78 results about "Real-time path planning" patented technology

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

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship are improved.

The invention discloses an intelligent self-adaptive industrial raw material transportation system based on digital twinning, which comprises a multi-source positioning sensing module composed of a UWB positioning unit, an IMU compensation unit and an RFID verification unit, an edge calculation module and a dynamic actuator, the system integrates multi-sensor data through Kalman filtering to realize high-precision positioning, and based on a digital twinning model, the intelligent self-adaptive industrial raw material transportation system based on digital twinning is obtained. An improved deep reinforcement learning algorithm is adopted for dynamic obstacle prediction and real-time path planning, an optimal transportation path is generated, a dynamic actuator controls the transportation vehicle to move according to the planned path, and self-adaptive navigation and obstacle avoidance are achieved; through the improved deep reinforcement learning algorithm and in combination with dynamic obstacle prediction, the problem of path failure caused by a local optimal solution is effectively avoided, the path diversity exploration and dynamic obstacle avoidance capability in a complex environment is enhanced, and the planning robustness is improved.

The invention discloses an unmanned aerial vehicle real-time path planning system based on digital twinning, relates to the technical field of unmanned aerial vehicles, and aims to realize efficient and real-time path planning of an unmanned aerial vehicle in a campus traffic road environment. The core architecture of the system comprises a traffic flow monitoring end, a data analysis processing end, a digital twin model construction end and an unmanned aerial vehicle regulation and control end. A campus traffic digital twinborn model is constructed by using the traffic flow and the campus traffic road network three-dimensional diagram, the campus traffic flow in a future period of time is predicted in real time, and a prediction result is transmitted to the unmanned aerial vehicle regulation and control end; the number and flight paths of the unmanned aerial vehicles corresponding to the traffic roads are locked according to the predicted flow, accurate and dynamic path planning of the unmanned aerial vehicles is achieved, and the flight efficiency and adaptability of the unmanned aerial vehicles in the complex traffic environment of the campus are improved.

The invention provides an intelligent tactile stick interaction method and system based on multi-modal perception and edge calculation, and relates to the technical field of intelligent auxiliary equipment. The method comprises the following steps: acquiring environment and position data through a camera, a GPS and an inertial measurement unit; performing multi-modal data fusion and deep learning identification by using an edge calculation module to generate a dynamic environment map; performing real-time path planning and obstacle avoidance based on a map and a destination; analyzing a user instruction through voice recognition and interactively adjusting a path; haptic and auditory bimodal feedback is provided through a vibration module and a voice synthesis module according to a navigation result; and when an emergency obstacle is detected or a user actively alarms, a local and remote alarm mechanism is triggered. The system correspondingly comprises an intelligent tactile stick terminal and a cloud server. According to the invention, accurate and real-time perception and intelligent navigation of the environment are realized, visual and natural interaction experience is provided, an effective emergency help channel is established, and the safety, independence and convenience of travel of visually impaired people are significantly improved.

The invention discloses an unmanned aerial vehicle path planning method and system in an unknown obstacle environment, and particularly relates to the technical field of unmanned aerial vehicle autonomous navigation and path planning. Constructing an observation space containing a relative target position, a speed, an attitude angle, an angular speed and an obstacle; designing a continuous action space comprising total lift force, rolling moment, pitching moment and yawing moment; constructing a multi-dimensional award function fusing a task end award, a distance target award, a forward efficiency award and an obstacle avoidance safety award, and guiding an intelligent agent to optimize a path; on the basis of observation space and reward feedback, an intelligent agent control strategy is continuously optimized through exploration and development of a self-adaptive entropy regularization balance strategy in an SAC algorithm, and collision-free and short-path real-time path planning is achieved.

According to the online path planning method and system for the automatic polishing robot, the one-to-one mapping relation between the plane and the curved surface is established through orthographic projection, the complex curved surface is locally segmented into the simple curved surface, the algorithm time complexity of searching the coordinates of the path points on the surface of the model is reduced, the running speed is increased, and the real-time path planning requirement is met; according to the method, path planning can be directly carried out on point cloud data, the requirements for reconstruction algorithm precision and model quality are low, three-dimensional path planning can be converted into two-dimensional plane path planning, a two-dimensional path in any shape can be generated, and automatic and efficient online path planning of a robot is ensured.

