39results about How to "Improve the efficiency of performing tasks" patented technology

The invention provides an auto-management data link of micro unmanned aerial vehicles, which comprises a physical medium which is 2.4 to 2.483GHz microwaves, a link protocol which is ZigBee protocol is an international standard IEEE802.15.4-based wireless network protocol, and equipment formed by connecting a micro unmanned aerial vehicle model with a wireless module and an antenna, which are fixedly arranged in the unmanned aerial vehicle model, through a serial port RS-232. The wireless module consists of a ZigBee coordinator and a ZigBee router and adopts a half duplex communication mode; a data link network topology is a tree topology; and data link simulation software is the 14.5 edition of OPNET network simulation software. The data link allows the transmission speed of the unmanned aerial vehicles to reach 250kbps; the channel utilization rate can reach 90 percent; the packet transfer delay is 0.4s; and the throughput of the data link is 63kbps.

The invention relates to a four-rotor multipurpose flying robot which comprises rotorcrafts and a control device, wherein each rotorcraft comprises a base plate, propellers, a wireless signal transceiver, a driving device, a controller and an electric energy storage device; the propellers are in transmission connection with the driving devices through transmission mechanisms; the driving devices and the wireless signal transceivers are electrically connected with the controllers; the controllers are electrically connected with the electric energy storage devices; an anti-collision ring is arranged on the outer side of the propellers of each rotorcraft; the anti-collision rings are fixedly connected to the upper sides of the four corners of the corresponding base plate; an ultrasonic sensor is arranged on the outer wall of one side, away from each base plate, of the corresponding anti-collision ring; the ultrasonic sensors are electrically connected with the controllers; support legs are fixedly arranged on the lower sides of the four corners of each base plate. According to the four-rotor multipurpose flying robot, the ultrasonic sensors are arranged on conventional rotorcrafts, so that the probability of collision of the flying robot in a flying process is reduced, the controllability of the flying robot s improved, meanwhile, flying of the flying robot in a narrow passage is facilitated, and task performance efficiency is improved.

The invention provides a DDPG (Deep Deterministic Policy Gradient)-based unmanned aerial vehicle autonomous guidance control method. The method comprises the steps of: establishing an unmanned aerialvehicle three-degree-of-freedom motion model, an unmanned aerial vehicle maneuvering control model, a reference terrain three-dimensional model and a peak three-dimensional model; calculating a terrain obstacle influence degree value of an unmanned aerial vehicle at the current position; constructing an evaluation network, a strategy network and a corresponding target network, and training the evaluation network and the strategy network; and using the training result as an unmanned aerial vehicle flight control outer loop controller to control the two-way overload and the unmanned aerial vehicle speed inclination angle of the unmanned aerial vehicle. According to the method, a deep reinforcement learning method is combined with unmanned aerial vehicle guidance control, learning training iscarried out in an offline simulation environment, practical application is carried out after requirements are met, the autonomy of the unmanned aerial vehicle in the task execution process is greatlyenhanced, and the task execution efficiency of the unmanned aerial vehicle is improved.

The invention provides a DDQN-based autonomous guidance maneuver decision-making method for an unmanned aerial vehicle, which is an unmanned aerial vehicle autonomous guidance maneuver decision-makingmethod based on combination of a priority sampling double-depth Q learning algorithm and a Markov decision-making process; the double-Q learning algorithm is introduced to improve the iteration modeof the deep Q learning algorithm, and the training efficiency is improved. A priority sampling method is adopted to promote rapid convergence of the algorithm, and the diversity of historical data isbetter utilized; the unmanned aerial vehicle can realize autonomous guidance maneuvering decision making according to the external flight environment state, and completes autonomous guidance maneuvering decision making under a fixed target point to effectively improve the flight autonomy of the unmanned aerial vehicle. According to the method, the over-fitting problem existing in the DQN algorithmis solved, the offline training efficiency of the autonomous guidance maneuvering decision-making method of the unmanned aerial vehicle is greatly improved, the autonomy of the unmanned aerial vehicle in the flight process is enhanced, and the task execution efficiency of the unmanned aerial vehicle is improved.

