3974results about How to "Avoid collision" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a road surface automatic inspection robot path planning method and device based on artificial intelligence, and the method comprises the steps: collecting road surface environment data in real time through various sensors, carrying out the feature extraction through a convolutional neural network based on the road surface environment data, and constructing a dynamic environment model through a graph convolutional network. According to the method, the change trend of potential road surface damage areas and obstacles is predicted, path planning and optimization are performed by using a deep reinforcement learning algorithm, after path planning is completed, the robot autonomously travels according to a planned path, and real-time adaptive adjustment is performed on the path through an online learning method so as to cope with environmental changes. Collected data and path planning information are fed back to the central control unit in real time to be stored, and a road surface health condition report is generated. According to the invention, the efficiency and the precision of the inspection task are improved, and the method has high environmental adaptability and is suitable for complex and dynamically changing road environments.

The invention discloses an unmanned vehicle formation cooperative hunting method, device and equipment based on pilot following, and a medium, and relates to the technical field of multi-agent control. The method comprises the following steps: determining role division of a leading vehicle and a following vehicle, planning a path by the leading vehicle through an improved fast random exploration tree algorithm, sending the path and a control strategy to the following vehicle in stages, dynamically adjusting the position and speed by the following vehicle according to an instruction, ensuring formation consistency and task execution, and realizing information sharing between unmanned vehicles through a communication network. The pilot vehicle broadcasts a path and a task instruction in real time, and the following vehicles receive and analyze the path and the task instruction and then dynamically adjust the motion trail, thereby ensuring the cooperative consistency of the formation and the efficient stability of information transmission; each vehicle executes a hunting task according to a task instruction and adjusts a motion track and speed in real time, the following vehicle keeps a formation according to a path instruction, the pilot vehicle dynamically adjusts a global path according to environment change and a target position, the instruction is updated in real time through a communication network, and finally effective hunting and capturing of a target are realized. The method is suitable for a target hunting task in a complex environment, and reliable technical support is provided for a multi-target complex scene.

The invention discloses a humanoid robot grabbing method based on three-dimensional vision, and the method comprises the following specific steps: S1, environment modeling and perception: carrying out the three-dimensional scanning of a working environment, and constructing a three-dimensional map of the environment; s2, object identification and 6D attitude estimation: identifying a target object, and estimating the position and attitude of the object in a camera coordinate system; s3, coordinate conversion and target positioning, wherein the posture of the object is converted into a robot base coordinate system from a camera coordinate system through TF conversion; and S4, path planning and motion control are conducted, specifically, path planning is conducted through a cumotion algorithm, a grabbing path is generated, and the two arms are driven to move according to the planned track to achieve grabbing. By means of environment modeling and sensing, the robot can update environment information in real time, conduct three-dimensional scanning on the working environment and generate the three-dimensional grid model of the environment in real time, the model is used for obstacle detection and avoidance planning, and the dynamic environment adaptability of the robot to object grabbing is improved.

The invention belongs to the technical field of robot obstacle avoidance, and particularly relates to an anti-explosion humanoid double-arm robot obstacle avoidance path planning method which comprises the following steps: acquiring a three-dimensional model of double mechanical arms; the joints, the connecting rods and the environmental obstacles of the double mechanical arms are enveloped by the spheres; the improved Bi-RRT algorithm is adopted to conduct global path planning on the double mechanical arms; whether the double-mechanical-arm global planning path collides or not is judged, if yes, the path is planned again, the master arm and the slave arm are determined, and if not, the global planning path is executed; and whether the distance between the obstacle and the joint or the connecting rod of any mechanical arm is smaller than a set safety threshold value or not is judged, if yes, an artificial potential field local obstacle avoidance mode is started, and if not, the steps are repeatedly executed till the double mechanical arms complete path planning and reach the target point. According to the invention, the obstacle avoidance of static and dynamic obstacles in a working space can be realized, and meanwhile, the collision problem existing in the local obstacle avoidance process between the two arms is solved.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

