CN116257058A - Intelligent local planning method for automatic driving logistics trolley in park - Google Patents
Intelligent local planning method for automatic driving logistics trolley in park Download PDFInfo
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- CN116257058A CN116257058A CN202310119465.8A CN202310119465A CN116257058A CN 116257058 A CN116257058 A CN 116257058A CN 202310119465 A CN202310119465 A CN 202310119465A CN 116257058 A CN116257058 A CN 116257058A
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims abstract description 10
- 238000010586 diagram Methods 0.000 claims description 7
- 230000004888 barrier function Effects 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims 1
- 230000008569 process Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention provides an intelligent local planning method for a park automatic driving logistics trolley. The method comprises the following steps that a main control system firstly establishes an interested area based on high-precision map exercisable area data and screens perceptually generated obstacles. And respectively adopting different methods to predict the track of the screened target according to the type of the obstacle. The main control system determines the global track from the starting point to the target point of the logistics trolley in a global road searching mode according to the real-time positioning information and the target point information. And intercepting part of the global path of the vehicle track. And generating a plurality of local paths to be selected according to the Frenet coordinate system and the fixed interval width. According to the real-time obstacle prediction track time sequence, calculating a space intersection point array of each obstacle and a plurality of real-time tracks, and performing time evaluation on each space intersection point to plan a logistics trolley path. The method effectively ensures the running safety of the automatic driving trolley.
Description
Technical Field
The invention relates to an intelligent local planning method for a park automatic driving logistics trolley, and belongs to the technical field of laser radar map building and automatic driving.
Background
The conventional park logistics trolley runs on a park main road according to a road path formulated by global planning, and in a local path planning part, obstacle avoidance processing is only considered when obstacles such as vehicles and pedestrians are encountered in a traffic road, but the simple obstacle avoidance algorithm has strong application boundary limit and extremely strong unsafe conditions aiming at complex perceived obstacle types and the conditions such as intersection meeting vehicles and the like. The utility model provides an intelligent local planning algorithm of district autopilot commodity circulation dolly. In the actual running process of the trolley, the method cuts out a part of the interested running area according to the prior vector map, extracts the barrier information in the interested area, classifies the barriers into three types of vehicles, vehicles and other types according to the types, and adopts different prediction algorithms for the three types of barriers. And outputting obstacle prediction information. And then intercepting part of the length of the global path according to the global path obtained by global path planning to obtain a local reference path, generating a plurality of local path plans by adopting a fraction coordinate system, evaluating different planned trajectories according to the current position of the real-time obstacle, and obtaining the selected final driving path according to the centering weight and the obstacle weight of the planned trajectories. Then, considering that the evaluation is only aimed at the obstacle relatively close to the vehicle in real time, the real-time safety evaluation of the remote rapid obstacle and the vehicle, especially the intersection working condition, cannot be ensured, and the collision risk exists, so that the local speed planning of the vehicle is required on the basis of the local path planning. Firstly, predicting an obstacle in an interested area, constructing a st graph according to predicted obstacle information, a local path and real-time speed of the vehicle, performing track intersection evaluation on a time sequence, and then planning the real-time speed of the vehicle. So as to ensure the running safety of the vehicle.
Disclosure of Invention
The invention aims to provide an intelligent local planning method for an automatic driving logistics trolley in a park, which is used for carrying out local path planning and real-time speed planning according to screened key obstacle information and combined with obstacle prediction information, so that the running safety of the automatic driving trolley is effectively ensured.
Step 1: the main control system intercepts the target area according to the vector diagram, completes the point cloud diagram construction of the closed park by utilizing a laser radar point cloud diagram construction technology, and extracts the barrier information of the target area;
step 2: marking the road nodes and the positions of the obstacles according to the obstacle information by manpower; generating a vehicle prediction track by the vehicle type obstacle based on the high-precision map road track and the vehicle running direction; the pedestrian obstacle forms a predicted track according to the pedestrian movement student; performing global path planning on the obtained closed park point cloud building map according to the generated prediction track main control system;
step 3: the main control system determines the current pose of the logistics trolley by utilizing an NDT registration algorithm through the point cloud information acquired in real time;
step 4: the logistics trolley determines current position information through the monocular camera and the laser radar, and uploads the position information to a main control system;
step 5: the main control system determines the global track from the starting point to the target point of the logistics trolley in a global road searching mode, performs local path planning and real-time speed planning by combining obstacle information, and sends the local path planning and real-time speed planning information to the logistics trolley;
step 6: the logistics trolley receives planning information and advances to a target place through the monocular camera and the laser radar.
Preferably, in the step 3, a technology of integrating the GPS and the IMU is further adopted to reposition and supplement the logistics trolley.
Preferably, the global road searching mode is specifically as follows: intercepting part of the length of the global path according to the global path obtained by global path planning to obtain a local reference path; the local reference path is a vehicle track within 10 seconds of the logistics trolley in the future;
adopting a Frenet coordinate system, fixing the interval width, generating a plurality of local path plans, and evaluating different planning tracks according to the current positions of real-time obstacles and logistics trolleys;
and obtaining the selected final driving path according to the weight of the obstacle in the planned track.
Preferably, the specific steps of obtaining the selected final driving path according to the weight of the obstacle in the planned trajectory are as follows: calculating a space cross point array of each obstacle and a plurality of real-time predicted tracks according to the real-time obstacle predicted tracks, performing time evaluation on each space cross point, evaluating whether the cross time difference is within a fixed threshold value, and if true collision points are generated, generating a true collision point array for all the obstacles;
real-time evaluation is carried out on the tracks to be selected according to the true collision point groups, and if true collision points exist in the current track, one track to be selected which has no true collision point and is closest to the vehicle is selected for running; if all the tracks have true collision points, the vehicle performs speed planning according to the current path, decelerates, and stops at a dangerous distance before the true collision points.