The invention discloses an anytime recovery sparse A<*> and Kalman filtering-based dynamic route planning method, and belongs to the technical field of unmanned aerial vehicle route planning. The implementation method comprises the steps of firstly, estimating dynamic threat and a position of a moving target in a next moment task environment by a self-adaptive expansion Kalman filtering algorithmwithin a target observation period T according to a rolling planning strategy, and taking an estimation result as input information of unmanned aerial vehicle route planning; and secondly, performingunmanned aerial vehicle rapid route planning by employing the Anytime recovery sparse A<*> algorithm so that an unmanned aerial vehicle can sense the environmental changes in advance, a route direction is changed, a practical route is output within limited time, the dynamic threat is smoothly prevented and a target region is accurately reached. Under the condition that the dynamic threat and the moving target exists in a real task environment of the unmanned aerial vehicle, all threats all can be enabled to be prevented by the unmanned aerial vehicle, and the time of reaching the target regionis shortened. The anytime recovery sparse A<*> and Kalman filtering-based dynamic route planning method has the characteristics of high solution efficiency and high robustness.

The invention discloses an AGV-based control method, which comprises the steps of acquiring task information corresponding to a task request upon receiving the task request; sorting loading points corresponding to each piece of the loading point information to obtain a first sequence of loading task stations corresponding to the loading points; obtaining a second sequence of discharging task stations corresponding to various pieces of discharging point information; and controlling a target AGV corresponding to the task request to execute a loading task corresponding to the loading point information according to the first sequence, and controlling the target AGV to execute a discharging task corresponding to the discharging point information according to the second sequence when the loading task is completed. The invention also discloses an AGV-based control method apparatus and a computer readable storage medium. According to the invention, multiple tasks can be performed by the same AGV, the utilization rate of the AGV is further increased and the execution task efficiency of the AGV is improved.

The invention belongs to the technical field of intelligent mechanical equipment, and particularly relates to an intelligent unmanned aerial vehicle hangar based on an automatic battery replacing device. The intelligent unmanned aerial vehicle hangar comprises a hangar cabin body, a parking apron and the rotatable automatic battery replacing device, wherein a two-degree-of-freedom mobile device ismounted on a hangar cabin door, and the two-degree-of-freedom mobile device can enable a telescopic electromagnetic adsorption device to move in a plane; the parking apron is a split type telescopicparking apron, and can move back and forth through a parking apron translation rod; and the rotatable automatic battery replacing device is installed at the bottom of the cabin body, the main shaft ofthe automatic battery replacing device can move up and down and rotate, and a laser positioning device and an image recognition device are arranged at the top of the main shaft so that batteries canbe autonomously and efficiently replaced through the cooperation of an airborne battery box.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on a binocular vision system. A binocular vision system acquires image information to obtain depth map information; with an ecological method, denoising is performed on the depth map by combining an exponential weighted moving average algorithm; an obstacle contour is extracted according to a threshold segmentationalgorithm based on region growing and fitting is performed by using a rectangular box to obtain a parallax value; an obstacle distance is calculated by the similar triangle theorem; the obtained distance is transmitted to a flight controller and is compared with a preset threshold distance, different commands are sent out, and then an unmanned aerial vehicle completes obstacle avoidance at accurate timing. According to the invention, on the basis of combination of the morphological method with the exponential weighted moving average algorithm, the denoising degree of the depth map is increased, the calculation accuracy of the distance between the unmanned aerial vehicle and the obstacle is improved, the fast visual obstacle avoidance capability of the unmanned aerial vehicle is enhanced, and the accuracy is greatly improved by being compared with the existing algorithms.

