387 results about "Local environment" patented technology

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention relates to the technical field of battery thermal management, and provides a dynamic thermal management method and system for a battery module, and the method comprises the following steps: carrying out the local environment heat dissipation capability evaluation when a preset triggering condition is satisfied, and generating an evaluation result; generating a heat dissipation adjustment parameter of the heat dissipation system based on the evaluation result and the current thermal state of the local battery; carrying out data monitoring and recording, and generating reported data information; the reported data information comprises data related to local environment heat dissipation capability evaluation, evaluation result data, heat dissipation adjustment parameter data of a heat dissipation system and battery thermal state change data after the heat dissipation system executes adjustment; and uploading the reported data information to a cloud platform, so that the cloud platform executes continuous learning and optimization operation of the thermal management strategy based on the reported data information. The method has the advantages that the adaptability and safety of heat management are improved, and continuous optimization of a cloud strategy is supported.

The invention relates to the technical field of robot control, in particular to a robot motion control method, a robot and a system. The method comprises the following steps: acquiring depth information and color information of a current scene and visual textures and geometric features of a target object through an RGB-D camera, and generating a local environment map; introducing a motor current sampling value, correcting the material category of the target object, and determining an initial set value of the clamping force; the corrected joint torque estimated value is converted into a tail end force corrected value through a mechanical arm jacobian matrix; and on the basis of the force control weight and the position control weight, position control output and impedance force control output are calculated respectively, and a final control instruction is generated in combination with the clamping force initial set value and the tooth space compensation item. A harmonic compensation function based on the joint position, the speed and the current is constructed, periodic torque disturbance caused by the cogging effect of the servo motor is effectively restrained, the tail end force estimation is closer to the real acting force, and the clamping control stability is improved.

The invention relates to the technical field of substation operation and maintenance, in particular to a robot inspection path optimization method and system applied to an extra-high voltage substation, and the method comprises the steps: triggering the dynamic optimization of an inspection path in response to the detected equipment operation state change or inspection environment change of the extra-high voltage substation; based on a preset transformer substation environment space model and real-time equipment states and environment parameters collected in real time, a first target inspection path is generated through path cost analysis, and simulation verification and fine adjustment are carried out; when interference risks, positioning deviation overrun and / or multi-machine conflicts exist in execution of the first target inspection path, path planning is updated based on local environment information collected by the robot in real time, and a second target inspection path is generated; and executing the second target inspection path. According to the invention, the equipment state and the environment change can be responded in real time, the inspection path can be automatically adjusted, key inspection tasks are prevented from being omitted, and the continuity of the inspection process is ensured.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention provides an unmanned ship intelligent hunting control method, device and equipment, and relates to the technical field of unmanned ship intelligent control, and the method comprises the steps: obtaining the state information of a current unmanned ship and the local environment data sensed by the current unmanned ship, and obtaining the sensing state information corresponding to the current unmanned ship; based on the sensing state information, constructing a structured state input vector by adopting a distance sorting and filling cutting mechanism; inputting the structured state input vector into a hierarchical strategy network, generating a task role and behavior decision by a high layer, and generating a navigation speed and direction control instruction by a low layer in combination with the high-layer output and structured state input vector; wherein the hierarchical policy network is obtained by optimizing a preset policy network by adopting reinforcement learning through a structured reward mechanism; and controlling the current unmanned ship to execute a hunting task on the target ship according to the navigation speed and direction control instruction. According to the scheme, the stability and flexibility of a multi-boat cooperation strategy and the success rate of a hunting task are effectively improved.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention discloses an office chair cushion temperature and humidity multi-stage regulation and control method and system. The method comprises the steps that the contact state of a cushion and a human body contact interface, the local environment, human body physiological response and external environment multi-dimensional parameters are collected in real time; the parameters are matched with a dynamic reference model constructed by multi-user historical comfort data, the matching degree is calculated, and multi-level discomfort levels of all contact areas of the cushion are divided; in combination with the discomfort level and the environmental condition, generating a personalized strategy corresponding to the multi-level regulation intensity through a multi-objective optimization algorithm; and the temperature and humidity adjusting device is driven to execute multi-stage cooperative regulation according to a strategy, and feedback data is collected to update the model. The system integrates a multi-parameter acquisition module, a data processing module, an execution regulation module and the like, and can realize automatic damp-proof management of the seat cushion after the seat is separated from the seat. The problems that existing regulation and control are single, the dynamic comfort requirement cannot be met, and the hidden danger of moisture after the cushion is separated from the seat is solved, and the comfort, sanitation and durability of the cushion are remarkably improved through full-period and multi-stage precise regulation and control.