The invention discloses a dynamic obstacle avoidance and path optimization method based on a body intelligence robot, and relates to the technical field of robot path planning, and the method comprises the following steps: collecting map data and task constraint data; performing path planning analysis on the map data and the task constraint data based on a set path optimization algorithm to generate initial planning path data; according to the intelligent robot dynamic obstacle avoidance and path optimization method, a robot actual driving path prediction model is constructed, and a target function is constructed for the deviation between the robot actual driving path prediction model and the real-time path planning data of the next time period to carry out minimization analysis; and the real-time path planning data of the next time period is optimized, so that the robot runs according to the optimized real-time path planning data to obtain an actual running path which fits the original real-time path planning data, and the running error of the robot is reduced.

The invention relates to a real-time path planning method and system based on conflict resolution, equipment and a medium. The method comprises the following steps: preprocessing original observation data to obtain an evidence unit of each sensor; calculating a total conflict amount and a sensor pair contribution matrix based on an evidence unit, updating the credibility of each sensor and selecting a fusion operator to obtain fusion evidence and a conflict degree after fusion; a short-term time sequence is generated according to the fusion evidence and the conflict degree after fusion, the expected occupation probability and the expected conflict trend of each grid unit are predicted, and a time sequence conflict map is obtained; according to the time sequence conflict map, conflict-level adjacent grid unit aggregation fuzziness and area-level conflict characteristics are carried out; and inputting the fusion evidence, the conflict fluctuation degree, the expected stable time and the region-level conflict feature as cost and constraint into a real-time local path planner to obtain a control decision. By adopting the method, the problem of path decision oscillation caused by multi-source sensor conflicts can be relieved.

The invention discloses a real-time processing method and system for robot dog environment perception and decision, and relates to the field of robot dogs. Carrying out multi-source data fusion and semantic segmentation based on deep learning; carrying out semantic enhanced 3D point cloud construction and target detection; real-time path planning and obstacle avoidance decision making in a dynamic environment; and carrying out adaptive motion control and system fault-tolerant processing. According to the invention, through four core technologies of multi-modal fusion, semantic enhancement, real-time calculation optimization and intelligent decision making, the robustness and real-time performance of multi-modal sensor fusion are significantly improved, and the precision of environmental perception is improved; calculation delay is greatly reduced, edge computing power distribution is optimized, and high-real-time decision making is achieved; the intelligent obstacle avoidance and path planning capability in the dynamic environment is enhanced, the intelligent decision-making capability in the dynamic environment is improved, the reliability and practicability of the system are improved, and an efficient, stable and low-cost solution is provided for large-scale application of the system in the fields of industry, security and logistics.

The invention discloses a multi-sensor data fusion method for an intelligent transfer device of power equipment, and the method comprises the steps: carrying out the dynamic weight fusion of multi-source data through building a measurement model of a plurality of visual sensors, and generating a high-precision measurement sequence; in combination with an improved adaptive Kalman filtering algorithm, process noise and a measurement noise covariance matrix are estimated in real time, and the stability and the anti-interference capability of state estimation are optimized. And based on the fused high-precision state information, a real-time path planning instruction is generated, and the transfer device is controlled to complete obstacle avoidance and millimeter-level docking of the cabinet body. The problems that traditional manual transfer efficiency is low, the positioning error is large, and a single sensor is prone to electromagnetic interference are solved, the navigation precision and operation safety of heavy equipment in the complex electromagnetic environment are remarkably improved, and the method is suitable for intelligent transfer scenes of equipment such as transformer substation circuit breaker handcarts and the like.

