CN110609570A - Autonomous obstacle avoidance inspection method based on unmanned aerial vehicle - Google Patents
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Abstract
The invention relates to an autonomous obstacle avoidance inspection method based on an unmanned aerial vehicle, which adopts a multi-sensor data fusion technology and an intelligent track online planning technology to perform data fusion on a plurality of devices such as a visual sensor, a laser radar and the like, so as to realize omnibearing and multithread obstacle position detection, and utilizes the intelligent track online planning technology to perform online adjustment or re-planning on an injected track task so as to avoid and bypass obstacles, thereby effectively improving the precision and reliability of the autonomous flight function of the unmanned aerial vehicle, ensuring the flight operation safety of the unmanned aerial vehicle, improving the inspection safety of the unmanned aerial vehicle, further causing the autonomous obstacle avoidance function of the unmanned aerial vehicle to be disordered or lost in the inspection process due to the interference of a strong electromagnetic field, and further reducing the economic loss.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicles, in particular to an autonomous obstacle avoidance inspection method based on an unmanned aerial vehicle.
Background
The unmanned aerial vehicle inspection uses the unmanned aerial vehicle as a carrier, inspection instruments (such as a visible light camera, an infrared imager, a damage detection instrument, a scanning cloud platform and the like) are used as a load system, and ground remote control or an unmanned aerial vehicle autonomous working mode is adopted for inspection according to a planned route. The unmanned aerial vehicle inspection is used in the modern day, and the unmanned aerial vehicle intelligent inspection is the main research direction at present. In the aspect of intelligent inspection of the unmanned aerial vehicle, the unmanned aerial vehicle autonomously avoids obstacles, and the design for realizing intelligent inspection of the unmanned aerial vehicle is the most basic design. The perfect autonomous obstacle avoidance system can reduce the damage or crash of the unmanned aerial vehicle caused by misoperation of the unmanned aerial vehicle, and save a series of losses caused by the misoperation of the unmanned aerial vehicle.
The unmanned aerial vehicle autonomous obstacle avoidance technology mainly utilizes sensors such as sound and wave to acquire obstacle information, and currently, a common unmanned aerial vehicle obstacle avoidance system mainly has three modes, namely ultrasonic and radar ranging, and a relatively more complex obstacle avoidance technology based on visual image processing.
The existing unmanned aerial vehicle inspection technology can generally position the position of an unmanned aerial vehicle according to a plurality of satellites, because the unmanned aerial vehicle sends positioning information to the satellites, the satellites feed back the position information of the unmanned aerial vehicle to the unmanned aerial vehicle, and the unmanned aerial vehicle determines the position of the unmanned aerial vehicle through the difference of the satellite positioning information of different positions. The existing unmanned aerial vehicle autonomous obstacle avoidance only needs to embody the flight path planning, and then sets the optimal flight path at a ground station before flight through a GPS/INS combined navigation system according to a preset digital map.
Unmanned aerial vehicle route planning is a key in unmanned aerial vehicle mission planning technology. Unmanned aerial vehicle patrols and examines and uses unmanned aerial vehicle as the carrier to patrol and examine the instrument (like visible light camera, infrared imager, damage detection instrument and scanning cloud platform etc.) and regard as the load system, adopt the working method of ground remote control or unmanned aerial vehicle autonomous working, accomplish patrolling and examining of transmission line, though unmanned aerial vehicle patrols and examines and demonstrate huge advantage and potential application potentiality, but because the transmission line environment is complicated, obstacle species and quantity are more, so unmanned aerial vehicle patrols and examines and urgently need solve autonomic obstacle avoidance problem.
The autonomous obstacle avoidance by adopting the technology mainly has the following defects: 1) the anti-electromagnetic interference capability is poor; 2) low intelligent degree and poor route timeliness: the unmanned aerial vehicle plans a route for the unmanned aerial vehicle according to an existing map in the positioning system, the navigation condition of the unmanned aerial vehicle cannot be planned according to an updated route, the unmanned aerial vehicle only knows the position information of the unmanned aerial vehicle, the position information of the unmanned aerial vehicle cannot be combined with the actual environment information, the real flying environment is simulated, and the intelligent degree is low; 3) large error, inaccurate positioning: the unmanned aerial vehicle is also flying when sending positioning information to the satellite, resulting in inaccurate positioning information, and the information sent to the satellite is influenced by the surrounding environment, and the time delay is large.
