1072 results about "Search and rescue" patented technology

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

The invention discloses an unmanned aerial vehicle cluster communication system based on weak signal connection. According to the invention, through the intelligent cooperation mechanism of the dynamic relay path optimization module, the communication reliability in a complex environment is significantly improved. The system analyzes cluster network topology and node states in real time, combines multi-dimensional parameters such as signal stability, energy consumption efficiency and task load, dynamically screens an optimal relay node and generates an efficient transmission path. For example, in a weak signal area, the system can automatically start an adjacent high-stability node as a relay, the problem of rigidity of a traditional fixed relay strategy is avoided, meanwhile, the signal attenuation risk is avoided in advance through predictive switching, and continuous return of key data is ensured. The capability of actively adapting to network fluctuation effectively reduces the probability of communication interruption, and can ensure the non-inductive connection of task instructions and key information especially in scenes with strict requirements on real-time performance, such as search and rescue, exploration and the like.

The invention discloses a multi-unmanned aerial vehicle maritime search and rescue path planning method based on multi-target particle swarm optimization. The method comprises the steps that S1, an area to be searched and rescued is converted into a two-dimensional search and rescue area; s2, performing grid decomposition on the two-dimensional search and rescue area; s3, obtaining a probability map through a Gaussian mixture model; s4, establishing an unmanned aerial vehicle search and rescue multi-target collaborative optimization function; s5, solving the function based on a hybrid particle swarm optimization algorithm to obtain an optimal solution; and generating a specific search and rescue path of the unmanned aerial vehicle according to the solved optimal solution. According to the method, an unmanned aerial vehicle search and rescue multi-target collaborative optimization function is established, the unmanned aerial vehicle is guided to execute the search and rescue task by taking resource balanced allocation, maximum path safety, preferential coverage of a high-risk area and minimum energy consumption as targets, task allocation, path planning and energy management are improved, multi-target optimization of multi-unmanned aerial vehicle search and rescue path planning is realized, and the search and rescue efficiency is improved. An accurate flight path is formulated for each unmanned aerial vehicle, comprehensive coverage of search and rescue tasks is ensured, and the search and rescue efficiency and success rate are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle task automatic planning and situation display system based on artificial intelligence. Comprising a task demand identification and priority dynamic adjustment module, a multi-task path optimization module, a multi-dimensional situation awareness and data fusion module, a three-dimensional situation display module, a task dynamic adjustment and feedback control module, a task data storage and historical analysis module and an emergency task response and environment interference adaptation module. By collecting task requirements and environment parameters in real time, the system can dynamically generate a task priority queue and optimize path planning based on priorities, it is ensured that the unmanned aerial vehicle has efficient response ability when executing tasks such as reconnaissance, monitoring, searching and rescue, the system combines a multi-source data fusion technology, the three-dimensional situation of the task environment is displayed in real time, and the system has good application prospects. Visual support is provided for decision making; the self-optimization capability and the adaptability to emergency situations lay a foundation for long-term intelligent development of unmanned aerial vehicle tasks.

The invention relates to the technical field of language model and agent perception control, provides a language prompt (Prompt) construction and task planning method based on a semantic scene graph, and solves the problems that in an existing system, a graph structure cannot adapt to a language model, and task planning lacks a perception closed loop. The method comprises the following steps: acquiring environmental data through a multi-modal sensor and constructing a semantic scene graph; automatically generating a language prompt based on the semantic graph structure, wherein the language prompt is used for guiding a language model to perform natural language task planning; wherein the language prompt adopts a multi-level template generation mechanism, room-object-attribute information is embedded into a structured template, hierarchical semantic blocks of an environment section, a target section and an operation section are formed, and the expression ability and generalization of a language model are improved; and a planning result output by the language model is implemented by the execution module. According to the method, a closed-loop path from a perceptual graph to language reasoning is established, and the method is suitable for a semantic task execution system in complex scenes such as unmanned aerial vehicle inspection, search and rescue and detection.

The invention discloses a robot unknown environment autonomous exploration navigation method based on CTSAC, and the method combines the powerful sequence perception capability of Transform and a Soft Actor-Critic deep reinforcement learning framework, and introduces curriculum learning of a regular review mechanism, laser radar partition optimization processing, and refined reward setting. The state of the robot is represented by laser radar data and gyroscope data which are subjected to laser radar partition optimization processing, meanwhile, after state information is subjected to refined reward setting, corresponding reward values are obtained, corresponding experience sequences are stored in an experience pool, and data obtained through sampling of the experience pool are supplied to a neural network for training. The method is suitable for the robot to perceive the surrounding environment through the sensor in an unknown environment to perform path planning so as to reach a specified target point or establish an environment map, and is used for complex tasks such as space exploration, search and rescue, reconnaissance and the like.