An unmanned aerial vehicle control method and device, an unmanned aerial vehicle, a system, and a storage medium, the method comprising: generating a flight strategy for unmanned aerial vehicles in an unmanned aerial vehicle group (S201), the flight strategy comprising a flight instruction for instructing the unmanned aerial vehicles to fly to a target position at a target time; sending flight information to the unmanned aerial vehicles in the unmanned aerial vehicle group (S202), the flight information comprising the generated flight strategy; performing clock synchronization processing on the unmanned aerial vehicles in the unmanned aerial vehicle group, so as to set a reference time for the unmanned aerial vehicles (S203); and sending a takeoff instruction to the unmanned aerial vehicles in the unmanned aerial vehicle group (S204), the takeoff instruction being used for triggering the unmanned aerial vehicles to fly, with the reference time as a standard reference time and according to the instruction of the flight strategy, to the target position at the target time. The method achieves the coordinated operation of unmanned aerial vehicles in an unmanned aerial vehicle group, improving the efficiency of the unmanned aerial vehicle group executing a task.

The embodiment of the invention provides a communication method and device based on RPA, equipment and a storage medium, relates to the field of RPA, and adopts the specific implementation scheme thatthe method is applied to an RPA robot execution end, and comprises the following steps: S1, receiving a task processing request message sent by a robot automation process management end, wherein thetask processing request message comprises task identification information; S2, processing a task corresponding to the task identification information according to the task processing request message,and generating a corresponding task response message; and S3, sending the task response message to the robot automation process management end. As the management end of the RPA robot can actively issue the task processing request message to the execution end of the RPA robot, the pressure of the management end of the RPA robot is effectively reduced, and the task execution efficiency of the execution end of the RPA robot is improved.

The invention relates to the field of unmanned aerial vehicle group task planning, and discloses a distributed online adaptive task planning method for an unmanned aerial vehicle group. In order to obtain a global optimal task planning effect and improve the efficiency of searching and strike task execution of the whole unmanned aerial vehicle group, state transition rules are optionally optimized, two task modes of reconnaissance search and attack of unmanned aerial vehicles are designed, and the unmanned aerial vehicles adaptively select the state transition rules according to task modes, so that the unmanned aerial vehicles can quickly approach and strike a target, and the existence time of the target is shortened. When a target attack task is allocated, the consumption attack condition of the unmanned aerial vehicles to the target in the local range is considered in advance, only part of the unmanned aerial vehicles are summoned to attack the target, and the other unmanned aerial vehicles still can execute a search task, so that the task execution efficiency of the whole unmanned aerial vehicle group is improved.

The invention belongs to the technical field of robots and discloses a robot control system. The robot control system comprises a camera shooting module, a temperature and humidity detection module, asingle-chip microcomputer control module, a wireless communication module, a computer, a voice module, a mobile module, a positioning module and an abnormity processing module. By means of the positioning module, the robot positioning mode is simplified, and the positioning accuracy and the positioning accuracy are improved. By means of the abnormity processing module, an abnormity detection instruction is sent to a robot, and then the abnormal information of an abnormal task detected by the robot is received. According to the abnormal information, a risk level corresponding to the abnormal task is determined. After that, an abnormity processing strategy matched with the risk level is issued to the robot. As a result, the problem that due to the robot is abnormally stopped, tasks cannot be executed in time by the robot can be avoided. The task executing efficiency of the robot is improved.

The invention discloses a fixed home teaching robot with a rotatable base. The fixed home teaching robot comprises a teaching robot body. The teaching robot body comprises the rotatable base and a robot shell installed on the rotatable base. A driving device, an intelligent processing system and a control module are arranged in the robot shell. The driving module is connected with the intelligent processing system and the control module. The control module is connected with the rotatable base. The rotatable base is provided with rotating shafts comprising one rotating shaft in the horizontal direction and the other rotating shaft in the vertical direction. The control module can control the facing direction of the teaching robot body through the rotating shafts. The fixed home teaching robot with the rotatable base is simple in structure and convenient to operate, provides automatic training and teaching, is more intelligent and user-friendly and helps students of all ages to complete some study tasks and relieve study pressure of the students, task executing efficiency of the robot can be improved, and energy consumption can be reduced.

The invention relates to a mobile robot path planning method and system, and the method comprises the steps: generating a plurality of feasible paths through an RRT * algorithm according to the operation constraint condition of a mobile robot; calculating the path time cost of each feasible path by utilizing a turning characteristic function, and selecting the feasible path with the minimum path time cost as the planned path of the mobile robot, so that the planned path with the shortest time can be planned for the mobile robot, the path time cost of the actual task of the robot is reduced, and then the task execution efficiency of the mobile robot is improved.