Disclosure of Invention
The invention is developed mainly to improve the autonomous obstacle avoidance capability of the unmanned aerial vehicle in the inspection process, and based on the multi-sensor data fusion technology and the intelligent track online planning technology, the data fusion is carried out by adopting various devices such as a visual sensor, a laser radar and the like, the omnibearing and multi-thread obstacle position detection is realized, the injected track task is adjusted online by applying the intelligent track online planning technology or the obstacle avoidance detour is realized in the planning, the precision and the reliability of the autonomous flight function of the unmanned aerial vehicle are effectively improved, the safety of the unmanned aerial vehicle flight operation is ensured, and the inspection safety of the unmanned aerial vehicle is improved.
In order to achieve the above purpose, the technical scheme of the invention comprises: an unmanned aerial vehicle autonomous obstacle avoidance inspection method and system are provided, and the method comprises the following steps:
step a, determining a flight task, planning a route according to a preset obstacle avoidance path, and executing the flight task;
b, detecting whether an obstacle exists on a forward planned route in real time in the flight process; if not, continuing the flight, if so, confirming the distribution of the obstacles;
step c, searching an obstacle avoidance path according to the obstacle distribution, and adjusting path planning in real time;
and in the step b, detecting the obstacles by adopting a data fusion algorithm of multiple sensors, wherein the sensors comprise a laser radar sensor and a vision sensor, and the size and the distribution of the obstacles are obtained by signals detected by the laser radar sensor and the vision sensor through the data fusion algorithm.
Further, the data fusion algorithm adopts a complementary fusion algorithm.
Further, the visual perception information extraction and analysis comprises camera parameter calibration and environment modeling; the camera parameter calibration adopts a Zhangyingyou camera calibration method to extract the angular points corresponding to the calibration plate, and carries out camera internal parameter calibration according to the position relationship between the extracted angular points; when calibrating the external parameters of the camera, acquiring the external parameters by adopting a Zhangyingyou camera calibration method, calculating the position of each camera relative to the same calibration plate, and obtaining the position relation between the binocular cameras through coordinate transformation so as to realize the calibration of the external parameters of the camera; the environment modeling comprises the following steps: firstly, preprocessing an acquired image, mainly comprising smoothing processing by a median filtering method and edge extraction by an LOG edge detection operator; secondly, utilizing constraint conditions such as epipolar geometry, image edges and the like, and adopting a method based on normalized gray scale to realize stereo matching of image feature points; then, calculating the space three-dimensional coordinates of the space points in the coordinate system of the unmanned aerial vehicle by using the projection relation of the camera and a corresponding formula, thereby completing the three-dimensional reconstruction of the environment; and finally, realizing the reconstruction of the environment two-dimensional map by adopting a method of rasterizing the obstacles.
Further, the laser radar detection employs a time-of-flight ranging method, which obtains a target object distance by detecting the time of flight (round trip) of a light pulse by continuously transmitting the light pulse to a target and then receiving the light returning from the object with a sensor.
Further, in the step c, after the unmanned aerial vehicle is confirmed to encounter obstacles, the path is searched according to the optimal path and/or the local path according to the obstacle distribution, and the path planning is adjusted in real time.
Further, the optimal path search adopts an a-algorithm, and an estimation function of the a-algorithm is as follows: (n) ═ g (n) + h (n), where: g (n) represents the actual distance from the starting point to any vertex n, and h (n) represents the estimated distance from any vertex n to the target vertex.
Furthermore, the local path planning adopts potential force field path planning, the potential force field comprises attraction potential generated by a target and repulsion potential generated by a barrier, the attraction potential and the repulsion potential act together to guide the flight direction of the unmanned aerial vehicle, and the flight track generated by the unmanned aerial vehicle under the guidance of the potential field force is the planned path.
The potential force field is calculated by the following formula:
a gravitational field:
xi here is the scale factor, ρ (q, q)goal) Indicating the distance of the current state of the object from the target.
Attraction force:
a repulsive force field:
eta in the formula is the repulsive scale factor, rho (q, q)obs) Representing the distance between the object and the obstacle; rho0Represents the radius of influence of each obstacle;
repulsion force:
the total field is the superposition of the gravitational field of the repulsive field:
U(q)＝Uatt(q)+Urep(q)
the total force is also a superposition of the corresponding force components:
has the advantages that: the invention adopts a multi-sensor-based data fusion technology and an intelligent track on-line planning technology to perform data fusion on data acquired by various devices such as a vision sensor, a laser radar and the like, thereby realizing omnibearing and multi-thread obstacle position detection; determining obstacle distribution according to the obstacle detection result; according to the distribution of the obstacles, the injected flight path task is adjusted on line or the obstacle is avoided and bypassed in planning by using the intelligent flight path online planning technology, so that the precision and the reliability of the autonomous flight function of the unmanned aerial vehicle are effectively improved, the safety of the flight operation of the unmanned aerial vehicle is guaranteed, and the inspection safety of the unmanned aerial vehicle is improved.