The invention relates to the technical field of video recognition, in particular to a construction safety real-time high-precision detection method and system based on environmental characteristics. Arranging a plurality of sensors in a to-be-detected area to collect field environment data in real time; analyzing the illumination change of the to-be-detected area through the light and shadow analysis model, calculating a light-safety misjudgment index, and identifying a potential light misjudgment area; a visual processing model is utilized to analyze the worker video, human skeleton key points are recognized in real time, personnel behavior safety indexes are obtained, and potential dangerous behaviors are detected; detecting local wind speed and turbulence conditions and vibration and resonance phenomena of a to-be-detected area in real time through a local breeze and vibration detection model, and evaluating a local environment stability index; and the safety level of the to-be-detected area is calculated in real time by using the light-safety misjudgment index, the personnel behavior safety index and the local environment stability index, so that multi-dimensional and real-time construction safety risk assessment is realized.

The invention provides a multi-modal fire-fighting robot cluster cooperation method and system, a medium and a product, and relates to the technical field of fire-fighting robots, and the method comprises the steps: obtaining own multi-modal sensing data collected by a fire-fighting robot cluster in real time, and generating a corresponding local environment model for each robot in the cluster; the working state of the current master control fire-fighting robot is monitored, if abnormity is found, the master control performance of a non-master control robot cluster (except the current master control robot) is analyzed according to the local environment models, and a new master control fire-fighting robot is determined; acquiring associated multi-mode sensing data collected by a new master controller from the local environment model of the slave robot cluster through a wireless ad hoc network, and generating a new global collaborative operation strategy according to the associated multi-mode sensing data; and distributing the new global collaborative operation strategy to each robot of the non-master-control robot cluster, and controlling the robots to carry out fire-fighting operation according to the strategy. The technical problem that a fire-fighting robot cluster is difficult to collaboratively carry out fire-fighting operation under the condition that a master control node fails in the prior art is solved.

The invention relates to the technical field of dust concentration detection, in particular to an online detection method and system based on limited space dust concentration, and the method comprises the following steps: based on each detection area, analyzing scattered light signals, classifying the scattered light signals into different particle size categories according to angles and coordinates, and performing signal compensation in combination with real-time humidity; adjusting the irradiation direction of the LED array to optimize illumination distribution, comparing light intensity differences, dynamically scheduling uniform illumination areas, subdividing detection points, analyzing layering signals, and obtaining particle size layering concentration data. According to the invention, distribution mapping with distinct particle size category difference is realized by using a spatial multi-dimensional layered structure, signal adaptability is improved by fusing a humidity dynamic response mechanism, particle size, humidity and illumination multi-source data are dynamically fused, local environment interference is eliminated through layering and compensation linkage, and the accuracy of the system is improved. The continuity of data expression and the response speed of concentration monitoring are improved, and continuous dynamic tracking of the dust state in a limited space complex environment is supported.

The invention provides an unmanned ship cluster confrontation game method and system based on situation information. The survival ability and combat efficiency of an unmanned ship cluster in a complex confrontation environment are improved. The method comprises the following steps: firstly, collecting threat data of the enemy unmanned surface vehicle and quantitatively evaluating the threat degree, collecting performance data of the our unmanned surface vehicle and quantitatively evaluating the adaptability, and fusing the threat data and the performance data by using an analytic hierarchy process to obtain a comprehensive situation evaluation result; then, sub-task targets are determined according to the comprehensive situation assessment and the overall task target, and tasks are reasonably allocated to the unmanned surface vehicles by adopting an improved Hungary algorithm; and finally, performing local path planning on each unmanned ship by adopting an improved A * algorithm in combination with a local environment, and performing global path planning based on multi-agent collaborative consideration of cluster collaborative requirements and enemy threat distribution. According to the method, comprehensive situation assessment, reasonable task planning and scientific path planning are realized, and the unmanned ship cluster confrontation capability is improved.

The invention provides a multilevel backtracking unmanned aerial vehicle path planning method based on a neighborhood extension algorithm, aiming at the problems that the traditional A * algorithm is low in efficiency and easy to fall into dead angles in unmanned aerial vehicle path planning. The method comprises the following steps: firstly, generating a random two-dimensional grid map simulation task environment; secondly, grids in eight neighborhoods of the current position of the unmanned aerial vehicle are scanned in real time, and local environment information is dynamically updated; and finally, a neighborhood extension planning algorithm is adopted, the unmanned aerial vehicle is allowed to perform diagonal motion, the planning flexibility and efficiency are improved, and redundant nodes and unnecessary curves are reduced. According to the method, a multi-stage backtracking strategy is designed, and a key backtracking point is preset in a path planning process, so that the unmanned aerial vehicle can quickly identify and return to a preset optimal backtracking point when entering a dead angle due to the limitation of a local environment, thereby quickly returning to a safe area and re-planning a path. According to the strategy, local optimal solution and infinite loop search are effectively avoided, and the task execution efficiency is remarkably improved in a complex environment containing a large number of dead angles.