The invention discloses an AI path planning method and system based on deep reinforcement imitation learning, and the method comprises the steps: carrying out the weighted fusion through introducing a state incidence matrix, and constructing an environment state space with time sequence correlation; on the basis of a generative adversarial imitation learning framework, adversarial training of a strategy generation network and a strategy discrimination network is carried out by utilizing a pre-trained expert strategy database; a master-slave distributed architecture is adopted to train a plurality of strategy network copies in parallel, and global parameters of the strategy generation network and the strategy discrimination network are optimized through an aggregation gradient and parameter soft update mechanism; and deploying the lightweight strategy generation network, outputting an action instruction according to a real-time environment state, and obtaining a real-time path planning result through a dynamic path correction mechanism and online performance fine adjustment. The problem that the robustness, the safety and the efficiency of path planning of the robot in a complex environment are difficult to meet actual requirements is solved.

The application discloses a real-time path planning method fusing improved locust-dynamic window method, and comprises the following steps: S1, generating an initial path according to an improved A* algorithm, performing global path planning on the path in a radiation environment through an improved locust optimization algorithm, and determining the path from the starting point to the ending point; S2, performing local path planning according to the path through an improved DWA algorithm, and generating a planning path. Through the introduction of a position updating strategy considering radiation distribution information and an improved adaptive comfort zone coefficient, the influence of the radiation risk on path selection is more truly reflected, and the position updating process of the GOA is optimized. This enables the algorithm to effectively guide the locust population to avoid high radiation areas in a nuclear environment, and significantly reduces the radiation exposure risk. Through the combination of the global optimization capability of the improved A* algorithm, the initial population distribution of the locust optimization algorithm is improved, and then the convergence speed of the locust optimization algorithm is improved.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.

The invention discloses a real-time path planning method fusing an improved locust-dynamic window method, and the method comprises the following steps: S1, generating an initial path according to an improved A * algorithm, carrying out the global path planning of a path in a radiation environment through introducing an improved locust optimization algorithm, and determining a path from a starting point to an end point; and S2, performing local path planning according to the path through an improved DWA algorithm, and generating a planned path. By introducing a position updating strategy considering radiation distribution information and an improved adaptive comfort zone coefficient, the influence of radiation risk on path selection is reflected more truly, and the GOA position updating process is optimized. The algorithm can effectively guide the locust population to avoid a high-radiation area in a nuclear environment, and the radiation exposure risk is remarkably reduced. The initial population distribution of the locust optimization algorithm is improved by combining and improving the global optimization capability of the A * algorithm, so that the convergence speed of the locust optimization algorithm is improved.

The invention discloses a micro-nano robot magnetic control system and method based on ultrasonic image feedback, and belongs to the field of micro-nano robot magnetic control. The system is composed of an ultrasonic image processing module, a path planning module and a micro-nano robot control module. According to the magnetic control method, the input ultrasonic image is processed into a clearer map image, the position of the robot is detected, real-time path planning and control are conducted on the robot, and the control precision and the response speed of the system are remarkably improved. The provided micro-nano robot magnetic control system based on ultrasonic image feedback has the advantages of being high in control precision, high in sensitivity, high in response speed and the like.

The invention relates to a high-precision assembly path planning method for an artificial intelligence cooperative industrial robot, and the method comprises the steps: replacing the path planning of a high-dimensional configuration space of an assembly system with the multi-group path planning of a low-dimensional configuration space of an industrial robot under the condition that the mutual collision of the industrial robots is not considered; and then iteratively searching and detecting conflicts among the candidate paths, and eliminating the conflicts among the candidate paths by utilizing low-cost candidate path replacement and new candidate path generation so as to obtain collision-free candidate paths among industrial robots, and combining the collision-free candidate paths to obtain a plurality of groups of collision-free assembly system path planning results. And selecting the path planning result with the optimal cost from the path planning results of the assembly system as the final path planning result of the assembly system. Through the design, dimension reduction in the path planning process is achieved, the path planning calculation efficiency is improved, and real-time path planning of a complex industrial robot assembly system is better met.

The application discloses a kind of path planning systems of dike breach rescue equipment, comprising: environment perception module, for obtaining environmental data and position information, constructs rescue environment digital model;Path planning model is used to determine path planning based on rescue environment digital model;Path planning model is strategy network and safety prediction network coupling reward function, fusion reliability safety index component intelligent agent decision model, is trained based on PPO algorithm and is constructed generation;Wherein, strategy network is used to obtain shared features, to safety prediction network output action mean, according to total reward determines the path trajectory of equipment target;Interaction module is used to output real-time path planning result, risk probability and safety classification.According to the above technical scheme, the dynamic coupling of geological bearing capacity, water flow power, boat bridge stability and equipment dynamics is comprehensively considered, and global safety constraint is formed;Ensure the transportation safety of equipment in complex environment, greatly improve the efficiency of rescue.