Drawings
FIG. 1 is a flow chart of the visual perception information extraction and analysis of the present invention;
FIG. 2 is a flow chart of laser radar obstacle information extraction according to the present invention;
FIG. 3 is a flow chart of data fusion in accordance with the present invention;
fig. 4 is a flow chart of obstacle avoidance path planning of the present invention;
fig. 5 is a flow chart of the autonomous obstacle avoidance inspection method of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The invention is developed mainly to improve the autonomous obstacle avoidance capability of the unmanned aerial vehicle in the inspection process, and based on the multi-sensor data fusion technology and the intelligent track online planning technology, the data fusion is carried out by adopting various devices such as a visual sensor, a laser radar and the like, the omnibearing and multi-thread obstacle position detection is realized, the injected track task is adjusted online by applying the intelligent track online planning technology or the obstacle avoidance detour is realized in the planning, the precision and the reliability of the autonomous flight function of the unmanned aerial vehicle are effectively improved, the safety of the unmanned aerial vehicle flight operation is ensured, and the inspection safety of the unmanned aerial vehicle is improved.
Firstly, the unmanned aerial vehicle plans a flight path according to a preset obstacle avoidance path to execute a flight task, when an obstacle is encountered, the flight information of the unmanned aerial vehicle is acquired through a camera module and a laser radar module of a multi-sensor, then the data of the camera module and the laser radar module are effectively combined by using a complementary fusion algorithm module, and the acquired image is preprocessed through an environment modeling module, so that the flight information of the unmanned aerial vehicle is extracted and analyzed, and the full-angle and high-precision exploration of the obstacle is realized.
The data fusion technology based on the multiple sensors mainly solves the technical difficulty of obstacle position detection, and because the vision sensor has the advantage of wide view field, the vision sensor can provide forward indexes for path online planning, but cannot identify smaller obstacles; the laser radar has narrow detection range and high precision, and is favorable for identifying smaller obstacles; the method of adopting vision sensor and laser radar, etc. respectively makes the technical route as follows:
visual perception information extraction and analysis
As shown in fig. 1, a flow chart of visual perception information extraction and analysis is shown, which mainly includes two parts of camera parameter calibration and environment modeling:
calibrating parameters of a camera: when calibrating the internal parameters of the camera, acquiring images distributed in all ranges in the visual field as far as possible to ensure the calibration precision, extracting the angular points corresponding to the calibration plate by adopting a Zhang Yongyou camera calibration method, and calibrating the internal parameters of the camera according to the position relationship between the extracted angular points; when calibrating the external parameters of the camera, acquiring the external parameters by adopting a Zhangyingyou camera calibration method, calculating the position of each camera relative to the same calibration plate, and obtaining the position relation between the binocular cameras through coordinate transformation so as to realize the calibration of the external parameters of the camera;
environment modeling: firstly, preprocessing an acquired image, mainly comprising smoothing processing by a median filtering method and edge extraction by an LOG edge detection operator; secondly, utilizing constraint conditions such as epipolar geometry, image edges and the like, and adopting a method based on normalized gray scale to realize stereo matching of image feature points; then, calculating the space three-dimensional coordinates of the space points in the coordinate system of the unmanned aerial vehicle by using the projection relation of the camera and a corresponding formula, thereby completing the three-dimensional reconstruction of the environment; and finally, realizing the reconstruction of the environment two-dimensional map by adopting a method of rasterizing the obstacles.
Lidar obstacle information extraction
The working flow of the laser radar obstacle information extraction is shown in fig. 2. The lidar is mounted above an aircraft platform and adopts a TOF (Time of flight) ranging method, which is based on the principle that a target object distance is obtained by continuously transmitting light pulses to a target, then receiving light returning from an object with a sensor, and detecting the Time of flight (round trip) of the light pulses. The laser radar is connected with the servo mechanism to realize 360-degree all-directional scanning.
Data fusion technique
The data fusion technology can realize the advantages and the disadvantages of the sensors with different advantages. The vision sensor has the advantage of wide field of view, can provide forward indexes for path on-line planning, but cannot identify smaller obstacles; the laser radar has a narrow detection range and high precision, is beneficial to identifying small obstacles, and can effectively combine the data of the two obstacles by using a complementary fusion algorithm to play the advantages of the two obstacles, thereby realizing the full-angle and high-precision exploration of the obstacles.