The invention discloses an unmanned ship distributed cooperative dynamic target surrounding control method under multiple constraints, and relates to the field of unmanned ship cooperative control. In order to solve the problems that multiple constraint coupling is not fully considered in cooperative surrounding of multiple unmanned ships, target state estimation is insufficient in an intermittent communication environment and the like, the following technical scheme is provided: firstly, a motion model of the unmanned ships and a target is established; a distributed internal model observer is arranged for each unmanned ship, and the internal motion state of the target is estimated; then designing a distributed type progressive average surrounding guidance algorithm, and performing distributed scheduling on the expected speed and heading of the unmanned ship, so that the unmanned ship can still keep safe surrounding when the speed or direction of the target is changed; the input saturation constraint of a propeller or a steering engine is considered, so that each unmanned ship can be autonomously controlled according to an expected trajectory and speed; and finally, stable and collision-free surrounding of the dynamic target is realized under multiple constraint conditions. The method can be applied to scenes such as ocean escort and maritime search and rescue which need to perform collaborative operation around a dynamic target.

The invention provides an intelligent unmanned cluster autonomous task planning scheme based on a large language model, the scheme mainly aims at the task planning problem of an unmanned cluster in a dynamic environment, the method adopts a Monte Carlo tree search method guided by the large language model and a global memory library, the search space is effectively constrained, the task planning efficiency is improved, and the task planning efficiency is improved. Specifically, a global memory library used for storing historical decision experience is constructed to realize reuse of historical experience so as to reduce the exploration cost of task planning, in addition, an early stop strategy and a reflection mechanism are introduced to correct the decision of a large language model according to errors generated in the search process, and the search efficiency is improved. The problem of decision errors caused by thinking circulation of a large language model is avoided, the search speed and decision robustness of an unmanned cluster task in a complex and changeable scene are further improved, and therefore an efficient and reliable technical scheme is provided for unmanned cluster cooperative combat, search and rescue monitoring and other multi-task scenes.

The invention discloses a cooperative positioning and trajectory planning method based on an air-ground robot system, and the method comprises the steps: enabling unmanned vehicles to construct a global environment map through a laser radar, achieving the positioning of the unmanned vehicles, obtaining the distances between the unmanned vehicles through the combination of an IMU (Inertial Measurement Unit) and a UWB (Ultra Wideband) distance measurement chip of the unmanned vehicles, and carrying out the real-time pose estimation. Based on the fused pose and global map information, a method of combining mixed A * search and nonlinear trajectory optimization is adopted, a collaborative trajectory planning problem is converted into a multi-constraint optimization model, the sight distance quality between air-ground robots, an unmanned vehicle team-shaped geometric structure and an unmanned aerial vehicle rapid navigation problem are comprehensively considered, and the space-ground robot cooperative trajectory planning method is established. And generating a collision-free optimized cooperative trajectory meeting multi-robot kinematics constraints in real time. According to the method, the positioning precision and the coordination degree of the air-ground coordination system in a complex dynamic environment are remarkably improved, and a safe, efficient and real-time heterogeneous multi-robot trajectory planning method is provided for various application scenes such as routing inspection, search and rescue, environment monitoring and the like.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an unmanned aerial vehicle cluster collaborative search and rescue algorithm based on reinforcement learning and a virtual simulation verification method, which are used for planning unmanned aerial vehicle cluster collaborative search and rescue tasks by combining the requirements of unmanned aerial vehicle cluster search and rescue and adopting an algorithm based on deep reinforcement learning. A virtual simulation verification platform is developed by utilizing Unity, a real unmanned aerial vehicle cluster model and a complex environment are established, and a real-time data transmission function with a reinforcement learning algorithm end is realized, so that the actual execution effect and effectiveness of the designed collaborative search rescue algorithm are verified.

