CN110673627A - Forest unmanned aerial vehicle searching method - Google Patents
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Abstract
The invention discloses a forest unmanned aerial vehicle searching method, wherein an unmanned aerial vehicle obtains the primary pose of the unmanned aerial vehicle by using an extended Kalman filtering algorithm according to IMU data and optical flow data; correcting the initial pose of the unmanned aerial vehicle by adopting a self-adaptive Monte Carlo positioning method; the method comprises the steps of measuring distances between obstacles in different directions and the unmanned aerial vehicle by using a laser sensor and an auxiliary positioning sensor, correcting data through comparison, building a map by using effective data, determining a passable position according to the size of the obstacle, and then determining a passable communication area and planning a path. The invention adopts a plurality of sensors, realizes high-precision positioning, gives full play to the advantages of different sensors, and complements the advantages and disadvantages of different sensors. The posture is corrected by adopting an algorithm combining various positioning algorithms, so that the complex environments with a large number of obstacles such as forests and the like can be fully scanned, the blind areas of the sensors are reduced, and the interference is overcome.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a forest unmanned aerial vehicle searching method.
Background
The quad-rotor unmanned aerial vehicle has the advantages of small size, simple structure, flexible action, low manufacturing cost and the like, and is more and more widely applied to the fields of aerial photography, post-disaster rescue, agriculture and forestry planting, military reconnaissance and the like. China is a big forestry country, and there are demands for close-range reconnaissance in forest woodland, wild animal's pursuit, search and rescue of personnel of the accident of open-air forest, etc., and people move inconveniently in the dense forest, therefore need unmanned aerial vehicle to assist, and general unmanned aerial vehicle application is all realized in spacious accessible place, and under the circumstances such as meeting proruption threat, meet the goal with chance, can't carry out course adjustment in real time, especially can't realize autonomous navigation flight in the mixed and disorderly building and under the forest environment that the obstacle spreads over, and then can't realize the flight task of high grade. Therefore, unmanned aerial vehicle autonomous flight among forests with complex multiple obstacles faces huge challenges.
The unmanned aerial vehicle autonomously flies in the forest, the positioning problem needs to be solved, the GPS positioning information is not applicable to the complex forest, the unmanned aerial vehicle autonomously detects the surrounding environment and constructs a map, and then the constructed map is used for path planning.
The defects of the prior art mainly comprise: the method has the advantages of long processing time, low positioning precision, blind area in detection, large interference caused by environmental influence, suitability for indoor static environment, inaccurate judgment on outdoor environment, and especially movable object interference. The positioning and obstacle avoidance technology based on the binocular camera needs to acquire images of the environment in real time, and a disparity map is generated and preprocessed based on the acquired left image and right image in a matching calculation mode; based on the disparity map information, carrying out clustering division on the contour and the gray value of the disparity map to obtain an area block with a clear structure, and removing noise interference of the area block to obtain a potential obstacle area. Such processing needs a large amount of calculation, and the processing time is long, can't satisfy unmanned aerial vehicle real-time's requirement. Some obstacle detection methods based on ultrasonic wave, sonar and other sensors, which can detect the environment around the intelligent body, however, in a complex environment, a plurality of ultrasonic wave information are interlaced together, which is easy to generate confusion. Moreover, any sensing device is not perfect, and there will always be a blind spot or interference from external noise, which results in a decrease in accuracy.
Disclosure of Invention
The invention aims to provide a forest unmanned aerial vehicle searching method which is high in processing speed and positioning accuracy, enables an unmanned aerial vehicle to quickly search a path in a forest environment and can be better adapted to the complex environment in forest.
In order to realize the task, the invention adopts the following technical scheme:
a forest unmanned aerial vehicle searching method is characterized in that a laser sensor, an auxiliary positioning sensor, an optical flow sensor and an inertia measuring unit are mounted on an unmanned aerial vehicle; when the drone is flying in a forest environment:
obtaining IMU data through an inertial measurement unit, obtaining optical flow data through an optical flow sensor, and obtaining a primary pose of the unmanned aerial vehicle through an extended Kalman filtering algorithm according to the IMU data and the optical flow data;
correcting the initial poses of the unmanned aerial vehicle at two adjacent time points by adopting a self-adaptive Monte Carlo positioning method to obtain the corrected poses as the true poses of the unmanned aerial vehicle;
measuring distances between obstacles in different directions and the unmanned aerial vehicle by using a plurality of laser sensors distributed in different directions around the unmanned aerial vehicle;
measuring the distance of obstacles around the unmanned aerial vehicle and the size of the obstacles by using an auxiliary positioning sensor; if the difference value between the distance between the obstacle and the unmanned aerial vehicle measured by the auxiliary positioning sensor and the distance between the obstacle and the unmanned aerial vehicle measured by the laser sensor in the same direction is larger than a set first threshold value, determining the distance as invalid data, and measuring the distance between the obstacles by reusing the laser sensor and the auxiliary positioning sensor; if the difference is smaller than a set first threshold value, the data is available data, and at the moment, the distance between the obstacles in different directions and the unmanned aerial vehicle, which is measured by the laser sensor, is used for constructing a map;
comparing the distance between every two adjacent obstacles in the constructed map by using the size of the obstacle measured by the auxiliary positioning sensor, and marking a passable position between the two obstacles on the map if the distance between the two adjacent obstacles is greater than the maximum outer diameter of the unmanned aerial vehicle and exceeds a set second threshold value than the maximum outer diameter;
changing the pose of the unmanned aerial vehicle, and acquiring different passable positions in a map; if one or more passable positions can form a communication area for communicating the current position of the unmanned aerial vehicle with the set target position, the unmanned aerial vehicle path planning is carried out according to the communication area, and the unmanned aerial vehicle reaches the target position according to the planned path.