The present invention proposes a control method and system for automated handling equipment on a dedicated transport line. The method includes: constructing a microgrid-based local environment model to collect and transmit equipment status information; performing task assignment and path planning for automated equipment based on the microgrid environment model; dynamically optimizing global task assignment and path planning based on task assignment and path planning within each microgrid; and monitoring the execution of the optimized task assignment and path planning in real time, dynamically adjusting the execution order of tasks and path planning through an intelligent scheduling algorithm, and making flexible adaptive adjustments based on resource status and environmental changes. By combining a microgrid network with an ant colony algorithm, the present invention solves the problems of single-point failure risk, poor path planning adaptability, and inflexible task scheduling in traditional automated transport systems, providing a new solution for the efficient, reliable, and flexible operation of automated transport lines.

The invention discloses a cloud native non-consistency fault detection method for a distributed application, and the method comprises the steps: deploying a Kubernetes cloud native platform in a local environment, deploying a to-be-detected distributed application on the Kubernetes cloud native platform as a detection object, and operating a work load in the detection object; injecting a plurality of cloud native non-consistency fault modes into the detection object, wherein the modes comprise unstable IP address distribution, DNS service delay and SIGTERM signal termination application program; based on a cluster health monitoring algorithm and test judgment logic of different fault modes, whether a fault exists or not is judged by monitoring the number and the state of Pod. The method is a testing tool for the cloud native non-consistency fault in the distributed application, the cloud native non-consistency fault can be detected in an end-to-end mode, and the stability and reliability of the distributed application are ensured.

The present application provides a method for generating a user interface and a method for controlling avatar movement through a user interface. An overhead view, a navigation mesh map and a local environment map that are generated and a main view constitute the user interface. Controlling avatars through the user interface allows users to control the avatars intuitively and immersively while allowing operators to control the freedom of limb movements of the avatars.

The invention discloses a vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning. The method comprises the following steps: independently executing detection according to local observation information and a strategy generation model through an intelligent agent deployed at an edge node; the strategy generation model is constructed based on a decentralized partial observable Markov decision process, and is obtained through cloud centralized training and global shared reward function optimization. When the agent obtains a forward reward, packaging a corresponding vulnerability context and related strategy parameters into an experience package; and the experience packet is diffused in the cooperative detection network in a mode of randomly selecting neighbor nodes and forwarding step by step. And after each agent receives the data, analyzing the content of the experience package and comparing the content with the local environment so as to dynamically adjust local strategy parameters and realize collaborative optimization. According to the vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning provided by the invention, the problems that network security vulnerability detection is poor in expandability and lacks knowledge generalization ability are solved.

The invention provides a heat stress activation state threshold regulation and control method and system based on poultry group behavior feedback, and relates to the technical field of intelligent management and control of livestock and poultry breeding environment, and the method comprises the steps: recognizing the number of poultry heads in a heat wheezing state and the total number of the poultry heads based on poultry head images, and obtaining the real-time heat wheezing proportion of poultry groups; based on a distributed environment real-time acquisition network, obtaining an in-cage environment temperature and humidity index; aiming at each region, integrating data pairs synchronized by a time window, fitting a piecewise linear regression relationship of the real-time heat wheezing proportion of the poultry group along with the change of the temperature and humidity index of the environment in the cage, dynamically generating a heat stress grading threshold value of the region based on a characteristic turning point, generating a house-level regulation and control instruction and a region-level regulation and control instruction, and carrying out region-level regulation and control on the region-level regulation and control instruction. The local environment of the corresponding area is adjusted and alarmed, and a heat stress activation state threshold regulation and control result is obtained. The problems that poultry group heat stress is difficult to accurately and dynamically assess and poultry house environment is difficult to finely and hierarchically control in different production scenes are solved.