The invention discloses an unmanned cluster search and rescue task planning method, system and device and a storage medium, and relates to the technical field of search and rescue, and the method comprises the steps: analyzing a multi-source distress signal of a region to be searched and rescued, and determining accident point information; dynamically updating the target discovery probability of each grid sub-region in the to-be-searched-and-rescued region based on the environment feature information and the target moving feature information of the to-be-searched-and-rescued region; determining a current task planning strategy based on the type number of the unmanned equipment in the unmanned cluster and the accident point information; and based on the current task planning strategy and the target discovery probability of the grid sub-region, determining a region search and rescue priority list of each unmanned device, and performing task allocation on the unmanned cluster. Through the above mode, task planning and path distribution are uniformly carried out on multiple types of unmanned equipment in combination with the environment characteristics and the target movement characteristics, accident points are rapidly covered, the search and rescue efficiency is improved, and the success rate of search and rescue tasks is improved.

The invention provides a smell traceability unmanned aerial vehicle and search robot cooperative navigation method and system, and relates to the technical field of intelligent search and rescue. According to the method, the information entropy is used as a main smell navigation basis, so that the search robot can still normally operate and accurately navigate in environments with poor visibility, such as smoke diffusion, night or low illumination, and the application scene of the search robot is expanded.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention belongs to the technical field of robot control, and particularly relates to a self-adaptive whole-body control method suitable for a quadruped robot in an uncertain dynamic environment, and the method integrates an extended state observer (ESO), a model prediction controller (MPC) and a whole-body controller (WBC) based on hierarchical optimization. And a closed-loop control architecture is constructed to improve the robustness and motion coordination ability of the quadruped robot in a complex environment. Firstly, external disturbance is estimated and compensated in real time through ESO; then, the MPC optimizes future system behaviors in a rolling manner under the condition of considering interference terms, and an expected foot end counter-acting force is output; and finally, the WBC is combined with the priorities of the multiple tasks to generate all joint control instructions in a constraint optimization form, and task collaboration and dynamic adaptation are achieved. The method has the advantages of being high in disturbance self-adaptive capacity, high in control precision, suitable for a multi-task environment and the like, and is particularly suitable for quadruped robot control tasks in complex dynamic scenes such as transportation and search and rescue.

The invention provides an interphone emergency rescue method and system based on ultra-low power consumption remote wakeup, and relates to the technical field of interphone emergency rescue, and the method comprises the steps: switching a normal working mode to an ultra-low power consumption mode according to a mode switching instruction generated by triggering a target emergency event; in the ultra-low power consumption mode, performing signal monitoring on a preset communication frequency band according to a preset monitoring strategy so as to receive a wake-up signal broadcasted by a first search and rescue user on the preset communication frequency band through a first search and rescue end interphone; when it is determined that the wake-up signal is received from the preset communication frequency band, performing security verification on the wake-up signal to switch the ultra-low power consumption mode to a normal working mode; and in the normal working mode, executing the target help-seeking operation. The technical problem that the emergency rescue efficiency of the interphone in the ultra-low power consumption mode is low in the prior art is solved, and the technical effect of improving the emergency rescue efficiency of the interphone in the ultra-low power consumption mode is achieved.

The invention relates to a heterogeneous unmanned ship task dynamic optimization method based on intelligent distributed CBBA, and relates to the technical field of unmanned autonomous cooperative control, the method comprises the following steps: firstly, using an intelligent distributed CBBA algorithm to realize task initial allocation, and constructing a cost function integrating distance cost, time cost and time window default penalty; and a task sequence is adjusted through a greedy strategy so as to meet strict time window constraints. And secondly, in the task execution process, a real-time monitoring and dynamic re-planning mechanism is established, and emergencies can be rapidly dealt with. A redistribution mechanism is introduced, the weight of a cost function is dynamically adjusted through a genetic algorithm, and loss and scheme optimization benefits caused by task execution subject change are balanced. Compared with the prior art, the task execution efficiency, reliability and adaptability of the heterogeneous unmanned ship cluster in a complex dynamic environment are effectively improved, and the method is suitable for complex application scenes such as maritime search and rescue, material delivery, patrol monitoring and the like.

