CN-121764161-B - Unmanned aerial vehicle self-adaptive obstacle avoidance control method and system based on environment awareness

CN121764161BCN 121764161 BCN121764161 BCN 121764161BCN-121764161-B

Abstract

The application relates to the technical field of unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle self-adaptive obstacle avoidance control method and system based on environment perception, wherein the method comprises the steps of obtaining environment perception data and flight state data; the method comprises the steps of constructing a slender obstacle parameter model, constructing an equivalent dangerous volume field and a repulsive potential field, generating a controllable risk potential field, generating obstacle avoidance control quantity and a reference track, performing online self-tuning on potential field parameters, and generating a final flight control command. Compared with the prior art, the method has the advantages that the method can not effectively convert the elongated obstacle into the safe control distance capable of participating in flight control calculation under the environment conditions that a large number of elongated obstacles such as wires, branches and vines exist in power grid inspection, forest areas low-altitude flight and the like. According to the application, the slender linear obstacle is mapped into the equivalent dangerous volume field, and the continuously-conductive repulsive potential energy field is constructed to participate in control quantity solving, so that the flight safety and stability of the unmanned aerial vehicle are improved.

Inventors

MA XUGUANG
ZHAO XINYONG
Zhao Chuchen
Gu huinan
ZHOU WEN

Assignees

华路易云科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260303

Claims (8)