The data fusion process of the present invention is shown in fig. 3, and comprises the following steps:
(1) starting to execute a flight task;
(2) determining a flight route;
(3) high measurement accuracy detection is carried out on process detection (20 meters) through a laser radar sensor, and three-dimensional detection is carried out on remote detection through visual identification;
(4) performing fusion analysis on data obtained by the laser radar sensor and the visual identification through a data fusion technology;
(5) and judging whether the unmanned aerial vehicle encounters an obstacle, if so, confirming obstacle distribution, and if not, continuing flying.
Obstacle avoidance path planning
After confirming that the unmanned aerial vehicle encounters an obstacle, adjusting the path plan in real time according to the obstacle distribution confirmed by the data fusion algorithm, wherein the obstacle avoidance path plan disclosed by the invention comprises the steps of optimal path search and local path search, and is shown in figure 4.
Searching an optimal path: the optimal path is obtained through heuristic search, in the heuristic search process based on an A-algorithm, an evaluation function shape is selected, such as f (n) ═ g (n) + h (n), and the optimal path node sequence is obtained through evaluation under different conditions. The algorithm is commonly called the A star algorithm. This is an algorithm for finding the lowest passing cost of a path having a plurality of nodes on a graphics plane. Mobile computing of NPC, which is commonly used in games, or mobile computing of BOT, which is a network game. The algorithm integrates the advantages of the best priority search and Dijkstra algorithm: heuristic search is performed to improve the efficiency of the algorithm, and an optimal path (based on an evaluation function) can be guaranteed to be found. In this algorithm, if g (n) represents the actual distance from the starting point to any vertex n, and h (n) represents the estimated distance from any vertex n to the target vertex (which varies according to the evaluation function used), the estimation function of the a-algorithm is: (n) g (n) + h (n). This formula follows the following characteristics:
(1) if g (n) is 0, only an evaluation function h (n) from any vertex n to a target is calculated, and the distance from a starting point to the vertex n is not calculated, the algorithm is converted into the best preferential search using a greedy strategy, the speed is fastest, but an optimal solution cannot be obtained possibly;
(2) if h (n) is not more than the actual distance from the vertex n to the target vertex, the optimal solution can be obtained certainly, and the smaller h (n), the more nodes needing to be calculated, the lower the algorithm efficiency, and common evaluation functions such as Euclidean distance, Manhattan distance and Chebyshev distance;
(3) if h (n) is 0, only the shortest path g (n) from the starting point to any vertex n needs to be calculated, and no evaluation function h (n) is calculated, the method is converted into a single-source shortest path problem, namely Dijkstra algorithm, and the most vertices need to be calculated at the moment.
Local path planning: the space structure is described by introducing a numerical function which becomes an artificial potential field, and the unmanned aerial vehicle is guided to avoid obstacles to reach a target place through the force in the potential field. The artificial potential field is divided into attraction potential generated by the target and repulsion potential generated by the obstacle, the attraction potential and the repulsion potential act together to guide the flight direction of the unmanned aerial vehicle, and the flight track generated by the unmanned aerial vehicle under the guidance of the potential field force is the planned path.
A gravitational field:
xi here is the scale factor, ρ (q, q)goal) Indicating the distance of the current state of the object from the target.
Attraction force:
a repulsive force field:
eta in the formula is the repulsive scale factor, rho (q, q)obs) Representing the distance between the object and the obstacle. Rho0Representing the radius of influence of each obstacle. In other words, at a certain distance, the obstacle has no repulsive influence on the object.
Repulsion force:
the total field is the superposition of the repulsive and gravitational fields:
U(q)＝Uatt(q)+Urep(q)
the total force is also a superposition of the corresponding force components:
the invention adopts a multi-sensor data fusion technology and an intelligent track online planning technology to perform data fusion on a plurality of devices such as a visual sensor, a laser radar and the like, realizes omnibearing and multi-thread obstacle position detection, and utilizes the intelligent track online planning technology to perform online adjustment and replanning on an injected track task to realize obstacle avoidance and detour, thereby effectively improving the precision and reliability of the autonomous flight function of the unmanned aerial vehicle, ensuring the safety of the flight operation of the unmanned aerial vehicle, improving the safety of the unmanned aerial vehicle in inspection, further avoiding the autonomous obstacle avoidance function disorder or loss of the unmanned aerial vehicle in the inspection process due to the interference of a strong electromagnetic field, and further reducing the economic loss.