The invention discloses a multi-unmanned aerial vehicle search and rescue task planning method based on ABC-DQN hierarchical optimization, and belongs to the technical field of multi-unmanned aerial vehicle cooperative search and rescue. According to the method, task planning is carried out on a search and rescue area applying multiple unmanned aerial vehicles based on an artificial bee colony algorithm and a deep Q network, and adaptive cooperative search and rescue of the multiple unmanned aerial vehicles is completed. According to the method, global planning of a search and rescue task and path planning of a local area are both constrained and optimized, scene analysis of a search and rescue area and selection of the number of unmanned aerial vehicles with different performances are firstly carried out, and the unmanned aerial vehicle of a central control node is determined; and then the central unmanned aerial vehicle completes static global region division by using an ABC algorithm, then unmanned aerial vehicles in a local region perform adaptive search and rescue path planning based on a DQN algorithm, and in a local search and rescue process, the local unmanned aerial vehicles and the central unmanned aerial vehicle perform information interaction. Once the target is determined to appear, the central unmanned aerial vehicle further reduces the global area and performs ABC algorithm division again until the search and rescue target is determined to be within the required range.

The invention discloses a full-aviation life intelligent detection and emergency rescue decision-making method and system. The method comprises the following steps: dividing emergency rescue scene gridding partitions; identifying the disaster degree and the number of affected people; combining an identification result with the gridding partition map to form a red, yellow, blue and green post-disaster gridding partition map; a full-aviation transient electromagnetic method life detector, an optical personnel detection device and a microwave radar life detection device are sequentially adopted to carry out step-by-step detection on the potential affected area, and a final replacement result of red, yellow, blue and green gridding target marking is formed; determining the priority level of ground rescue entering search and rescue according to the gridding partition map and the final replacement result; searching and inputting rescue result information of ground rescue entering search and rescue, and forming box counting statistics of grid information; a rescue decision suggestion is automatically generated according to a box counting statistical result, and efficient cooperation with a ground rescue team is formed; the method can effectively overcome the limitation of a single detection method in complex terrains and extreme environments.

The invention provides a multi-robot cooperative control method and system based on edge computing, and relates to the technical field of multi-robot control, and the method comprises the steps: dividing a search and rescue region into a plurality of sub-regions, generating a sub-region search and rescue task, and carrying out the search and rescue of the sub-regions according to the sub-region search and rescue task and the capability parameters and current states of ground robots. An optimal matching scheme of ground robots and sub-region search and rescue tasks is generated, different search and rescue strategies are adopted for different types of ground robots, and a heterogeneous robot cooperative search and rescue strategy is generated by defining formalized languages and lexical methods for cooperative control of the ground robots; each ground robot executes a search and rescue task in the corresponding sub-region according to the heterogeneous robot cooperative search and rescue strategy, when it is detected that the resource use condition or the network condition meets the preset condition, a calculation unloading decision is started, part of the search and rescue tasks are unloaded to other calculation nodes according to the decision result, and the search and rescue action continues to be executed. And the search and rescue task is completed.

The buried person search and rescue device comprises a portable drilling machine, the portable drilling machine is provided with a plurality of hollow drill rod sleeves, a gas conveying channel used for conveying gas to the ground is arranged in a drill rod, and a gas outlet of the gas conveying channel is connected with a sampling gas inlet of a gas component analysis device through a gas conveying pipe. A gas purifying and filtering device and a gas extracting pump for extracting gas are connected to the gas conveying pipe in series; a plurality of air inlets communicated with the air conveying channel are formed in the side wall of each drill rod sleeve in a surrounding mode, a jet flow nozzle is arranged at each air inlet in the drill rod sleeve, each jet flow nozzle is installed at an outlet of an injection pipe, each injection pipe is connected with an electromagnetic valve in series, and an inlet of each injection pipe is connected with the jet flow pipe. The invention aims to provide the device for searching and rescuing the buried persons, which can remarkably improve the search and rescue efficiency and effect in a complex geological disaster scene, and further realize the rapid search and rescue of the trapped and buried persons in earthquake, landslide and debris flow disasters and the search and rescue of the remains of the victims.

The invention relates to the technical field of infrared thermal imaging, and particularly discloses a fire personnel intelligent search and rescue method and device based on infrared thermal imaging, and the method comprises the steps: synchronously collecting fire scene data through an infrared thermal imager and a laser radar, so as to obtain infrared thermal imaging data and fire scene three-dimensional point cloud data; further introducing a data processing technology based on deep learning to carry out feature extraction on the infrared thermal imaging data and the three-dimensional point cloud data of the fire scene so as to capture infrared thermal radiation distribution features and three-dimensional space structure features of the fire scene, and then carrying out fine-grained feature alignment joint perception on the infrared thermal radiation distribution features and the three-dimensional space structure features; therefore, the deep correlation characteristics of the fire scene heat distribution and the three-dimensional space can be captured, and the intelligent detection of the human body target can be realized on the basis. In this way, the problem that heat radiation information and space structure information are separated in a traditional method can be effectively solved, the information utilization rate is increased, and the accuracy and efficiency of human body target recognition are enhanced.