Further, the auxiliary positioning sensor comprises an ultrasonic sensor or an infrared sensor.
Further, the diameter of the communication area is larger than the maximum outer diameter of the unmanned aerial vehicle and exceeds a second threshold value.
The invention has the following technical characteristics:
1. compared with the prior art, the invention adopts various sensors, realizes high-precision positioning, gives full play to the advantages of different sensors, and complements the advantages and disadvantages of different sensors. The posture is corrected by adopting an algorithm combining various positioning algorithms, so that the complex environments with a large number of obstacles such as forests and the like can be fully scanned, the blind areas of the sensors are reduced, and the interference is overcome.
2. The invention realizes high-precision positioning, improves the anti-interference capability to environmental changes, adopts a self-adaptive Monte Carlo positioning algorithm, introduces a particle number self-adaptive mechanism, and improves the pose estimation efficiency, thereby improving the calculation efficiency. And a multi-sensor redundant barrier judgment algorithm is adopted, so that the safety performance of the unmanned aerial vehicle is fully ensured.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a laser sensor for obstacle distance measurement;
FIG. 3 is a schematic illustration of Gaussian distribution sampling;
FIG. 4 is a schematic flow chart of an adaptive Monte Carlo localization method;
fig. 5 is a schematic view of a passable position determination process.
Detailed Description
The invention discloses a forest unmanned aerial vehicle searching method, which is characterized in that besides an onboard computer, a wireless transceiver module, a camera, an inertia measurement unit and a power system which are originally carried by an unmanned aerial vehicle, the unmanned aerial vehicle is also provided with a laser sensor, an auxiliary positioning sensor and an optical flow sensor; wherein:
the airborne computer is used as a control center and is used for receiving data acquired by various sensors to carry out unmanned aerial vehicle path planning, image building, flight control and the like, and correspondingly controlling the unmanned aerial vehicle according to a ground control instruction received by the wireless transceiver module; the wireless receiving and transmitting module is used for sending the current flight information of the unmanned aerial vehicle to the ground controller and receiving a control instruction sent by the ground controller; the camera is used for collecting images and sending the images to the ground controller through the wireless transceiving module; the inertial measurement unit is used for gathering IMU data, and driving system is used for driving unmanned aerial vehicle.
The primary task of unmanned aerial vehicle flying among forests is to scan the forest environment, construct an electronic map, identify the position and size of obstacles, mainly use trunks and leaves as the obstacles among the forests, perform path specification, and then perform corresponding tasks when arriving at a target point. The method comprises the following specific steps:
step 1, IMU data are obtained through an inertial measurement unit IMU, but due to the fact that a barometer of the IMU is unstable, and the height of the unmanned aerial vehicle relative to the ground needs a more accurate optical flow sensor to conduct height fixing processing, optical flow data are obtained through the optical flow sensor, and an airborne computer obtains the primary pose of the unmanned aerial vehicle through an extended Kalman filtering algorithm according to the IMU data and the optical flow data.
And 2, correcting the initial poses of the unmanned aerial vehicle at two adjacent time points by the airborne computer by adopting a self-adaptive Monte Carlo positioning method to obtain the corrected poses as the true poses of the unmanned aerial vehicle. The two adjacent time points refer to a current time point and a previous time point; the corrected pose is the real pose of the unmanned aerial vehicle at the current moment. The pose of the unmanned aerial vehicle is the basis of the positioning, path planning, drawing and other processes of the unmanned aerial vehicle, so that the accurate pose has important significance for the processes of the unmanned aerial vehicle.
In the adaptive monte carlo positioning method, M particles are sampled by using gaussian distribution to represent the position of the unmanned aerial vehicle. As shown in fig. 3, where the dots represent the particles employed and the curves represent the gaussian distribution. The positioning process comprises four steps, as shown in fig. 4.
And 3, measuring the distances between the obstacles in different directions and the unmanned aerial vehicle by the airborne computer by using a plurality of laser sensors distributed in different directions around the unmanned aerial vehicle. Laser sensor can measure the nearest barrier in different directions, also be the distance between branch, leaf etc. and the unmanned aerial vehicle. At each time point, the onboard computer acquires a measurement. As shown in fig. 2, where the triangle represents the drone, the straight line is the laser beam, and the square is the nearest obstacle detected in the laser direction. And after the distance value detected by the laser sensor is judged by the data validity in the follow-up process, the effective data is utilized to construct a map.