The invention relates to the technical field of space positioning and path planning, in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. The method comprises the following steps of performing accurate longitude and latitude height data acquisition based on a laser range finder to generate accurate longitude and latitude height data; obtaining tower equipment parameters; performing inspection point three-dimensional coordinate calculation according to the tower equipment parameters and the tower three-dimensional coordinate system to generate inspection point three-dimensional coordinate data; cruise point comprehensive constraint range data are obtained according to the unmanned aerial vehicle flight association data and the polling area terrain data, and cruise point comprehensive constraint range data are generated; performing cruise node three-dimensional coordinate design on the three-dimensional coordinate data of the inspection point according to the cruise point comprehensive constraint range data to generate cruise point three-dimensional coordinate data; and performing optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data. According to the invention, more accurate unmanned aerial vehicle tower space positioning and path planning are realized.

The invention discloses an unmanned aerial vehicle inspection and power transmission operation control method and system based on path planning. The method comprises the following steps: constructing a three-dimensional environment model of a target area; constructing a total cost function at the cost of distance, weather risk, energy consumption and electromagnetic interference; taking the minimum total cost function as a target, and obtaining an inspection path of the unmanned aerial vehicle in the target area, so that the unmanned aerial vehicle flies based on the inspection path; in the flight process of the unmanned aerial vehicle, the inspection path is corrected in combination with the dynamic obstacle influence factors; the dynamic obstacle influence factor is determined by the distance between the unmanned aerial vehicle and the dynamic obstacle, the speed of the dynamic obstacle and the acceleration of the dynamic obstacle; in the flight process of the unmanned aerial vehicle, the airborne electricity testing device is controlled to measure the line voltage, and the airborne ground wire hanging and dismounting device is controlled to carry out ground wire hanging and dismounting operation according to the measured line voltage. According to the embodiment of the invention, dynamic obstacles encountered by the unmanned aerial vehicle in the flight process can be avoided, and collision is avoided.

The invention provides an Internet of Vehicles key information classification early warning method and a vehicle-mounted device. The Internet of Vehicles key information classification early warning method comprises the steps: acquiring Internet of Vehicles information of a target vehicle; determining current driving state information of the target vehicle according to the Internet of Vehicles information; and determining the type of information sent to the target vehicle according to the driving state information. According to the invention, the information with different emergency degrees can be sent to the user according to the current driving state information, multi-channel grading early warning is realized according to the information type, and the effects of assisting driving, promoting safety and improving user experience are achieved.

The invention relates to the technical field of obstacle avoidance early warning, in particular to an obstacle avoidance early warning method and system for a robot. The method comprises the following steps: acquiring motion data and moving target point data of a robot; robot motion mode classification is conducted on the robot motion data, and a robot motion mode and a robot static mode are generated; performing environment detection on the moving target point data by using a built-in laser radar of the robot according to the static mode to obtain standard static environment detection data; and planning the path according to the standard static environment detection data, and generating initial running path planning data of the robot. According to the invention, the obstacle avoidance early warning precision and real-time performance of the robot in a complex environment are improved by accurately distinguishing static and dynamic objects, reconstructing the motion trail and planning and adjusting the path.

The invention provides an unmanned aerial vehicle low-altitude obstacle avoidance system, and belongs to the technical field of control systems, and the unmanned aerial vehicle low-altitude obstacle avoidance system comprises a multi-sensor fusion module which comprises a plurality of sensors used for collecting environment information; the data processing module is used for analyzing and processing the data acquired by the multi-sensor fusion module in real time to generate an obstacle recognition result; the obstacle avoidance decision module is used for generating an optimal obstacle avoidance path based on the obstacle recognition result and the flight state of the unmanned aerial vehicle; and the control execution module is used for generating a control command based on the optimal obstacle avoidance path so as to control the flight of the unmanned aerial vehicle. According to the unmanned aerial vehicle low-altitude obstacle avoidance system provided by the invention, the unmanned aerial vehicle can be ensured to safely avoid obstacles in a low-altitude complex environment, and collision is avoided.

