojection are combined to generate a shielding probability graph, cross-channel compensation reconstruction is carried out, and the image integrity is improved. And forming a conflict risk set through voxel field discretization and short-time prediction, and performing combined solution with standardized operation process constraints to generate a scheduling instruction and a safety time window. And finally, mapping the image, the pose and the physiological signal to a knowledge graph, and outputting consistency judgment and deviation correction suggestions in combination with a time sequence attention recognition process node. According to the method, the problem that the operation process management is not accurate enough due to the problems of view overlapping and the like of multi-source sensor data can be solved.

The invention belongs to the technical field of artificial intelligence and intelligent control, and particularly relates to an obstacle avoidance control method based on dynamic obstacle trajectory prediction. The method aims at solving the problems of obstacle avoidance decision lag and path oscillation caused by nonlinearity and strong interactivity of obstacle movement in a complex dynamic environment. A historical pose sequence of an obstacle is obtained through multi-source sensor fusion, a probabilistic trajectory prediction model is constructed by combining Gaussian mixture process regression with scene topology constraints, an attention mechanism is introduced to enhance inter-individual interactive intention modeling, and a socially compatible joint prediction trajectory is generated. The method also supports the dynamic adjustment of the prediction time domain and the re-prediction frequency, and gives consideration to the calculation efficiency and the response capability. The method is deployed in an embedded platform, the cycle period is less than 80 milliseconds, more than 32 obstacle trajectories can be tracked and predicted in real time, and the obstacle avoidance safety and smoothness of the system in a dense and high-speed dynamic scene are remarkably improved.

The invention relates to the technical field of multi-sensor detection, and discloses a multi-sensor fusion coal conveying belt coal blockage detection method and device and a storage medium, and the method comprises the steps: collecting a laser point cloud, a first visual image and millimeter wave volume information of a coal flow of a coal conveying belt transfer point, and carrying out the dust environment restoration of the first visual image, obtaining a second visual image; registering the laser point cloud with the second visual image to obtain first fusion data; performing surface projection transformation on the millimeter wave volume information to obtain surface projection data, and integrating the surface projection data with the first fusion data to obtain second fusion data; performing time compensation on the second fusion data to obtain synchronous coal flow data; the coal flow accumulation abnormity identification is performed based on the synchronous coal flow data, the coal flow blockage early warning signal is generated, the accurate identification of the coal flow accumulation abnormity state is realized, and the reliability and the accuracy of blockage detection are improved.

The invention provides a multi-sensor fusion cloth online flaw identification method and system, and relates to the technical field of cloth flaw identification. The method comprises the following steps: acquiring specified maps of the front and back sides of current detected cloth; performing space-time alignment on the data in the specified atlas to obtain aligned multi-source data; key information is extracted from the multi-source data; according to the cloth type parameter of the current detected cloth, determining the fusion weight of each data in the key information; performing weighted fusion on data in the key information according to the fusion weight to generate a fusion feature vector; and comparing the fusion feature vector with the fusion feature of the normal cloth, and determining and positioning a chromatic aberration area. The method provided by the invention aims at solving the technical problem of performing color difference defect detection on the cloth which has double-sided property and light transmission and is subjected to an after-finishing process on a cloth manufacturing production line, and realizes more accurate identification and positioning of the color difference defect of the complex cloth.

The invention discloses an energy efficiency improving method and device for a slope type gravity energy storage system, and relates to the technical field of electric energy storage systems. The method and device for improving the energy efficiency of the slope type gravity energy storage system comprise the following steps that S1, energy storage operation data are obtained through multi-source sensor fusion, and data cleaning and normalization processing are conducted on the energy storage operation data; s2, extracting a multi-dimensional state factor, carrying out quantitative evaluation on the operation state of the energy storage unit, constructing a state grading mechanism based on an evaluation result, and generating an operation state label; s3, calling a running state label to screen candidate units, constructing a scheduling priority ranking index, and dynamically allocating tasks and adjusting an execution sequence according to the index; and S4, analyzing structural wear and energy consumption load characteristics, establishing a scheduling correction mechanism, and dynamically adjusting a scheduling priority ranking index. The problem that the scheduling self-adaption cannot be realized according to the load, loss and wear difference of each unit in the parallel operation process of a plurality of energy storage units is solved.