The above description is only a preferred embodiment of the present invention, the present invention is not limited to the above embodiment, and there may be some slight structural changes in the implementation, and if there are various changes or modifications to the present invention without departing from the spirit and scope of the present invention, and within the claims and equivalent technical scope of the present invention, the present invention is also intended to include those changes and modifications.
Claims (7)
1. An unmanned aerial vehicle autonomous obstacle avoidance inspection method comprises the following steps:
step a, determining a flight task, planning a route according to a preset obstacle avoidance path, and executing the flight task;
b, detecting whether an obstacle exists on a forward planned route in real time in the flight process; if not, continuing the flight, if so, confirming the size and distribution of the obstacles;
step c, searching an obstacle avoidance path according to the obstacle distribution, and adjusting path planning in real time;
and in the step b, detecting the obstacles by adopting a data fusion algorithm of multiple sensors, wherein the sensors comprise a laser radar sensor and a vision sensor, and the size and the distribution of the obstacles are obtained by signals detected by the laser radar sensor and the vision sensor through the data fusion algorithm.
2. The unmanned aerial vehicle autonomous obstacle avoidance inspection method according to claim 1, characterized in that: the data fusion algorithm adopts a complementary fusion algorithm.
3. The unmanned aerial vehicle autonomous obstacle avoidance inspection method according to claim 1, characterized in that: the visual perception information extraction and analysis comprises camera parameter calibration and environment modeling;
the camera parameter calibration adopts a Zhangyingyou camera calibration method to extract the angular points corresponding to the calibration plate, and carries out camera internal parameter calibration according to the position relationship between the extracted angular points; when calibrating the external parameters of the camera, acquiring the external parameters by adopting a Zhangyingyou camera calibration method, calculating the position of each camera relative to the same calibration plate, and obtaining the position relation between the binocular cameras through coordinate transformation so as to realize the calibration of the external parameters of the camera;
the environment modeling comprises the following steps: firstly, preprocessing an acquired image, mainly comprising smoothing processing by a median filtering method and edge extraction by an LOG edge detection operator; secondly, utilizing constraint conditions such as epipolar geometry, image edges and the like, and adopting a method based on normalized gray scale to realize stereo matching of image feature points; then, calculating the space three-dimensional coordinates of the space points in the coordinate system of the unmanned aerial vehicle by using the projection relation of the camera and a corresponding formula, thereby completing the three-dimensional reconstruction of the environment; and finally, realizing the reconstruction of the environment two-dimensional map by adopting a method of rasterizing the obstacles.
4. The unmanned aerial vehicle autonomous obstacle avoidance inspection method according to claim 1, characterized in that: the lidar detection employs a time-of-flight ranging method that obtains the target object distance by continuously transmitting light pulses to the target, then receiving light returning from the object with a sensor, and detecting the time of flight (round trip) of the light pulses.
5. The unmanned aerial vehicle autonomous obstacle avoidance inspection method according to claim 1, characterized in that: and c, after confirming that the unmanned aerial vehicle encounters an obstacle, searching a path according to the optimal path and/or a local path according to the obstacle distribution, and adjusting the path planning in real time.
6. The unmanned aerial vehicle autonomous obstacle avoidance inspection method according to claim 1, characterized in that: the optimal path search adopts an A-x algorithm, and an estimation function of the A-x algorithm is as follows: (n) ═ g (n) + h (n), where: g (n) represents the actual distance from the starting point to any vertex n, and h (n) represents the estimated distance from any vertex n to the target vertex.
7. The unmanned aerial vehicle autonomous obstacle avoidance inspection method according to claim 1, characterized in that: the local path planning adopts potential force field path planning, the potential force field comprises attraction potential generated by a target and repulsion potential generated by a barrier, the attraction potential and the repulsion potential are combined to guide the flight direction of the unmanned aerial vehicle, and the flight track generated by the unmanned aerial vehicle under the guidance of the potential field force is the planned path.
The potential force field is calculated by the following formula:
a gravitational field:
xi here is the scale factor, ρ (q, q)goal) Indicating the distance of the current state of the object from the target.
Attraction force:
a repulsive force field:
eta in the formula is the repulsive scale factor, rho (q, q)obs) Representing the distance between the object and the obstacle; rho0Represents the radius of influence of each obstacle;
repulsion force:
the total field is the superposition of the gravitational field of the repulsive field:
U(q)＝Uatt(q)+Urep(q)
the total force is also a superposition of the corresponding force components:
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