The invention belongs to the technical field of humanoid double-arm robots, and particularly relates to an intelligent grabbing method of a humanoid double-arm robot, which comprises the following steps: acquiring an RGB image and a depth image of a target object through a global camera, performing alignment processing, detecting the target object by using a coarse positioning model, and determining pre-grabbing positions of double arms; global point cloud data in a working space are obtained in real time through a global camera, and path planning is performed by adopting an improved artificial potential field algorithm, so that two arms respectively reach pre-grabbing positions; acquiring an RGB image and a depth image of a target object through a double-arm tail end camera, performing alignment processing, detecting the RGB image of a target camera by using a fine positioning model, and finding a feasible target grabbing attitude by using a pre-trained 6D grabbing attitude prediction model; and the two arms are controlled to execute the grabbing action according to the target grabbing posture and placed at the preset position. The method can adapt to various complex scenes, and diversified grabbing is achieved.

The invention discloses an unmanned aerial vehicle full-coverage path planning method, system and device and a storage medium, and relates to the field of unmanned aerial vehicles. The method comprises the following steps: acquiring preset map information of a first target area, and determining a second target area for delivering materials according to the preset map information; determining a relay unmanned aerial vehicle set according to the data information, a preset starting point, a second target area, preset map information, a first communication distance of the first unmanned aerial vehicle and a second communication distance of the relay unmanned aerial vehicle, and determining a second initial path of the second unmanned aerial vehicle according to the first initial path; controlling the first unmanned aerial vehicle and the second unmanned aerial vehicle to fly according to a preset initial path, and adjusting the preset initial path according to the environmental data collected by the first unmanned aerial vehicle; and when the first unmanned aerial vehicle arrives at a preset terminal point, determining flight paths of other unmanned aerial vehicles according to the actual load weights and types of the other unmanned aerial vehicles in the transportation unmanned aerial vehicle group and the environment data. By implementing the technical scheme provided by the invention, the task execution efficiency of the unmanned aerial vehicle group is improved.

The invention discloses a simulation-oriented multi-agent maneuvering behavior decision-making system and method, and the system comprises a message transmission module which introduces the signal intensity into the communication among multiple agents, simulates a complex simulation environment with interference in a probabilistic communication mode for the discrete quantification of the communication intensity among the agents, and transmits the signal intensity to a decision-making module; obtaining joint state information influencing a maneuvering behavior decision; the state coding module is used for discretely quantifying the obtained joint state information into a unified index and coding the unified index to obtain a joint state code corresponding to each agent; the reward optimization module is used for determining a target reward of each agent in combination with the task target and the historical trajectory of each agent, and the target reward is used for optimizing the deep reinforcement learning network model of the strategy execution module; and the strategy execution module is used for determining and executing a final decision result of each agent by adopting a deep reinforcement learning network model based on an MADDPG algorithm by each agent. According to the invention, the problem of multi-agent collaborative navigation in a complex high-dimensional environment is solved.

The invention belongs to the technical field of robot obstacle avoidance, and particularly relates to a double-arm collision detection and obstacle avoidance path planning method based on an improved artificial potential field method.The method comprises the steps that a three-dimensional model of double mechanical arms is obtained; the working space and environment obstacle information of the double mechanical arms is initialized; respectively calculating the repulsive force distance between each joint sphere and each connecting rod cylinder of the double mechanical arms and the obstacle; a mechanical arm performing path planning is defined as a main arm, the other mechanical arm is defined as a slave arm, positions of joints and connecting rod centers of the slave arm are calculated according to joint angles of the slave arm, a repulsive force field is set, meanwhile, the repulsive force field is set at a center point of an obstacle, and a gravitational field is set at a target point of the main arm; generating a track by using an improved artificial potential field method and updating the tail end position of the current main arm; and whether the tail end of the mechanical arm reaches the target point or not is judged, and if not, execution is repeated till the mechanical arm reaches the target point. According to the method, the calculation amount in a complex environment is reduced, and the planning efficiency and the safety of robot operation are improved.