The invention provides a multi-sensor fusion welding temperature monitoring method and a related device, and the method comprises the steps: dividing a welding region into a plurality of sub-regions according to the spatial distribution of the welding region, and obtaining the initial temperature data corresponding to each sub-region according to various temperature sensors; performing time alignment on the initial temperature data through difference resampling of different sampling points to generate a time sequence temperature matrix; performing real-time quality detection on the time sequence temperature matrix to generate a real-time quality score, and performing weighted combination on the real-time quality score and a preset confidence coefficient parameter table to obtain a dynamic confidence coefficient result set; fusing the effective temperature data exceeding a preset confidence threshold in the dynamic confidence result set according to a fusion rule corresponding to each sub-region to obtain a fused temperature result; and the fusion temperature results of the sub-regions are subjected to time sequence splicing to obtain a global temperature curve of the welding process, so that the accuracy of temperature detection in the welding process can be effectively improved.

The invention discloses a railway comprehensive defect detection method and system based on multi-source sensor fusion. The method comprises the following steps: carrying out joint calibration on static external parameters by adopting a multi-modal calibration plate; a high-precision timestamp and vehicle multi-source motion parameters are utilized to construct a motion compensation model to carry out adaptive dynamic distortion correction calibration; synchronously acquiring multi-source sensor data, and based on static external parameters and adaptive dynamic distortion correction, obtaining a corrected visible light image, an infrared image and a laser radar three-dimensional point cloud; complementarity features are extracted from the data of the three modes, adaptive fusion and optimization are carried out through an attention mechanism, and multi-scale fusion features are output; segmenting different railway facility areas by using multi-scale fusion features, extracting corresponding area features, and calling detection algorithms of corresponding facilities to identify defects of different railway facilities; according to the method, high-precision calibration, feature-level fusion and railway facility special defect detection are fused, and the automation level and the detection accuracy of railway inspection are improved.

The invention provides a multi-sensor fusion robot grabbing path planning method and device, and relates to the field of automation. The method comprises the following steps: constructing a first time correction relation based on observation parameters of a multi-source sensor; correcting the relation based on the first time; inputting the hardware timestamp synchronized to the master clock source into the extended state estimation model; iteratively updating the observation parameters of the multi-source sensor based on the extended state estimation model, and constructing a second time correction relation according to the updated observation parameters of the multi-source sensor; constructing a pose continuous time trajectory function corresponding to the target robot based on the second time correction relation; the continuous time trajectory function and the multi-source sensor observation parameters serve as input, and a grabbing path corresponding to the target robot is output through a time sequence consistency constrained joint optimization model; and controlling the target robot to execute grabbing operation based on the grabbing path. The problem that in the prior art, the precision of a robot grabbing path is low is solved.

The invention discloses a multi-scene adaptive AGV scheduling management system and a path planning method. Comprising a multi-scene perception and environment modeling module, a multi-AGV task scheduling and resource allocation module, a dynamic path planning and optimization module, a scene adaptive decision module, a multi-sensor fusion obstacle avoidance module, a heterogeneous communication and data synchronization module and a whole-process monitoring and fault diagnosis module. According to the invention, full-process adaptive control from environment modeling to decision optimization is realized. An SVM classification algorithm and dynamic matching degree calculation are adopted, so that the system can accurately identify characteristics of heterogeneous scenes such as warehousing, workshops and logistics hubs, and an optimal scheduling strategy is automatically switched; and a multi-dimensional cost function including path length, obstacle risk, smoothness and congestion degree is constructed, and seamless connection of global path pre-planning and local dynamic adjustment is realized.