The invention discloses a method and device for avoiding robot collision, and relates to the technical field of computers. One embodiment of the method comprises the steps of generating a datum pointset from a start point to a target point through path planning, and acquiring interference area identities corresponding to datum points of the datum point set; splitting the datum point set accordingto the interference area identities corresponding to the datum points, and setting interference area identities for multiple child routes obtained through split respectively; and locking the corresponding interference areas according to the interference area identities of the child routes, and issuing route instructions after locking succeeds to enable the robot to enter an interference area, wherein the interference area is acquired by dividing the datum points according to distribution of the datum points in a target area. According to the method for avoiding robot collision, because the interference area is marked off beforehand, and the interference area identities are set for the child routes, the corresponding interference area is locked according to the interference area identitiesof the child routes, it is guaranteed that only one robot can enter the interference area, and robot collision is avoided.

The invention discloses a deep neural network partitioning method for an unmanned aerial vehicle and an edge computing server. The method comprises the steps of: firstly building a system cost model, then converting a partitioning decision problem in the process in which an unmanned aerial vehicle and a edge computing server execute DNN into a particle optimization problem, and solving the binary particle optimization problem based on a chaotic variation binary particle swarm algorithm to obtain the optimal partition point between the unmanned aerial vehicle and the edge computing serve. Computing resources of the unmanned aerial vehicle can be utilized to the maximum extent, the system energy consumption and total time delay cost are minimized, and the task execution efficiency of the unmanned aerial vehicle is effectively improved. According to the invention, the DNN can be reasonably divided between a mobile device and the edge computing server according to the hierarchical structure and the output data size of the DNN and the energy consumption required by each layer of network computing.

The embodiment of the invention discloses a task matching method and device for a manufacturing workshop, relates to the technical field of intelligent manufacturing, and can reasonably allocate tasks according to the ability of personnel and improve the task execution efficiency of the personnel. The method comprises: according to a workshop ID, extracting a personnel ID associated with the workshop ID from a personnel information database, and extracting skill information corresponding to the personnel ID, the skill information comprising skill type information and skill weight information; querying a task demand template according to the task information, and determining a skill type and skill weight information required for executing the task; determining personnel information of which the matching degree meets the task demand template, and determining personnel participating in the task; after the task is executed, the result of the task is scored according to historical task data, and the integral value of the personnel participating in the task is obtained; and according to the obtained integral value, updating skill weight information of the personnel participating in the task.

The invention discloses a method and a device for creating a navigation map of mobile equipment and a storage medium, relates to the technical field of artificial intelligence, and aims to reduce the calculation pressure of the mobile device and improve the task execution efficiency by performing navigation based on the navigation map created by the technical scheme of the invention. The method comprises the steps that a scanning map of a to-be-mapped area is obtained, wherein the to-be-mapped area at least comprises a first floor area, a second floor area and a communication area, the first floor area and the second floor area are the same floor area of different buildings, and the first floor area is connected with the second floor area through the communication area; at least two navigation maps are created for the to-be-mapped area according to the scanning map, an overlapping area is created for the at least two navigation maps, and the overlapping area is used for the mobile device to achieve switching of the navigation maps.

The invention discloses a method and a device for preventing robot collisions, and relates to the technical field of computers. A specific implementation of the method includes: generating a set of reference points from the starting point to the target point through path planning, and obtaining the identification of the interference area corresponding to the reference point of the set of reference points; according to the identification of the interference area corresponding to the reference point, the set of reference points Carry out splitting, and set the interference area identification for the multiple sub-routes obtained from the splitting; lock the corresponding interference area according to the interference area identification of the sub-routes, and issue routing instructions after the locking is successful, so that the robot enters the interference area; among them, the interference The area is obtained by dividing the reference points according to the distribution of the reference points in the target area. The method divides the interference area in advance, and sets the interference area identification for the sub-routing, so as to lock the corresponding interference area according to the interference area identification of the sub-routing, so as to ensure that only one robot can enter the interference area, and avoid robot collision.