The invention relates to a stirring friction extrusion solid phase additive deposition layer edge precision control device and method, and belongs to the technical field of additive manufacturing, the stirring friction extrusion solid phase additive deposition layer edge precision control device comprises a hollow stirring head, the hollow stirring head can rotate, and a through hole is formed in the central axis of the hollow stirring head and used for filling raw materials; the periphery of the hollow stirring head is sleeved with four split type sleeves, and the four split type sleeves can independently move up and down respectively. According to the method, side wall forming of the additive layer is limited, generation of burrs, the arc-shaped side wall and semi-cylindrical materials at the two ends of the additive layer is restrained, and therefore the follow-up cutting amount is reduced, and near-net forming of components is achieved; by limiting the plasticized material to flow to the periphery, the additive layers at the center and the edge are more compact, and the mechanical properties of the whole additive layers and the side wall part are improved; and multi-layer material adding in the vertical plane and multi-channel material adding in the horizontal plane can be achieved, accurate forming of the side wall can be achieved under the condition that the device is not replaced, and the flexibility of the device is improved.

The invention relates to an automatic driving YOLO target detection method and system for coping with light influence, and the method comprises the steps: employing a vehicle-mounted camera to shoot the actual condition images of streets and roads on a plurality of streets, obtaining the vehicle actual condition images of different streets and different roads in different time periods, and forming an image data set; marking the image data set by using a LabelImg tool, and marking a vehicle target in the image, including a bounding box and category information of a vehicle; constructing an automatic driving YOLO target detection network model coping with light influence; and training an automatic driving YOLO target detection network model coping with the light influence by using the obtained image data set, wherein the trained automatic driving YOLO target detection network model coping with the light influence is used for coping with vehicle target detection under the light influence. Accurate vehicle distance adjustment and safe driving decisions can be provided for the automatic driving vehicle, and driving safety and efficiency under various light conditions are ensured.

The invention discloses a food conveying device with an anti-falling mechanism for food processing, and relates to the technical field of food processing.The food conveying device with the anti-falling mechanism for food processing comprises a mounting frame, a conveying assembly is arranged on the mounting frame and comprises a driving motor and a conveying belt, and the conveying belt is rotationally mounted on the inner side of the mounting frame; the driving motor fixedly penetrates the side wall, away from the conveying belt, of the mounting frame. The output end of the driving motor is fixedly connected with a driving shaft of the conveying belt. According to the food conveying device with the anti-falling mechanism for food processing, multiple sets of partition plates arranged at equal intervals are arranged on the conveying belt, dough can be separated to be conveyed, material collision is prevented, when the dough is conveyed to the end of the conveying assembly, a push block rotates, the push block applies extrusion force to the dough, the dough is extruded and pushed, and the dough is conveyed to the end of the conveying assembly. The dough can be pushed away from the conveying belt, and the situation that the dough adheres to the conveying belt and is inconvenient to fall off is prevented.

The invention, which relates to the technical field of artificial intelligence, discloses a warehouse-in and warehouse-out path planning method and system for intelligent warehousing, and the method comprises the steps: generating an optimal operation path of a to-be-operated carrier through a path planning model; the method comprises the following steps: predicting whether a current carrier has a collision risk with all carriers in a warehouse in a time dimension or not through a time block, predicting whether the current carrier has a collision risk with all carriers in a warehouse in a space dimension or not through a space block, and when the time block and the space block simultaneously detect that the current carrier has the collision risk at a certain moment, judging whether the current carrier has the collision risk; the decision block judges that the current carrier has a collision conflict, and determines an optimal avoidance strategy through an action value function; according to the optimal avoidance strategy, the avoidance waiting carrier is determined, the total waiting time and the waiting space position of the avoidance waiting carrier are calculated, and the avoidance strategy is executed. According to the technical scheme, the problem that in-out garage path planning and collision processing cannot be intelligently and accurately carried out in the prior art can be solved.