Preferably, the fixed threshold is 3s.
The invention has the advantages that: in the real-time traveling process of the trolley, the local path planning and the real-time speed planning are carried out according to the screened key obstacle information and the obstacle prediction information, so that the traveling safety of the automatic driving trolley is effectively ensured
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a schematic diagram of the screening of an obstacle ROI according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the method, in the real-time traveling process of the trolley, the local path planning and the real-time speed planning are carried out according to the screened key obstacle information and the obstacle prediction information, so that the traveling safety of the automatic driving trolley is effectively ensured.
The conventional park logistics trolley runs on a park main road according to a road path formulated by global planning, and in a local path planning part, obstacle avoidance processing is only considered when obstacles such as vehicles and pedestrians are encountered in a traffic road, but strong inconvenience still exists by considering the algorithm reliability and running speed of the logistics trolley and the application limit of the algorithm.
The method comprises the following steps: the main control system firstly establishes an interested region based on the high-precision map exercisable region data and screens perceptually generated obstacles. And respectively adopting different methods to predict the track of the screened target according to the type of the obstacle. The vehicle type obstacle generates a vehicle predicted trajectory (time series sequence) based on the high-precision map road trajectory and the vehicle traveling direction. The pedestrian obstacle forms a predicted track (time sequence) according to the pedestrian movement student. The main control system determines the global track from the starting point to the target point of the logistics trolley in a global road searching mode according to the real-time positioning information and the target point information. And according to the vehicle speed, intercepting a global path of the vehicle track length in the future 10 s. And generating a plurality of local paths to be selected according to the Frenet coordinate system and the fixed interval width. According to the real-time obstacle prediction track time sequence, calculating a space cross point array of each obstacle and a plurality of real-time tracks, performing time evaluation on each space cross point, evaluating whether the cross time difference is within a fixed threshold (3 s), and if true collision points are generated, generating a true collision point array for all the obstacles. And (3) carrying out real-time evaluation on the tracks to be selected according to the true collision point groups, and if the current track has true collision points, selecting one track to be selected which has no true collision points and is closest to the vehicle to run. If all the tracks have true collision points, the vehicle performs speed planning according to the current path, decelerates, and stops at a dangerous distance before the true collision points. The algorithm effectively ensures the running safety of the automatic driving trolley.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An intelligent local planning method for a park automatic driving logistics trolley is characterized by comprising a main control system and the logistics trolley, and comprises the following specific steps,
step 1: the main control system intercepts the target area according to the vector diagram, completes the point cloud diagram construction of the closed park by utilizing a laser radar point cloud diagram construction technology, and extracts the barrier information of the target area;
step 2: marking the road nodes and the positions of the obstacles according to the obstacle information by manpower; generating a vehicle prediction track by the vehicle type obstacle based on the high-precision map road track and the vehicle running direction; the pedestrian obstacle forms a predicted track according to the pedestrian movement student; performing global path planning on the obtained closed park point cloud building map according to the generated prediction track main control system;
step 3: the main control system determines the current pose of the logistics trolley by utilizing an NDT registration algorithm through the point cloud information acquired in real time;
step 4: the logistics trolley determines current position information through the monocular camera and the laser radar, and uploads the position information to a main control system;
step 5: the main control system determines the global track from the starting point to the target point of the logistics trolley in a global road searching mode, performs local path planning and real-time speed planning by combining obstacle information, and sends the local path planning and real-time speed planning information to the logistics trolley;
step 6: the logistics trolley receives planning information and advances to a target place through the monocular camera and the laser radar.
2. The intelligent local planning method for the automatic logistics trolley for the park according to claim 1, wherein the logistics trolley is further relocated and supplemented by adopting a fusion technology of GPS and IMU in the step 3.
3. The intelligent local planning method for the automatic driving logistics trolley in the park according to claim 1, wherein the global road searching mode is specifically as follows: intercepting part of the length of the global path according to the global path obtained by global path planning to obtain a local reference path; the local reference path is a vehicle track within 10 seconds of the logistics trolley in the future;
adopting a Frenet coordinate system, fixing the interval width, generating a plurality of local path plans, and evaluating different planning tracks according to the current positions of real-time obstacles and logistics trolleys;
and obtaining the selected final driving path according to the weight of the obstacle in the planned track.
4. The intelligent local planning method for the automatic driving logistics trolley in the park according to claim 3, wherein the specific steps of obtaining the selected final driving path according to the weight of the obstacle in the planned track are as follows: calculating a space cross point array of each obstacle and a plurality of real-time predicted tracks according to the real-time obstacle predicted tracks, performing time evaluation on each space cross point, evaluating whether the cross time difference is within a fixed threshold value, and if true collision points are generated, generating a true collision point array for all the obstacles;
real-time evaluation is carried out on the tracks to be selected according to the true collision point groups, and if true collision points exist in the current track, one track to be selected which has no true collision point and is closest to the vehicle is selected for running; if all the tracks have true collision points, the vehicle performs speed planning according to the current path, decelerates, and stops at a dangerous distance before the true collision points.
5. The intelligent local planning method for a campus autopilot logistics trolley of claim 4 wherein the fixed threshold is 3s.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117102724A (en) * | 2023-10-25 | 2023-11-24 | 宁波经纬数控股份有限公司 | Multi-cutting-head cutting path control method and cutting equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117102724A (en) * | 2023-10-25 | 2023-11-24 | 宁波经纬数控股份有限公司 | Multi-cutting-head cutting path control method and cutting equipment |
| CN117102724B (en) * | 2023-10-25 | 2024-02-23 | 宁波经纬数控股份有限公司 | Multi-cutting-head cutting path control method and cutting equipment |
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