1. The unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness is characterized by comprising the following steps of: Step 10, environment sensing data and current flight state data of an unmanned aerial vehicle, which are acquired by an unmanned aerial vehicle-mounted sensor, are acquired, and an elongated obstacle parameter model is constructed by adopting an elongated linear obstacle geometric discrimination modeling mechanism based on the environment sensing data and the flight state data, wherein the method specifically comprises the following steps: Step S101, obtaining environment sensing data in a preset time window, and executing time stamp alignment and coordinate system first processing on the environment sensing data to form an environment observation data set; Step S102, denoising and candidate structure extraction processing are carried out on an environment observation data set to obtain a candidate obstacle point set, wherein denoising comprises statistics outlier rejection and sparse complement, and candidate structure extraction comprises clustering based on neighborhood connectivity or primary screening based on line segment detection; Step 103, calculating covariance matrixes of candidate obstacle point sets, extracting characteristic values and characteristic vectors, judging the candidate obstacle point sets by combining preset fine length criteria, and outputting central line parameters and scale parameters of the judged slender linear obstacles to form a slender obstacle parameter model, wherein the scale parameters at least comprise length estimated values and equivalent diameter estimated values, and the central line parameters at least comprise coordinates and direction vectors of two end points of the central line; Step S20, performing an equivalent dangerous volume field construction task by adopting a Minkowski volume expansion and distance field coupling mapping mechanism based on an elongated obstacle parameter model, and outputting an obstacle equivalent dangerous volume field and obstacle repulsive potential energy field, wherein the method specifically comprises the following steps of: Step S201, acquiring geometric outline parameters and control error margin parameters of the unmanned aerial vehicle, calculating equivalent dangerous radius and generating obstacle volume expansion parameters, wherein the control error margin parameters are used for compensating wind disturbance, controlling risk deviation caused by tracking errors and sensor range errors; Step S202, mapping each elongated linear obstacle into a columnar forbidden volume by adopting Minkowski volume expansion according to central line parameters in an elongated obstacle parameter model, and performing gridding or implicit distance field coding on the columnar forbidden volume to obtain an obstacle equivalent dangerous volume field; step 203, calculating the shortest distance from a space point corresponding to the pose of the unmanned aerial vehicle to the center line of the obstacle on the basis of the obstacle equivalent dangerous volume field, and constructing an obstacle repulsive force potential energy field according to the shortest distance and a preset influence distance threshold value, wherein when the space point corresponding to the pose of the unmanned aerial vehicle enters the preset influence distance threshold value range, repulsive force potential energy is enhanced in a nonlinear way along with the reduction of the distance and is used for generating obstacle avoidance driving in advance; step S30, a potential field preprocessing task is executed by adopting a risk continuity modulation mechanism based on an obstacle equivalent dangerous volume field and an obstacle repulsive potential field, and a preprocessed controllable risk potential field is output; Step S40, acquiring task target navigation point data, executing an obstacle avoidance control amount solving task based on controllable risk potential fields, flight state data and self-adaptive control amount generating mechanism synthesized by the task target navigation point data by adopting attraction and repulsion, and outputting the obstacle avoidance control amount and a reference track segment; And S50, performing on-line parameter self-setting and landing control instruction generation according to the obstacle avoidance control quantity and the reference track segment, and outputting a final flight control instruction.
2. The unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness, as set forth in claim 1, wherein in the step S10, the environment awareness data comprise laser radar point cloud data, the flight state data comprise unmanned aerial vehicle positions, unmanned aerial vehicle speeds, unmanned aerial vehicle headings and unmanned aerial vehicle attitude angles, and the elongated obstacle parameter model is used for representing central line positions, direction vectors, length intervals and equivalent diameters of the elongated linear obstacle.
3. The unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness according to claim 1, wherein in step S20, an equivalent dangerous volume field is used for mapping the elongated linear obstacle from a one-dimensional line segment into a columnar forbidden volume with a safe radius, and a repulsive potential energy field is used for quantifying risk intensity distribution between the unmanned aerial vehicle and the elongated linear obstacle.
4. The unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness according to claim 1, wherein the obstacle avoidance control quantity comprises an expected acceleration vector, an expected speed vector or an expected yaw rate, the reference track segment is used for representing a continuous obstacle avoidance track in a preset prediction time window, the on-line parameter self-tuning comprises the steps of dynamically updating the influence distance of an obstacle, the repulsive force coefficient and the safety margin according to the change of speed and environment density, and the updated parameters are written back for potential field preprocessing and control quantity solving of the next period so as to realize closed-loop self-adaptive obstacle avoidance control.
5. The method for adaptively controlling obstacle avoidance of an unmanned aerial vehicle based on environmental awareness according to claim 1, wherein in step S40, task target waypoint data is acquired, an obstacle avoidance control amount solving task is executed by an adaptive control amount generating mechanism based on controllable risk potential field, flight state data and task target waypoint data synthesized by adopting attraction and repulsion, and the step of outputting the obstacle avoidance control amount and a reference track segment specifically comprises: step S401, acquiring task target waypoint data and flight state data, and calculating a target attraction direction and a target attraction strength based on the task target waypoint data and the flight state data to obtain a target attraction control component, wherein the target attraction control component is used for ensuring that the unmanned aerial vehicle still has a convergence trend towards a patrol target in the obstacle avoidance process; Step S402, calculating potential field gradient and risk direction vector at the current position of the unmanned aerial vehicle based on the controllable risk potential field, generating an obstacle repulsive force control component, and carrying out self-adaptive gain modulation on the repulsive force control component according to the speed of the unmanned aerial vehicle, wherein the self-adaptive gain modulation is used for enhancing repulsive force in advance in a high-speed or dense wire area; Step S403, combining a target gravitation control component and an obstacle repulsive force control component to form an obstacle avoidance control quantity, and limiting and smoothing the obstacle avoidance control quantity based on preset unmanned aerial vehicle dynamic constraint to obtain a reference track segment, wherein the limiting and smoothing are used for ensuring that the control quantity meets the constraint of maximum acceleration, maximum angular velocity and attitude inclination angle, and avoiding abrupt change of control instructions caused by abrupt potential field of an elongated obstacle.