The invention discloses an indoor unmanned aerial vehicle positioning navigation path planning method and system, and the method comprises the steps: installing a binocular camera device and an inertial measurement unit sensor for an unmanned aerial vehicle, and obtaining a binocular camera image and inertial measurement unit data for the unmanned aerial vehicle; performing feature extraction on the binocular camera image to obtain image feature points, and establishing a time sequence corresponding relation database of the image feature points based on the image feature points; estimating the pose of the unmanned aerial vehicle by using the time sequence corresponding relation database of the image feature points and the data of the inertial measurement unit; constructing a 3D space through the image feature points, and constructing a local environment map for the constructed 3D space by using the image feature points; the unmanned aerial vehicle performs path planning according to the local environment map to generate an unmanned aerial vehicle flight path; and optimizing the pose of the unmanned aerial vehicle based on the flight path of the unmanned aerial vehicle and a visual semi-direct method to obtain the final pose of the unmanned aerial vehicle. The optimal path is calculated, and the pose of the unmanned aerial vehicle is optimized, so that the estimation error of the position of the unmanned aerial vehicle is reduced.

The invention discloses a rapid splicing device for wall decoration plates, and particularly relates to the field of building construction.The rapid splicing device comprises a self-driven trolley, a distance adjusting mechanism is installed in the middle of the top end of the self-driven trolley, a supporting frame is supported at the top end of the distance adjusting mechanism, a lifting mechanism is installed on the supporting frame, and a fixing plate is installed on the lifting mechanism; the fixing plate is connected with a hydraulic telescopic rod, the hydraulic telescopic rod is connected with a transverse frame, a moving mechanism is installed on the transverse frame, a vacuum suction cup is installed on the moving mechanism, the vacuum suction cup is connected with a vacuum pump, the hydraulic telescopic rod is hinged to the top end of the fixing plate, and the other end of the hydraulic telescopic rod is movably connected with the transverse frame. A movable frame is welded to the bottom end of the fixed plate, a connecting plate is welded to the bottom end of one side of the transverse frame, and the connecting plate is hinged to the movable frame. The wall decoration plate splicing device has the advantages that wall decoration plates can be automatically spliced, the splicing efficiency is high, the automation degree is high, and labor is saved.

The invention discloses a copper pipe cutting machine, and relates to the technical field of cutting machines, the copper pipe cutting machine comprises a machine tool, the middle of the machine tool is fixedly connected with a fixing frame, the top of the fixing frame is fixedly connected with a motor, an output shaft of the motor is fixedly connected with a first gear, the interior of the fixing frame is rotatably connected with a clamp, and the outer surface of the clamp is fixedly connected with a first gear ring; a clamping jaw is slidably arranged on the right side of the clamp, and a strip-shaped shell is fixedly connected between the left side of the fixing frame and the machine tool. The horizontal concentric positions of the stabilizing rings and the clamp are kept, the horizontal concentric positions of the stabilizing wheels and the clamp are also kept, rotation of the copper pipe can be kept stable through rolling, abutting and supporting of the stabilizing wheels on the copper pipe, and therefore the situation that due to the fact that the copper pipe is inclined, a cutter cannot evenly cut the copper pipe is reduced. And the copper pipe can be prevented from being impacted by the copper pipe, so that the service life of the cutting knife is prolonged.