The embodiment of the invention provides a task process triggering method and device based on NIFI, and the method comprises the steps: triggering the judgment of an execution record of a first Flow task through controlling the periodic polling of a second StartPoint Processor configured in a second Flow task, and according to the task execution instance information recorded by a first EndPoint Processor and a first triggering condition defined by the first StartPoint Processor, judging whether the first Flow task execution instance information meets the first triggering condition or not, if so, triggering the subsequent Processor to execute the task, and if not, not triggering the subsequent Processor to execute the task. Task execution can be triggered on the basis of the logic relation between NIFI dependent data collection and summarization tasks, and the task execution efficiency is improved.

The invention discloses a path width determination method, a robot and a computer readable storage medium. The path width determination method comprises the following steps: acquiring position information of a current position of a first robot in a grid map; according to the position information and the contour information of the first robot, determining a passing path of a second robot in the grid map when the first robot and the second robot meet at the current position; and determining the path width of the path, and sending the path width to a second robot, so that the second robot determines whether to execute a task through the path according to the path width. The task execution efficiency of the robot can be improved.

The invention relates to a remote control starting system for a piston engine unmanned aerial vehicle, which belongs to the field of aviation unmanned aerial vehicles, and comprises an airplane-boardsystem and a ground command and control system. The airplane-board system starts the airplane engine, collects system parameters, receives and sends and controls data during the flying process. The ground command and control system displays the work state of the unmanned aerial vehicle in a real-time manner to a manipulator of the unmanned aerial vehicle, and sends instructions input by the manipulator of the unmanned aerial vehicle to the airplane-board system, and conducts remote control on the unmanned aerial vehicle. Data interaction between the airplane-board system and the ground commandand control system are conducted through an airplane-board data sending / receiving terminal and a ground data sending / receiving terminal. The invention is advantageous in that under the situation thatstorage batteries in the airplane are sufficient in power, no multiple returning maintenance is required when quite a large amount of work conditions occur; the technical problem of time and labor consuming due to the fact that support personnel need to conduct multiple maintenance through multiple trip under the current situation can be resolved; the airplane preparation time can be shortened, and the system is especially suitable for work fields without access to the unmanned aerial vehicles.

The invention discloses a charging matching method and device and a storage medium, relates to the technical field of artificial intelligence, and can reduce the situation that a mobile device cannot execute a task due to low power by setting an automatic charging task for the mobile device, thereby improving the task execution efficiency of the mobile device. In addition, the average waiting time of the mobile equipment in queuing at the charging pile can be reduced, and the charging efficiency is improved. The method comprises the following steps: acquiring a task list of the mobile equipment under the condition of determining that the mobile equipment meets a first preset condition; the first preset condition comprises that the residual electric quantity of the mobile equipment meets a triggering condition for executing an automatic charging task; and matching a target charging pile for the mobile device according to the working state of the candidate charging pile, and updating the matching state of the mobile device and the target charging pile according to the task list.

The present invention provides a DQN-based method for autonomously generating a maneuvering strategy of an unmanned aerial vehicle, which respectively establishes a three-degree-of-freedom motion model of the unmanned aerial vehicle, a maneuvering control model of the unmanned aerial vehicle, a three-dimensional model of a reference terrain and a three-dimensional model of a mountain peak; The value of the influence degree of terrain obstacles in the position; build the evaluation network and the target network, and train the evaluation network; use the training result as the UAV flight control outer loop controller to control the two-way overload of the UAV and the speed of the UAV Tilt angle. The invention combines the deep reinforcement learning method with the guidance and control maneuvering strategy of the UAV, conducts learning and training in an offline simulation environment, and then implements practical application after meeting the requirements, which greatly enhances the UAV's ability to perform tasks. Autonomy, which improves the efficiency of UAVs in performing tasks.

The invention discloses a robot elevator selection method. The method comprises the following steps that an elevator configuration file issued by a cloud is received, and a list of all available elevators in a current building is obtained; the occupancy states of all the elevators in the available elevator list are obtained through multi-machine communication; the elevators currently occupied by other robots in the available elevator list are filtered, and a current selectable elevator list of the robot is obtained; and the robot is controlled to select any elevator in the selectable elevator list. According to the robot elevator selection method, after the available elevator list of the current building is obtained, the occupation states of all the elevators in the list are continuously obtained, so that the occupied elevators in the elevator list are filtered, subsequent elevator taking failures are avoided, the task execution efficiency of the robot is improved, and accidents are reduced.