6. An unmanned aerial vehicle self-adaptive obstacle avoidance control system based on environment perception, which is applied to the unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment perception according to any one of claims 1 to 5, and is characterized in that the unmanned aerial vehicle self-adaptive obstacle avoidance control system based on environment perception comprises: the device comprises an elongated obstacle parameter modeling module, a model analysis module and a model analysis module, wherein the elongated obstacle parameter modeling module is used for acquiring environment perception data and current flight state data of an unmanned aerial vehicle, which are acquired by an unmanned aerial vehicle onboard sensor, and constructing an elongated obstacle parameter model by adopting an elongated linear obstacle geometric discrimination modeling mechanism based on the environment perception data and the flight state data, and the method specifically comprises the following steps: acquiring environment sensing data in a preset time window, and executing time stamp alignment and coordinate system one processing on the environment sensing data to form an environment observation data set; Denoising and extracting candidate structures from the environmental observation data set to obtain a candidate obstacle point set, wherein denoising comprises statistics outlier rejection and sparse completion, and candidate structure extraction comprises clustering based on neighborhood connectivity or primary screening based on line segment detection; Calculating covariance matrix of the candidate obstacle point set, extracting characteristic values and characteristic vectors, judging the candidate obstacle point set by combining with a preset fine length criterion, and outputting central line parameters and scale parameters of the judged elongated linear obstacle to form an elongated obstacle parameter model, wherein the scale parameters at least comprise length estimated values and equivalent diameter estimated values, and the central line parameters at least comprise coordinates and direction vectors of two end points of the central line; the equivalent dangerous volume field construction module is used for executing an equivalent dangerous volume field construction task by adopting a Minkowski volume expansion and distance field coupling mapping mechanism based on an elongated obstacle parameter model and outputting an obstacle equivalent dangerous volume field and obstacle repulsive potential energy field, and specifically comprises the following steps: The method comprises the steps of obtaining geometric outline parameters and control error margin parameters of the unmanned aerial vehicle, calculating equivalent dangerous radius and generating obstacle volume expansion parameters, wherein the control error margin parameters are used for compensating wind disturbance, controlling risk deviation caused by tracking errors and sensor ranging errors; Mapping each elongated linear obstacle into a columnar forbidden volume by adopting Minkowski volume expansion according to central line parameters in the elongated obstacle parameter model, and performing rasterization or implicit distance field coding on the columnar forbidden volume to obtain an obstacle equivalent dangerous volume field; Calculating the shortest distance from a space point corresponding to the pose of the unmanned aerial vehicle to the center line of the obstacle on the basis of the equivalent dangerous volume field of the obstacle, and constructing an obstacle repulsive potential energy field according to the shortest distance and a preset influence distance threshold value, wherein when the space point corresponding to the pose of the unmanned aerial vehicle enters the preset influence distance threshold value range, repulsive potential energy is enhanced in a nonlinear manner along with the reduction of the distance and is used for generating obstacle avoidance driving in advance; the potential field preprocessing module is used for executing a potential field preprocessing task based on the obstacle equivalent dangerous volume field and the obstacle repulsive potential energy field by adopting a risk continuity modulation mechanism and outputting a preprocessed controllable risk potential field; The obstacle avoidance control quantity solving module is used for acquiring the task target navigation point data, executing an obstacle avoidance control quantity solving task based on the controllable risk potential field, the flight state data and the self-adaptive control quantity generating mechanism synthesized by the task target navigation point data by adopting attraction and repulsion, and outputting the obstacle avoidance control quantity and the reference track segment; the on-line self-tuning and instruction generation module is used for generating on-line parameter self-tuning and landing control instructions according to the obstacle avoidance control quantity and the reference track segment and outputting final flight control instructions.
7. The unmanned aerial vehicle self-adaptive obstacle avoidance control device based on environment awareness is characterized by comprising a memory, a processor and an unmanned aerial vehicle self-adaptive obstacle avoidance control program based on environment awareness, wherein the unmanned aerial vehicle self-adaptive obstacle avoidance control program based on environment awareness is stored on the memory and can run on the processor, and the unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness is realized when the unmanned aerial vehicle self-adaptive obstacle avoidance control program based on environment awareness is executed by the processor, and the unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness is as claimed in any one of claims 1 to 5.
8. A computer program product, characterized in that the computer program product comprises an environment awareness based unmanned aerial vehicle adaptive obstacle avoidance control program, which when executed by a processor implements an environment awareness based unmanned aerial vehicle adaptive obstacle avoidance control method according to any of claims 1 to 5.