The invention discloses a mining robot path planning method based on artificial intelligence, relates to the technical field of artificial intelligence, and aims at solving the problem that the path of a mining robot can be planned by introducing an environment perception coefficient Rfx, an environment change coefficient Envc and a dynamic obstacle monitoring mechanism and combining real-time data processing of a dynamic obstacle total number Zzs, an obstacle distance Zjj, an obstacle relative speed Zsd and a robot steering radius Zbj. The problem that the traditional path planning cannot adapt to the sudden change of the mine environment is effectively solved; an obstacle avoidance path is generated by dynamically evaluating an environment change coefficient Envc, so that the safety and adaptability of path planning are improved; meanwhile, the emergency degree coefficient Urg of the assessment task is calculated, a path adjustment scheme is preferentially generated, and the emergency task response efficiency is improved; besides, whether a path optimization mode is started or not is judged through calculation and evaluation of a path energy consumption evaluation value Nex, it is guaranteed that tasks are completed smoothly, and the energy consumption risk is reduced; and the dynamic adaptability, the task allocation efficiency and the energy consumption management capability of the path planning of the mining robot are integrally improved.

The invention relates to a driving device and a blood pump. The driving device comprises a shell, a rotating assembly, a stator and a far-end bearing. The rotating assembly is rotatably mounted on the shell and comprises a rotating shaft and a rotor fixedly connected with the rotating shaft; the stator is fixedly connected with the shell; the far-end bearing comprises a far-end shaft sleeve, a far-end accessory and a far-end ball. The far-end shaft sleeve is fixed at the far end of the shell, and the rotating shaft rotatably penetrates through the far-end shaft sleeve; the far-end accessory is contained in the shell, and the far-end accessory is opposite to the far-end shaft sleeve in the axial direction and fixedly connected with the rotating assembly. The far-end ball is movably installed on one of the far-end shaft sleeve and the far-end accessory, the other one of the far-end shaft sleeve and the far-end accessory is provided with an abutting wall, and the abutting wall abuts against the spherical surface of the far-end ball in the axial direction.

The invention relates to an inspection robot global path planning method based on a three-dimensional grid map. The method comprises the following steps: constructing a three-dimensional point cloud map; constructing a three-dimensional occupation grid map; calculating the height difference between each grid and an adjacent grid of the three-dimensional occupied grid map, and marking the current grid and continuous grids in the vertical direction, namely the z direction, as grids with the same attribute; giving current position and target point position information of the robot, and converting the information into serial numbers to which grids belong; and traversing surrounding grids from a starting point by using an A * algorithm until a target point position is searched, and forming a global path from the starting point to the target point. According to the invention, a traditional two-dimensional grid map is replaced by a three-dimensional grid map, so that the obstacle avoidance capability of the ground mobile robot in a complex terrain is remarkably improved, the flexibility of the robot is improved, the safety of the robot in an actual application scene is enhanced, and especially in a dynamically changing environment, the obstacle avoidance capability of the robot is greatly improved. The path can be adjusted in time to avoid collision.

The invention relates to the technical field of electric wheelchair control, in particular to a multi-mode navigation control method and system of an electric wheelchair controller. The method comprises the following steps: acquiring a real-time environment monitoring video based on an electric wheelchair high-definition camera; video jitter distortion compensation and dynamic object frame-by-frame continuous optical flow tracking are carried out on the real-time environment monitoring video, and a moving track of each dynamic object is generated; performing environment static obstacle accurate identification on the real-time environment monitoring video, and performing dynamic environment perception modeling based on a moving track of each dynamic object so as to generate a dynamic environment map; a navigation mode switching decision is made based on a real-time wheelchair operation instruction of the user to obtain a current navigation mode of the user, and the current navigation mode of the user comprises a rocker control mode and an intelligent navigation mode. According to the invention, efficient and intelligent multi-mode control of the electric wheelchair is realized, so that the electric wheelchair can more flexibly and safely cope with a complex environment.