The invention relates to the technical field of machine control, and discloses a chemical safety inspection robot control system and method based on sensor fusion, and the system comprises a data fusion module, a strategy construction module, a strategy analysis module, a real-time monitoring module, an optimization instruction module, and an event determination module. Original data of the chemical environment is fused into standardized data, and structural features are analyzed to identify risk factors; constructing a security situation map, mapping the security situation map to the robot, and determining a control strategy; when the robot begins to inspect, analyzing the control strategy to control the movement adjustment path action and the path safety inspection action of the robot; coding the movement adjustment path action and the path safety inspection action into a safety control instruction; optimizing the safety control instruction to obtain an optimized control instruction; when the robot completes inspection, an environment safety abstract and an abnormal event record are obtained; the control accuracy of the chemical safety inspection robot can be improved.

The invention relates to the technical field of inertial navigation and sensor fusion, in particular to an IMU (inertial measurement unit) attitude estimation and error correction method based on a state space physical information neural network, which comprises the following steps: acquiring original data of an IMU and preprocessing; constructing a state space model, and designing a state transition physical information neural network based on the state space model; training the state transition physical information neural network based on the preprocessed original data, optimizing network parameters through a joint loss function in the training process, and obtaining the trained state transition physical information neural network; and collecting real-time data streams of the IMU, inputting the real-time data streams into the trained state transition physical information neural network, and outputting the corrected pose, position and speed. According to the method, physical constraints and adaptive noise estimation are fused, so that the attitude estimation precision and the error correction capability in a dynamic complex environment are remarkably improved.

The invention relates to an automatic driving equipment monitoring system on an expressway, and belongs to the technical field of intelligent traffic. The sensor collects road data; the anti-interference module is used for performing space-time alignment on data acquired by the sensors and dynamically adjusting the fusion weight of the sensors according to weather conditions; the edge calculation layer is used for performing real-time processing and dynamic task scheduling on the data and balancing calculation load and response speed; and the cloud decision-making layer is used for carrying out global integration and decision-making optimization on the data, triggering a multi-level early warning protocol when an emergency event or a road condition is detected, and broadcasting early warning information and vehicle passing suggestions through road test equipment. Manual work is replaced by automatic inspection, the disease identification accuracy is greatly improved, the inspection efficiency is improved, the emergency response time is shortened through real-time data uploading and global scheduling, inspection can be completed through a common vehicle-mounted terminal, special equipment does not need to be leased, and the daily cost is reduced.

The invention relates to the technical field of multi-sensor fusion positioning, and discloses an outdoor robot multi-sensor fusion method. Comprising the following steps that robot operation data are obtained through multiple sensors in the running process of a robot; taking the preprocessed operation data as measurement input of an unscented Kalman filter state estimation process, and obtaining first position data after fusion processing; learning the noise characteristics of each sensor by using a long-short-term memory network of a sliding window, and compensating the position deviation of the robot to obtain second position data; performing time synchronization processing on the first position data and the second position data, and inputting the first position data and the second position data into an improved double-layer multi-model adaptive estimation framework; the improved double-layer multi-model adaptive estimation framework adopts a double-layer structure; and obtaining final real-time position data. The method can inhibit noise and drift of a single sensor, reduces error accumulation caused by factors such as environment shielding, improves positioning precision, robustness and continuity, and is suitable for scenes such as outdoor driving robots.

The invention relates to the field of data processing, in particular to a multi-source data fusion method based on AGV cooperative positioning, and the method comprises the steps: carrying out the collection and preprocessing of AGV positioning original data through multi-source sensor fusion, and obtaining a standardized feature value and a prediction parameter for cooperative updating; carrying out noise factor optimization adjustment by fusing the physical propagation characteristics and the ranging fluctuation information to obtain a UWB measurement noise variance subjected to preliminary dynamic correction; deviation risk identification is carried out through risk factor adjustment based on innovation consistency inspection, and a final dynamically corrected UWB measurement noise variance is obtained; constructing a variance matrix through the dynamic variance and executing filtering updating to obtain corrected AGV state estimation and variance; by optimizing a state estimation result and recursively inputting a prediction process, dynamic updating of multi-AGV cooperative positioning is realized, so that the stability and the overall performance of AGV positioning in a dynamic environment are enhanced.