Description

Unmanned aerial vehicle self-adaptive obstacle avoidance control method and system based on environment awareness Technical Field The invention relates to the technical field of unmanned aerial vehicle flight control, in particular to an unmanned aerial vehicle self-adaptive obstacle avoidance control method and system based on environment awareness. Background At present, unmanned aerial vehicle obstacle avoidance control technology relies on visual recognition, laser radar point cloud to construct a three-dimensional occupation grid or distance threshold judgment based on point obstacles to realize path avoidance. The method can obtain better obstacle avoidance effect when facing obstacles (such as walls, buildings, trunks and the like) with large volume and obvious geometric outline. However, in practical application scenes such as power grid inspection, forest low-altitude flight and the like, a large number of elongated obstacles such as wires, branches, vines and the like commonly exist in the environment, and the elongated obstacles can be detected through vision or point cloud at a perception level, but because the geometric size of the obstacles is far smaller than the size of an unmanned aerial vehicle body, the conventional obstacle avoidance algorithm often simplifies the obstacles into point-shaped information or is ignored in a grid modeling process, so that the elongated obstacles which are visible in vision are difficult to be effectively mapped into safe distance information which is available for control. For example, in the existing obstacle avoidance method based on an occupied grid or voxel map, the slender wires are in sparse linear distribution in the point cloud, due to limitation of grid resolution, a continuous occupied area cannot be formed, a flight control system is difficult to identify the risk range in advance, emergency obstacle avoidance actions are triggered only when unmanned aerial vehicles are extremely close, track mutation and even obstacle avoidance failure are easy to occur, in a control strategy based on point obstacle distance judgment, targets such as wires, thin branches and the like are often misjudged as traversable areas due to the fact that the number of points is small and the spatial continuity is poor, and therefore flight risks are increased remarkably. Therefore, the prior art cannot fully meet the requirements of the unmanned aerial vehicle on 'advanced, continuous and stable' obstacle avoidance control under the environment that a large number of slender obstacles exist, such as power grid inspection, forest cruising and the like. What is needed is an unmanned aerial vehicle self-adaptive obstacle avoidance method which can directly convert a perception result into an equivalent dangerous volume capable of participating in flight control calculation under a complex environment condition that an elongated linear obstacle widely exists, and can realize continuous smooth obstacle avoidance control so as to improve safety, stability and passing efficiency of the unmanned aerial vehicle in the flight process. Disclosure of Invention Aiming at the technical defects, the invention aims to provide an unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment perception, and aims to solve the technical problem that in the prior art, especially under the environment conditions that a large number of elongated obstacles such as wires, branches, vines and the like exist in power grid inspection, forest low-altitude flight and the like, the elongated obstacles cannot be effectively converted into safe control distances capable of participating in flight control calculation. In order to solve the technical problems, the invention adopts the following technical scheme that the invention provides an unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness. The unmanned aerial vehicle self-adaptive obstacle avoidance control method based on environment awareness comprises the following steps: s10, acquiring environment sensing data and current flight state data of an unmanned aerial vehicle, which are acquired by an onboard sensor of the unmanned aerial vehicle, and constructing an elongated obstacle parameter model by adopting an elongated linear obstacle geometric discrimination modeling mechanism based on the environment sensing data and the flight state data; s20, executing an equivalent dangerous volume field construction task by adopting a Minkowski volume expansion and distance field coupling mapping mechanism based on an elongated obstacle parameter model, and outputting an obstacle equivalent dangerous volume field and an obstacle repulsive potential energy field; step S30, a potential field preprocessing task is executed by adopting a risk continuity modulation mechanism based on an obstacle equivalent dangerous volume field and an obstacle repulsive potential field, and a prepro