The invention provides a DDPG-based autonomous guidance and control method for an unmanned aerial vehicle, which respectively establishes a three-degree-of-freedom motion model of the unmanned aerial vehicle, a maneuvering control model of the unmanned aerial vehicle, a three-dimensional model of a reference terrain and a three-dimensional model of a mountain peak; calculates the current position of the unmanned aerial vehicle The value of the degree of influence of terrain obstacles under the control system; build the evaluation network, the strategy network and the corresponding target network, and train the evaluation network and the strategy network; use the training result as the UAV flight control outer loop controller to control the two UAVs. Tilt angle to overload and drone speed. The invention combines the deep reinforcement learning method with the guidance and control of the unmanned aerial vehicle, conducts learning and training in an offline simulation environment, and then implements practical application after meeting the requirements, which greatly enhances the autonomy of the unmanned aerial vehicle in the process of performing tasks, and improves the the efficiency of the UAV's mission.

The invention relates to a four-rotor multi-purpose flying robot, which includes a rotorcraft and a control device. The rotorcraft includes a chassis, a propeller, a wireless signal transceiver, a drive device, a controller and an electric energy storage device. The propeller is connected to the drive device through a transmission mechanism. , the driving device and the wireless signal transceiver are electrically connected to the controller, and the controller is electrically connected to the electric energy storage device. There is an anti-collision ring on the outside of the propeller, and the anti-collision ring is fixedly connected to the upper side of the four corners of the chassis. The anti-collision ring Ultrasonic sensors are arranged on the outer wall away from the chassis, and the ultrasonic sensors are electrically connected to the controller, and legs are fixed on the lower sides of the four corners of the chassis. The present invention reduces the probability of a flying robot colliding during flight by arranging an ultrasonic sensor on a traditional rotorcraft, improves the controllability of the flying robot, facilitates the flying of the flying robot in a narrow passage, and improves the efficiency of performing tasks. efficiency.

The invention discloses an AGV-based control method, which includes the following steps: when receiving a task request, obtaining task information corresponding to the task request; sorting the loading points corresponding to each of the loading point information, so as to Obtain the first order of the loading task sites corresponding to each of the loading points; obtain the second order of the unloading task sites corresponding to the information of each of the unloading points; control the tasks corresponding to the task requests according to the first order The target AGV executes the loading task corresponding to the loading point information, and when the loading task is completed, controls the target AGV to execute the unloading task corresponding to the unloading point information according to the second sequence. The invention also discloses an AGV-based control method device and a computer-readable storage medium. The invention enables the same AGV to perform multiple tasks, thereby improving the AGV utilization rate and improving the efficiency of the AGV performing tasks.

The invention discloses a multi-unmanned aerial vehicle cooperative operation scheduling method and system and a storage medium, and the method comprises the steps: assigning stage tasks to unmanned aerial vehicles without task execution, and enabling the landing time of all unmanned aerial vehicles in the same group to be staggered; planning a stage flight path for the corresponding unmanned aerial vehicle according to the stage task, wherein an end point of the stage flight path is a drop point of the unmanned aerial vehicle; controlling each unmanned aerial vehicle to fly along the corresponding stage flight path to execute operation; and controlling the battery replacement robot to move to the landing point of each unmanned aerial vehicle and carrying out battery replacement service on the unmanned aerial vehicle corresponding to the landing point. The landing time of the unmanned aerial vehicles is staggered, so that the battery replacement robot can move to the landing points of the unmanned aerial vehicles in sequence to provide battery replacement service for the unmanned aerial vehicles, the idle running time of the unmanned aerial vehicles can be shortened, and the battery power of the unmanned aerial vehicles can be fully applied to task execution; and the interval time between the tasks in each stage is short, so that the task execution efficiency of the unmanned aerial vehicle can be improved.