The invention discloses a basement ventilation and smoke exhaust system and method based on digital management, and the method comprises the steps: constructing a digital twin architecture comprising a terminal layer, an edge layer and a cloud layer, and carrying out the real-time mapping and interaction of a physical system for basement ventilation and smoke exhaust and a virtual model; carrying out integrated monitoring on the terminal layer by utilizing multiple sensors, and collecting relevant data parameters of ventilation and smoke exhaust of the basement; and a high-integration-level edge computing gateway is adopted to preprocess the collected data. The regional controller adopts a dynamic Bayesian network to fuse the preprocessed multi-source data, and constructs a local environment state model; the edge layer utilizes a deep reinforcement learning algorithm and a digital twinborn model to simulate multiple scenes to carry out state real-time prediction, and generates an emergency plan library; and the cloud layer central server operates a double-track decision engine and performs adaptive adjustment in combination with a deterministic rule base and an adaptive deep reinforcement learning model, and the technical scheme of the invention realizes efficient and accurate ventilation and smoke exhaust cooperative control.

The embodiment of the invention discloses a robot autonomous navigation and obstacle avoidance method and system based on multi-sensor fusion. The method comprises the following steps: acquiring multi-dimensional data; fusing multi-dimensional data to generate a robot pose and a local environment point cloud; dividing a front sensing area into a high-reliability green area and a low-reliability red area according to the positions of the red and green light spots, and respectively distributing sensing weights; projecting the local environment point cloud into an occupied grid, performing obstacle detection according to the point cloud density in each grid unit, and multiplying the detection result by the corresponding sensing weight to obtain a sensing energy consumption cost value of the unit; according to the perceived energy consumption cost value, generating a target attraction direction and an obstacle rejection direction on the occupied grid; and generating a motion path based on the attraction direction and the rejection direction, and controlling the robot to advance according to the motion path. According to the invention, deep collaborative optimization of energy consumption cost and motion planning is realized.

The invention relates to the field of robot navigation, in particular to a robot dog formation collaborative operation method and system in a disaster environment. The method comprises the following steps: acquiring future intention information of a pilot dog and a local environment model sensed by a relay dog, and fusing to construct a prospective environment model; calculating a link quality score of a relay dog to the intention of a pilot dog based on the model, constructing a potential field, and solving a gradient to obtain a communication gradient impetus; switching the operation mode of the relay dog according to the operation state of the pilot dog; in the corresponding mode, the communication gradient driving force, the communication attraction pointing to the pilot dog and the obstacle repulsive force are fused, the navigation resultant force is calculated, and the relay dog is controlled to work cooperatively. Through the prospective communication quality gradient field, the relay dog prejudges and avoids the communication blind area, the modal switching adaptation requirement is combined, the problem that formation communication is prone to being interrupted in the disaster environment is solved, the communication continuity and robustness are improved, and support is provided for efficient collaborative operation of the robot dog.

The invention discloses a debugging method and device for a low-code platform, a storage medium and electronic equipment. The method comprises the steps that a low-code engine is started in a local environment, the low-code engine is used for calling a target code to provide a low-code platform service, and the target code represents a modified original low-code segment; under the condition that the target code is used for transforming the front-end service, request flow data is obtained through a flow forwarding component, breakpoint debugging operation is executed based on the request flow data to obtain a debugging result, and the request flow data is used for indicating an interaction request obtained from a front-end page; the traffic forwarding component is used for controlling the request traffic data to be forwarded from the test environment to the local environment; under the condition that the target code reforms the back-end service, a target interface is used for executing breakpoint debugging operation to obtain a debugging result, and the target interface is used for debugging the target code in the local environment. According to the method and the device, the technical problem that the debugging efficiency is relatively low when the modified original low-code segment in the low-code platform is debugged is solved.

The invention relates to the technical field of transformer substation operation environment regulation and control, and discloses a transformer substation operation environment low-carbon energy-saving intelligent regulation and control method, system and device and a medium. The method comprises the steps that transformer substation operation environment parameters and device state signals are collected, and a first environment device coupling dynamic representation model is constructed; and in combination with the time sequence prediction and condensation risk assessment sub-model, the nonlinear response relationship of various factors to the heat and humidity distribution and condensation risk of the cabinet is described, and a foundation is laid for accurate regulation and control. And in collaborative optimization, the second multi-target collaborative optimization regulation and control model comprises an upper-layer global energy consumption optimization and lower-layer local environment precise control target function, so that the overall energy consumption is reduced. And low-carbon operation targets and equipment safety constraints are considered, so that low-carbon energy conservation is realized during safe operation of the equipment. The solving strategy is a first improved solving strategy based on model predictive control and particle swarm optimization improvement, the efficiency and precision of processing the complex multi-target collaborative optimization regulation and control problem are improved, and the optimal regulation and control scheme is found quickly and accurately.