Step 4, measuring the distance of the obstacles around the unmanned aerial vehicle and the size of the obstacles by using auxiliary positioning sensors, such as an ultrasonic sensor and an infrared sensor; and the on-board computer judges:
if the difference value between the distance between the obstacle and the unmanned aerial vehicle measured by the auxiliary positioning sensor and the distance between the obstacle and the unmanned aerial vehicle measured by the laser sensor in the same direction is larger than a set first threshold value, for example, the first threshold value is 5cm, considering that the two distances are invalid data, and reusing the laser sensor and the auxiliary positioning sensor to measure the distance between the obstacles; and if the difference is smaller than the set first threshold value, the data is available data, and at the moment, the distance between the obstacles in different directions and the unmanned aerial vehicle, which is measured by the laser sensor, is used for constructing a map. Therefore, the auxiliary positioning sensor is used for correcting the distance of the obstacle, so that the image building is more accurate.
And 5, comparing the distance between every two adjacent obstacles in the constructed map by using the size of the obstacle measured by the auxiliary positioning sensor through the onboard computer, and marking a passable position between the two obstacles on the map if the distance between the two adjacent obstacles is greater than the maximum outer diameter of the unmanned aerial vehicle and exceeds the maximum outer diameter by a set second threshold value, for example, the second threshold value is set to be not less than 20 cm. The distance between two obstacles is larger than the maximum outer diameter of the unmanned aerial vehicle, and a certain margin (a second threshold value) is reserved, so that the passing position can meet the passing requirement under the influence condition of external factors such as wind power and the like. The position where the distance between two obstacles is smaller than the maximum outer diameter of the unmanned aerial vehicle is marked as a non-accessible position.
Step 6, changing the pose of the unmanned aerial vehicle, and acquiring different passable positions in the map according to the same method in the step 5; if one or more passable positions exist, a communication area for communicating the current position of the unmanned aerial vehicle with the set target position can be formed, the diameter of the communication area is larger than the maximum outer diameter of the unmanned aerial vehicle and exceeds a second threshold value, unmanned aerial vehicle path planning is carried out according to the communication area, and the unmanned aerial vehicle reaches the target position according to the planned path.
Claims (3)
1. A forest unmanned aerial vehicle searching method is characterized in that a laser sensor, an auxiliary positioning sensor, an optical flow sensor and an inertia measuring unit are mounted on an unmanned aerial vehicle; when the drone is flying in a forest environment:
obtaining IMU data through an inertial measurement unit, obtaining optical flow data through an optical flow sensor, and obtaining a primary pose of the unmanned aerial vehicle through an extended Kalman filtering algorithm according to the IMU data and the optical flow data;
correcting the initial poses of the unmanned aerial vehicle at two adjacent time points by adopting a self-adaptive Monte Carlo positioning method to obtain the corrected poses as the true poses of the unmanned aerial vehicle;
measuring distances between obstacles in different directions and the unmanned aerial vehicle by using a plurality of laser sensors distributed in different directions around the unmanned aerial vehicle;
measuring the distance of obstacles around the unmanned aerial vehicle and the size of the obstacles by using an auxiliary positioning sensor; if the difference value between the distance between the obstacle and the unmanned aerial vehicle measured by the auxiliary positioning sensor and the distance between the obstacle and the unmanned aerial vehicle measured by the laser sensor in the same direction is larger than a set first threshold value, determining the distance as invalid data, and measuring the distance between the obstacles by reusing the laser sensor and the auxiliary positioning sensor; if the difference is smaller than a set first threshold value, the data is available data, and at the moment, the distance between the obstacles in different directions and the unmanned aerial vehicle, which is measured by the laser sensor, is used for constructing a map;
comparing the distance between every two adjacent obstacles in the constructed map by using the size of the obstacle measured by the auxiliary positioning sensor, and marking a passable position between the two obstacles on the map if the distance between the two adjacent obstacles is greater than the maximum outer diameter of the unmanned aerial vehicle and exceeds a set second threshold value than the maximum outer diameter;
changing the pose of the unmanned aerial vehicle, and acquiring different passable positions in a map; if one or more passable positions can form a communication area for communicating the current position of the unmanned aerial vehicle with the set target position, the unmanned aerial vehicle path planning is carried out according to the communication area, and the unmanned aerial vehicle reaches the target position according to the planned path.
2. A forest drone search method as claimed in claim 1, characterised in that the auxiliary location sensor comprises an ultrasonic sensor or an infrared sensor.
3. A forest drone search method as claimed in claim 1, characterised in that the diameters of the connected areas are all greater than the maximum outer diameter of the drone and exceed a second threshold.
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| CN113325867A (en) * | 2021-05-21 | 2021-08-31 | 华中科技大学 | Path planning method and device for searching of unmanned aircraft and unmanned aircraft |
| CN113485392A (en) * | 2021-06-17 | 2021-10-08 | 广东工业大学 | Virtual reality interaction method based on digital twins |
| CN117631686A (en) * | 2023-12-07 | 2024-03-01 | 浙江大学 | Path optimization method and track tracking control method for multi-rotor unmanned aerial vehicle |
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