The invention relates to the field of safety science and emergency management, and provides a real-time path planning method and device for personnel evacuation in a building fire scene, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a building fire map in real time, for any sampling moment, constructing a sampling evacuation path based on a preset algorithm, and forming a real-time evacuation path according to the change of the sampling evacuation path along with time; the preset algorithm comprises the following steps: constructing a preliminary evacuation path according to a preset path planning algorithm; constructing initial pheromone distribution of the ant colony algorithm according to the initial evacuation path, wherein the pheromone concentration on the initial evacuation path in the initial pheromone distribution is higher than the pheromone concentration of a common grid; and constructing a sampling evacuation path according to the initial pheromone distribution and an ant colony algorithm. The method has the technical effects of improving the ant colony algorithm search speed, improving the convergence speed, improving the reliability of the path planning result, and improving the personnel evacuation efficiency and the personnel evacuation safety.

The application discloses a tower crane real-time path planning system and method based on deep learning, which comprises a construction site environment real-time sensing module, a tower crane data acquisition module and a real-time path planning module.The construction site environment real-time sensing module is used for collecting and processing the spatial information of objects in the construction site environment, constructing a real-time construction site spatial model, and performing real-time three-dimensional collision prediction based on the model.The tower crane data acquisition module is used for collecting the operation actions of a tower crane driver and the movement state of the tower crane, forming the operation sequence of the tower crane driver and the movement state sequence of the tower crane according to time records.The real-time path planning module is used for training the neural network of a deep learning algorithm, learning the planning characteristics of the real tower crane driver for the object path, and simultaneously performing real-time dynamic adjustment on the planned path to avoid obstacles dynamically appearing in the construction site.The application guarantees the overall rationality of path planning, updates the path planning in real time according to the real-time information of the tower crane working scene, and avoids the interference caused by newly appearing obstacles in the operation process.

The application provides a micro-motion real-time tracking control system for an orthopedic surgery robot, which judges whether the bone tissue produces micro-motion by judging whether the contact force is consistent with the expected contact force, because when the bone tissue produces micro-motion, the bone tissue will give the cutter a reverse force, so that the contact force is inconsistent with the expected contact force; and when the micro-motion is produced, the contact force is close to the expected contact force by solving the optimal control input; that is, the application carries out real-time path planning through the state quantity fed back by the robot and the sensor, and does not need to use a neural network for prediction, so that the micro-motion real-time tracking is not dependent on the length of collected data and is not affected by the individual patient.

The embodiment of the invention provides a mobile robot sound source tracking method, a target mobile robot, equipment and a medium, and belongs to the technical field of robot intelligent control, the method is applied to the target mobile robot, and the method comprises the following steps: collecting and positioning a multi-channel audio signal to obtain an estimated navigation point; performing weighted average processing on the estimated navigation point and the historical navigation point to obtain a target navigation point, performing global and local path fusion planning on the environment states of the target navigation point and the target mobile robot, outputting a fusion control instruction, and performing navigation control according to the fusion control instruction. According to the embodiment of the invention, the estimated navigation point is generated by collecting and positioning the audio signal, and weighted average processing is performed by combining the historical navigation point, so that the sound source can be positioned only according to the sound source signal, and real-time path planning and control instruction generation are performed by further combining the environment state and the target navigation point; operation of the mobile robot can be ensured in a graph-free environment, and the tracking effect of the mobile robot is improved.

The invention relates to an automatic driving dynamic path planning method and system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting real-time path planning information; transmitting the real-time path planning information to a path planning model, wherein the path planning model outputs an optimal path analysis result; generating a specific path planning scheme based on the optimal path analysis result, and transmitting the specific path planning scheme to an execution mechanism of the automatic driving system; and the execution mechanism is used for controlling the vehicle to run according to the specific path planning scheme. By adopting the method, automatic driving path planning can be realized by means of a big data model.