The invention discloses an intelligent safety control method for an exhibition, and the method comprises the steps: multi-sensor data fusion, real-time modeling and prediction, intelligent obstacle avoidance and planning, emergency braking and safety protection, cooperative control and communication, risk assessment and early warning, learning and self-adaption, and monitoring and manual intervention. The motion states of actors and stage equipment can be monitored and predicted in real time, and collision accidents are effectively avoided. And meanwhile, through the arrangement of an emergency braking system and protective equipment, the safety in the exhibition process is further improved. Besides, the control system has learning ability and adaptive ability, can automatically adjust safety control parameters according to different performance contents and stage arrangements, and improves the flexibility and practicability of the system.

The invention relates to a mechanical arm autonomous obstacle avoidance method and device based on deep reinforcement learning and a medium, and solves the obstacle avoidance problem of a mechanical arm in the process from an initial position to a target position through a reward function comprehensively designed in the deep reinforcement learning. Multiple aspects of obstacle avoidance of the tail end of the mechanical arm, obstacle avoidance of the mechanical arm body, self-collision avoidance, accurate reaching of a target position, smoothness of a movement track and the like are comprehensively considered. When an environment map is uncertain or obstacles dynamically change, the method has high adaptability. The working area of the mechanical arm is divided into the safety area, the warning area and the dangerous area, so that the mechanical arm can adopt a stricter obstacle avoidance strategy when the mechanical arm is in the dangerous area, and collision between the mechanical arm and the obstacle is avoided. Compared with the prior art, the method has the advantages of high success rate, wide application range, high reliability and the like.

The invention discloses a tower crane installation path planning method and system, a terminal and a medium, and the method comprises the following steps: collecting and preprocessing three-dimensional point cloud data and visual information in a tower crane installation area, fusing the point cloud data with image data, and generating a fused three-dimensional point cloud image which comprises distance and color information; a deep learning model is adopted to carry out obstacle detection on the fused three-dimensional point cloud image; an RRT algorithm is adopted, and a safety path is planned according to the obstacle detection result and the requirement of the hoisting task; and controlling the movement of the hoisting equipment according to the obtained safe path, and executing ascending, descending, horizontal moving and rotating actions of a lifting hook according to a path planning result. The method has the advantages that the safety and the efficiency in the hoisting operation of the power transmission tower are improved, and the quality and the reliability of power transmission line construction are improved.

The invention discloses a motor stator assembly tool and an assembly line, and relates to the technical field of motor assembly. A turntable; a plurality of placing grooves for placing the motor shell are formed in the turntable; the three-jaw chuck is connected to the rack in a sliding and rotating mode; the driving assembly comprises a rotary drum rotationally connected to the rack; the plurality of positioning mechanisms are arranged at the bottoms of the placing grooves and are used for positioning the motor shells in the placing grooves; through clockwise and anticlockwise rotation of the rotating drum, after a stator is inserted into a motor shell in the clockwise direction, the rotating disc is driven to rotate, the motor shell to be assembled downwards is rotated to an assembling station, when the rotating drum rotates anticlockwise, the three-jaw chuck can vertically move upwards and rotate, and the stator is clamped, grabbed and reset in the rotating process; preparation is made for next-time assembly, and the assembly efficiency is greatly improved; through the positioning of the positioning device, the collision between the stator and the motor shell is avoided, and the assembly effect is ensured.

The invention provides an unmanned aerial vehicle path planning method based on a river horse state optimization particle swarm algorithm, and the method comprises the steps: obtaining the coordinates of a static obstacle, and carrying out the environment modeling through a grid method; initializing a particle swarm, and establishing a global path optimization function by taking the shortest driving path and no collision with an obstacle as a target; and optimizing the particle swarm optimization algorithm through a Heman state mechanism to obtain an improved particle swarm optimization algorithm, and solving the global path optimization function through the improved particle swarm optimization algorithm to obtain a global planning path. According to the unmanned aerial vehicle path planning method based on the river horse state optimization particle swarm algorithm, a traditional particle swarm algorithm is optimized by combining a river horse state mechanism, adaptive adjustment of algorithm parameters is achieved, the convergence speed and global search capability of path planning are improved, and the precision and efficiency of path planning are improved.