A computer-implemented method for managing deck operations on a floating deck is disclosed. One example of the method comprises the following: obtaining a prediction of local environment conditions including one or more wave characteristics during a future time-window, generating, using the predicted environmental conditions with a vessel model, a forecast vessel motion during the future time-window, providing the predicted vessel motion to a vessel stabilization system model; determining, using the vessel stabilisation system model a predicted vessel stabilization configuration, and generating, using the predicted vessel stabilization configuration for the future time-window, a refined forecast vessel motion during the future time-window, providing the refined forecast vessel motion to a physics model of a candidate deck operation for execution on the floating deck during the future time-window, and generating, using the physics model for a candidate deck operation, one or more predictions of one or more deck operation physical characteristic metrics of the candidate deck operation for over the duration of the future time-window.

The invention provides a multi-view autonomous cooperative positioning method and system. The method comprises the following steps: determining a plurality of local maps and a plurality of key frame information of an environment where a plurality of agents are located based on a preset synchronous positioning and mapping algorithm; the plurality of intelligent agents are used for acquiring local environment information of multiple viewpoints from different positions and visual angles through carried visual sensors; the multi-view local environment information corresponds to a plurality of local maps and a plurality of key frame information; determining a constraint relationship among the plurality of local maps according to the information of the plurality of key frames, and determining a local map coordinate system transformation matrix according to the constraint relationship; the constraint relation comprises pose constraints of adjacent key frames; and fusing the plurality of local maps according to the local map coordinate system transformation matrix to obtain a global map, and determining global positioning information according to the global map. The method can adapt to a complex environment, and realizes high-precision and high-robustness unmanned aerial vehicle cluster positioning.

The invention relates to the technical field of intelligent manufacturing workshop logistics scheduling, in particular to a multi-agent path planning method based on social value orientation and DRL fusion, and the method comprises the steps: S1, building multi-channel tensor representation based on local observation, carrying out the spatial coding of dynamic information, and building an interpretable thinking feature space; s2, receiving neighbor agent dynamic information based on a heterogeneous graph attention network communication mechanism of SVO, separating and aggregating the information through a multi-head SVO perception heterogeneous graph attention network, and extracting information which is most critical to self decision; and S3, based on an SVO double-layer decision-making mechanism of a neighbor agent group, combining SVO and PPO algorithms, and dynamically adjusting an SVO strategy by adopting a local environment and a neighbor agent group to realize dynamic change of a self-adaptive environment of a lower-layer action strategy. According to the method, path conflicts and deadlocks are effectively eliminated, the throughput and success rate of the system are improved, an efficient collaborative solution is provided, and the workshop operation efficiency and the intelligent level are improved.

3D objection detection is a computer vision task that generally refers to detecting (e.g. classifying and localizing) an object in 3D space from an image or video that captures the object. This computer vision task has many useful applications, such as autonomous driving applications which rely on the detection of 3D objects in a local environment to make autonomous driving decisions. State-of-the-art 3D object detectors generally rely on machine learning, but current training processes for these detectors do not specifically address false negative detections, or missed objects, which are often caused by occlusions and / or cluttered backgrounds in the given image / video. Reducing false negatives is crucial for many downstream applications, particularly autonomous driving applications which rely on accurate detection of obstacles for making safe driving decisions. The present disclosure provides for a multi-stage training process that reduces false negative detections by 3D object detectors.

The invention provides a complex terrain navigation method and system based on a brain-like neural network. The method comprises the following steps: collecting global environment point cloud data, and constructing a three-dimensional semantic map by using an SLAM algorithm; generating a sparse global path for the robot by using a preset T-NIRRT algorithm; smoothing the sparse global path by adopting a B spline interpolation algorithm, then performing trafficability prediction on the smoothed global path by utilizing a GPR technology, and constructing a three-dimensional safety corridor for the robot; acquiring pose estimation of the robot and local environment point cloud acquired by the robot in real time, and inputting the pose estimation and the local environment point cloud into a pre-trained brain-like neural network model together with the three-dimensional safety corridor to generate a trajectory increment; reading the latest trajectory increment, constructing an NMPC optimization problem and performing iterative solution to obtain an optimal robot control instruction, and sending the optimal robot control instruction to the robot for execution; repeating the steps until navigation is completed; the invention provides a set of more efficient, safer and intelligent solution for ground robot navigation under complex terrains.

An indoor air quality system is configured to improve indoor air quality conditions within a local environment of a building. The indoor air quality system operates according to a method that includes obtaining indoor air levels and operating one or more appliances to adjust the indoor air levels. The system includes one or more monitoring devices.