The invention relates to the technical field of inertial navigation and sensor fusion, in particular to a multi-sensor fusion and adaptive filtering attitude estimation method, which comprises the following steps: S1, acquiring sensor data of a gyroscope, a magnetometer and an accelerometer, and respectively acquiring angular velocity, magnetic field direction and gravitational acceleration information; s2, angle information is obtained, and magnetometer and accelerometer sensor data are converted into attitude information; s3, carrying out weighting processing on the angle information and the attitude information through a fading memory weighting method and a limited memory weighting method; S4, carrying out filtering fusion on the weighted angle information and attitude information through an RAEKF filtering fusion algorithm; s5, an adaptive factor and a robust factor are introduced into the RAEKF filtering fusion algorithm; s6, outputting attitude angle estimation; according to the method, the theoretical contradiction that long-term drifting and stability cannot be achieved at the same time is fundamentally solved, stable precision is kept under abnormal data interference, and the robust performance is remarkably improved.

The invention provides a multi-sensor fusion crop harvesting and pile-dividing cooperative control method and a multi-sensor fusion crop harvesting and pile-dividing cooperative control system. The method comprises the following steps: extracting morphological characteristics of crops from crop image data, and generating a crop maturity identification result according to a difference value between reflection spectrum data of two groups of wavebands; determining a crop quality identification result according to the morphological characteristics and the crop maturity identification result; an opening and closing control instruction is sent to a pneumatic flow guide plate at a pile separation opening corresponding to the crop quality recognition result in a multi-stage pile separation opening in the tail end of the conveying belt, so that the crops on the conveying belt are conveyed to a corresponding pile separation area; and when the pressure value of the pile dividing area is larger than a preset pressure threshold value, the harvesting speed of the crop harvesting and pile dividing all-in-one machine is adjusted according to the pressure value. According to the technical scheme provided by the invention, the shape and maturity of the crops are analyzed through the image and spectral data, piling and harvesting can be intelligently controlled, and accurate and efficient crop quality grading and harvesting management are realized.

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

The invention discloses a multi-sensor fused vehicle lateral mistaken intrusion early warning system, which comprises an automatic cruise vehicle, and is characterized in that an operation area coordinate system is established by taking a starting point of an upstream transition area of an operation area as an original point; the system has three working modes, namely a visual perception mode, a radar perception mode and a fusion working mode. Through collection of vehicle motion information, identification of a vehicle lane changing intention is obtained, whether an intrusion intention exists is judged, whether a prediction track enters an operation area boundary is judged, and if a lateral mistaken intrusion risk exists, an early warning module is triggered. The normal mode is a fusion working mode with the combined action of the visual perception mode and the radar perception mode, and the accuracy of judging whether the vehicle has the risk of intruding into the vehicle by mistake is improved. And the visual perception mode and the radar perception mode can be used for independently judging the mistaken intrusion risk, so that the problem that a single mode fails or cannot work normally is prevented.

The invention discloses an autonomous positioning and navigation method and system for a ground mobile robot. The method comprises the following steps: S1, multi-source asynchronous environment perception and initial positioning; s2, carrying out adaptive fusion on multi-source data of time-space association; step S3, global pose optimization under semantic constraint; s4, decision planning driven by the dynamic risk field; and S5, automatically recovering the abnormal state and updating the map. According to the invention, through environmental response type sensor fusion, multi-modal semantic constraint modeling and entropy-driven intelligent decision-making mechanism synergistic effect, industrial pain points of positioning drift, low planning efficiency and insufficient long-term operation reliability in a complex dynamic scene are systematically solved; the centimeter-level positioning precision and the smooth motion trail are kept under the extreme conditions of severe illumination change, dense obstacle disturbance and repeated structure path, the passive operation and maintenance mode that a traditional navigation system depends on manual intervention is thoroughly changed, and a full-autonomous navigation capability closed loop is provided for industrial logistics and commercial service scenes.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.