The invention relates to the technical field of holders, and discloses a self-stabilization holder searchlight tracking system, which comprises a holder autonomous stabilization subsystem, a tracking control subsystem and a searchlight subsystem, and is characterized in that the holder autonomous stabilization subsystem comprises a servo motor, a servo motor connecting arm, a servo controller and an attitude position sensor; one end of the servo motor connecting arm is fixedly connected with the servo motor, the other end of the servo motor connecting arm is fixedly provided with a fixing structure, and the top of the fixing structure is provided with a data transmission interface. According to the self-stabilizing pan-tilt searchlight tracking system, stable operation of a searchlight at different angles and in complex environments is achieved through the pan-tilt autonomous stabilizing system, the system can automatically adjust the direction and angle of the searchlight according to the position of a target, it is ensured that continuous tracking is conducted in the target moving process, and efficient illumination is provided; the design improves the precision of target tracking and the stability of illumination, and is suitable for various application scenes such as night monitoring, security patrol, search and rescue and the like.

The invention discloses a deep-sea-oriented fault underwater vehicle positioning, searching and rescuing system and method. The system comprises a seabed environment construction module, an underwater vehicle position prediction module and an underwater vehicle searching module. The seabed environment construction module is used for constructing a seabed environment model of a sea area where the target underwater vehicle is located; the underwater vehicle position prediction module is used for constructing a position prediction model to predict the fault position of the target underwater vehicle in the seabed environment model when the target underwater vehicle breaks down; and the underwater vehicle searching module is used for searching and rescuing the target underwater vehicle based on the fault position. Aiming at the problems of position prediction and search and rescue of the deep-sea underwater vehicle, an underwater vehicle position prediction model based on federated Kalman filtering and a Markov chain Monte Carlo method is established, and searching and positioning of the underwater vehicle under the three conditions of normal navigation, power out-of-control and power failure are achieved.

The invention discloses an under-actuated unmanned ship distributed multi-task distribution method for sea rescue, and the method comprises the following steps: carrying out the clustering and grouping through employing a nearest neighbor clustering algorithm of a greedy strategy based on the position information of a person falling into water at sea, building a task cluster package, and carrying out the clustering and grouping of the task cluster package; constructing a target function of a target allocation problem of the rescue task of the unmanned ship at sea by taking the shortest search and rescue distance as a constraint condition, and taking the optimal time satisfaction as a rescue target; and solving the target function of the target allocation problem of the rescue tasks of the maritime unmanned ships by adopting a clustering consensus consistency packet algorithm to realize task allocation of the plurality of maritime search and rescue unmanned ships carrying the plurality of search and rescue unmanned ships. Compared with an existing unmanned ship task allocation method, the allocation method designed by the invention fully considers the kinematics constraint problem caused by the underactuation characteristic of the unmanned ship, so that the allocation result is closer to the path distance from the unmanned ship to the target point, and the task performability is improved.

The invention relates to the technical field of unmanned aerial vehicle systems, and discloses an unmanned aerial vehicle path planning method based on deep learning, and the method comprises the following working steps: 1, obtaining the flight environment data of an unmanned aerial vehicle in real time through a data collection module; 2, transmitting the collected data to a data storage module for storage, and providing the data for a data training module to use; and step 3, the data training module trains the stored data by using a deep learning algorithm, and optimizes a path planning model in the artificial intelligence module. According to the method, intelligent planning and optimization of the path of the unmanned aerial vehicle are realized through a deep learning technology, and the flight safety, flexibility and robustness of the unmanned aerial vehicle in a complex environment are improved in combination with mechanisms such as real-time data acquisition, safety early warning, system self-optimization and upgrading and the like. Through the 5G communication and data encryption technology, the high efficiency and safety of data transmission are ensured, and the method has wide application prospects in the fields of logistics, agriculture, environment monitoring, search and rescue and the like.