The utility model relates to the technical field of robot intelligent inspection, in particular to a module for autonomous navigation and three-dimensional reconstruction of a robot dog, which comprises a robot dog main body, a mounting shell is mounted at one end of the top of the robot dog main body, and a switch is mounted at one end of the surface of the mounting shell; and a module host is arranged above the mounting shell, a laser radar is mounted at the top end of the module host, and cameras are fixed to the two sides of the surface of the module host. According to the utility model, through pressing the switch, the battery male connector and the battery female connector in the battery compartment are connected to supply power to the module host, then video photographing is carried out through the two cameras on the surface of the module host, inspection of a complex scene is completed, and detection induction is carried out through the laser radar on the top of the module host. And high-precision real-time path planning and obstacle avoidance are completed.

The invention relates to an unmanned aerial vehicle intelligent path planning method and system based on visual signals, and belongs to the field of path planning. The method comprises the following steps: optimizing a neural network through progressive pruning; fuzzy coordinates and scene types are provided for the unmanned aerial vehicle, the unmanned aerial vehicle flies to a fuzzy coordinate area, and when the unmanned aerial vehicle is close to the fuzzy coordinate area, the unmanned aerial vehicle is switched to a pure vision mode to perform environment perception through a neural network, and a three-dimensional semantic map is constructed; performing real-time path planning based on the three-dimensional semantic map to obtain a flight path; the method comprises the following steps: performing autonomous flight along a flight path, when approaching an operation target, performing autonomous operation to obtain real-time monitoring data, storing the real-time monitoring data in airborne storage equipment, and returning the real-time monitoring data after returning. And an unmanned aerial vehicle intelligent path planning system with high robustness, high autonomy and high task adaptability is constructed.

The invention relates to a multi-level optimized real-time path planning system and method, and belongs to the technical field of path planning. According to the system and the method, static environment data and dynamic environment data are collected, multi-source sensing information is fused to establish a high-precision environment model, and an initial path is generated. The scheduling module is responsible for dynamic optimization of the path, and improves the path adaptability and the system robustness in combination with vehicle dynamics constraints, path smoothness and environment change factors. In the path execution process, the risk assessment unit monitors the running state and potential risks of the vehicle at high frequency in real time, and dynamically triggers path replanning or speed adjustment based on a risk assessment result. And the path monitoring unit continuously tracks the deviation between the current position of the vehicle and the target path, and performs path correction and speed regulation and control if necessary. According to the invention, a modular architecture and a self-adaptive scheduling strategy are adopted, so that efficient collaboration among different levels is realized.

The invention discloses a real-time path planning method and system for a fixed-wing unmanned aerial vehicle in simulated flight and electronic equipment. The real-time path planning method comprises the following steps: S1, importing discrete elevation data of a task area; s2, modifying the current route of the unmanned aerial vehicle based on the security constraint, and calculating discrete points between the current point of the unmanned aerial vehicle on the current route and the latest route point; s3, the discrete points are initialized; and S4, planning an optimal path of the unmanned aerial vehicle by using an algorithm and the initialized discrete points. According to the method, the bidirectional A * algorithm is adopted to synchronously initiate search from the starting point and the target point, and the invalid search range in a complex three-dimensional space is greatly reduced.

The invention discloses an automatic warehousing system for intelligent warehousing, and relates to the technical field of intelligent warehousing. The system comprises a warehouse management system, a transfer robot, a warehousing temporary storage area and a warehouse area. The system realizes automatic warehousing through the following steps: an operator inputs article information in the warehouse management system and uploads a warehousing task; the system retrieves the article information, judges whether the article is a new warehousing article or fills up an existing article, and pushes a task to the transfer robot; the transfer robot goes to the storage area according to the planned path to take vacant or non-full storage boxes; after an operator puts articles into the storage box, the articles are conveyed to the output table through the conveying table; and the transfer robot takes the goods and returns to the goods shelf. According to the system, through priority management, real-time path planning, positioning identification navigation and a multi-robot cooperative obstacle avoidance mechanism, the warehousing process is optimized, and the automation level and efficiency of warehouse management are improved.