The invention relates to the technical field of fishing boat navigation positioning, in particular to a fishing boat dynamic drift prediction method and system based on South China Sea water area distress analysis. Constructing a dynamic drift prediction model of the target fishing boat and a future meteorological environment field model of the South China Sea water area, stripping a first future meteorological environment sub-simulation field of the current target fishing boat in the first static distress environment area through the future meteorological environment field model, and performing dynamic prediction in the dynamic drift prediction model, obtaining an initial dynamic drift route; and planning a second static distress environment area formed by dynamic drift of the current target fishing boat through the initial dynamic drift route to analyze a vector disturbance index of the meteorological environment, adjusting a second future meteorological environment sub-simulation field according to the vector disturbance index, and performing dynamic prediction again to obtain a final dynamic drift route. According to the method, the dynamic drifting route and coordinates of the fishing boat under the influence of the meteorological environment when the fishing boat is in danger in the South China Sea can be accurately predicted, so that the search and rescue efficiency and accuracy are improved.

The invention provides an intelligent marine emergency equipment automatic positioning method and system. Wherein multiple distress signals are received and fused, and optimal geographical location information and timestamps are generated; determining a distress area on a preset sea area electronic map, correcting a distress position, evaluating probability distribution of each potential distress area, and generating a primary search target with the highest probability; a thermal imaging and optical sensing system on the rescue unmanned aerial vehicle is started for scanning, and a high-accuracy potential survivor position is generated; the search and rescue path is dynamically adjusted and optimized, the search strategy is adjusted, the optimal flight path is updated in real time, and an economical and efficient search and rescue path plan is generated; and timely feeding back the accurate coordinates of the position of the potential survivor to the nearest rescue coordination center, and guiding first-aid equipment to go along the optimal path to implement rescue. According to the technical scheme provided by the invention, the search and rescue efficiency and accuracy are improved, and the position of a potential survivor can be quickly and accurately positioned.

The invention relates to the technical field of data processing, and provides an earthquake narrow space accurate rescue method using a micro intelligent robot. The method comprises the following steps: acquiring millimeter wave radar point cloud data and thermal imaging data of an infrared thermal imager based on a target area; a ruin environment three-dimensional model is prepared according to the data, and a passage path map is generated; self-adaptive networking is deployed for the micro robot cluster, and structural stability evaluation and residual vibration risk prediction are carried out in combination with the atlas; distributing detection partitions and rescue tasks of the micro robot cluster in real time based on an evaluation result; vital sign information is introduced, detection partition and task balanced distribution are optimized, and a parallel scheduling strategy is output. The technical problem that the rescue efficiency and accuracy in the narrow space are low due to the complex environment and incomplete sensing data is solved, and the technical effects that life search and rescue and task allocation are conducted in the narrow space through the micro robot cluster, and the accuracy and efficiency of earthquake narrow space rescue are improved are achieved.

The intelligent fire rescue robot capable of climbing the stairs comprises a tracked robot body, the tracked robot body comprises a body driving wheel, a shell and a bottom plate, a driving mechanism of the tracked robot body is installed on the rear portion of the robot, and two swing mechanisms are symmetrically installed on the front portion of the robot. A steering engine of each swinging mechanism is mounted below the adjacent bottom plate; each swinging mechanism comprises a steering engine, a swinging driving shaft, a swinging plate, a swinging driven wheel, a swinging crawler belt and a core-missing wheel; one end of the swinging driving shaft is connected with a driving steering engine shaft at the output end of the steering engine through a coupler, and the other end of the swinging driving shaft sequentially penetrates through a connecting plate connected with the bottom plate and the center-missing wheel and is connected with the swinging plate through a flange coupler; the upper space of the bottom plate is divided into two parts, one part is used for installing the stretching platform, and the other part is used for installing the detection execution module and the water storage tank. The system has better adaptability to complex terrains, and can realize the integration of discovery, fire extinguishment and search and rescue.

The invention discloses an unmanned ship intelligent search and rescue method and system, and the method comprises the steps: deploying an unmanned ship cluster driven by a multi-agent algorithm, collecting environment parameters, generating an initial cooperative path, starting an improved PV-RCNN model to detect a target in real time, and transmitting a result to an algorithm state space to adjust motion parameters. And repeatedly detecting and adjusting until a surrounding track is generated. The system comprises a distributed dynamic environment parameter acquisition unit, a multi-agent decision control unit and the like. According to the method and the system, the cooperative path is dynamically adjusted through the multi-agent algorithm, the target features are accurately extracted in combination with the improved target detection model, closed-loop linkage is formed, the defects that the adaptability to the dynamic environment is insufficient and target detection and control are disjointed in the traditional technology are overcome, and the search and rescue efficiency and accuracy are improved.