CN112572416A - Parking method and system for unmanned vehicle in mining area - Google Patents
Parking method and system for unmanned vehicle in mining area Download PDFInfo
- Publication number
- CN112572416A CN112572416A CN202011307154.7A CN202011307154A CN112572416A CN 112572416 A CN112572416 A CN 112572416A CN 202011307154 A CN202011307154 A CN 202011307154A CN 112572416 A CN112572416 A CN 112572416A
- Authority
- CN
- China
- Prior art keywords
- cost
- vehicle
- distance
- value
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/06—Automatic manoeuvring for parking
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
Abstract
The invention discloses a parking method and a parking system for unmanned vehicles in a mining area, which comprise the following steps: according to the current posture and the current position of the vehicle, determining the cost weight and the predicted running time related to the following items: distance cost, orientation cost, direction cost and obstacle avoidance cost; traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle, and predicting each driving track of the vehicle within the prediction time by using an Ackerman model; determining the following costs in each predicted driving trajectory: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value of each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track. The invention avoids collision with the barrier and realizes barrier avoidance.
Description
Technical Field
The invention relates to the field of mine vehicle driving, in particular to a parking method and system for unmanned vehicles in a mine.
Background
In a mine, the cars are generally parked evenly at a relatively low speed (below 6 km/h) alongside the excavator for loading. Due to the complexity of the mining area, certain requirements are imposed on the running path, the final position and the orientation of the mine car, otherwise, the unmanned vehicle in the mining area can not normally charge, and in the parking process, scattered obstacles (possibly ores) with large volume can appear in the running area, and at the moment, collision risks can appear if the obstacles cannot be avoided.
Disclosure of Invention
The invention aims to provide a parking method and a parking system for a mine unmanned vehicle, which solve the problem that in the parking process in the prior art, a large-volume scattered obstacle appears in a driving area, avoid collision with the obstacle and realize obstacle avoidance.
In order to achieve the above object, the present invention provides a parking method of a mine unmanned vehicle, comprising: according to the current posture and the current position of the vehicle, determining the cost weight and the predicted running time related to the following items: distance cost, orientation cost, direction cost and obstacle avoidance cost; traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle, and predicting each driving track of the vehicle within the prediction time by using an Ackerman model; determining the following costs in each predicted driving trajectory: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value of each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track.
Preferably, the determining each item of cost weight and the predicted travel time according to the current posture and the current position of the vehicle includes: when the current posture of the vehicle shows that an included angle between the orientation of the vehicle and the expected orientation is smaller than a preset angle threshold, determining that the cost weight of the distance cost is a first preset value and the cost weight of the orientation cost is a second preset value, otherwise, determining that the cost weight of the distance cost is a third preset value and the cost weight of the orientation cost is a fourth preset value; the first preset value is larger than the second preset value, and the third preset value is smaller than the fourth preset value; obtaining the spacing distance between the vehicle and an obstacle, and determining a cost weight value of the obstacle avoidance cost according to the comparison value of the spacing distance and a predicted distance correlation value; configuring a cost weight value of the directional cost as a fifth preset value; and obtaining a vertical distance between a current position and a desired position of the vehicle, the predicted travel time being determined according to either: the size relation between the vertical distance and a preset distance threshold value and the size relation between the spacing distance and a prediction distance correlation value.
Preferably, the determining the cost weight of the obstacle avoidance cost according to the comparison between the separation distance and the predicted distance correlation value includes: when the separation distance is larger than twice of the predicted distance, determining that the cost weight value of the obstacle avoidance cost is 0, otherwise, configuring the cost weight value of the obstacle avoidance cost to be associated with the user requirement.
Preferably, the method of determining the predicted travel time includes: when the vertical distance is greater than a preset distance threshold or the spacing distance is less than twice a predicted distance, configuring the predicted travel time as a first preset time value; otherwise, the predicted travel time is configured to be a second preset time value, wherein the first preset time value is smaller than the second preset time value.
Preferably, the following costs are determined for each predicted driving trajectory: the distance cost, the orientation cost, the direction cost and the obstacle avoidance cost comprise: determining a vertical distance from the end of the predicted travel trajectory to a desired position as a distance cost; taking an included angle between the orientation of the vehicle at the end position of the predicted driving track and the expected orientation as an orientation cost; taking an included angle between the orientation of the vehicle at the end position of the predicted driving track and a preset direction as a direction cost, wherein the preset direction is configured to be a direction of a connecting line of the end of the predicted driving track and the expected position; calculating the obstacle avoidance cost by the following formula:
wherein, r isiIn order to compare the minimum distances between all the front axle center positions and all the rear axle center positions of the vehicle in the ith running track and the obstacles, n is the number of the running tracks, rjAnd the distances from the obstacles to all the front axle center positions and the rear axle center positions of the vehicle in the j-th driving track in the n driving tracks.
Preferably, after controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal trajectory, the parking method for the unmanned vehicle in the mining area further includes: judging whether the vehicle reaches a terminal, and controlling the vehicle to stop under the condition of reaching the terminal; and if not, continuing to execute the steps of determining various related cost weights and predicting the running time according to the current posture and the current position of the vehicle until the vehicle reaches the terminal.
In addition, the present invention provides a parking system for a mine unmanned vehicle, comprising: the weight time determining unit is used for determining the cost weight and the predicted running time related to the following items according to the current posture and the current position of the vehicle: distance cost, orientation cost, direction cost and obstacle avoidance cost; the track determining unit is used for traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle and predicting each driving track of the vehicle within the prediction time by utilizing an Ackerman model; a vehicle control unit for determining the following costs in each predicted travel trajectory: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value aiming at each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track.
Preferably, the weight time determination unit includes: the distance orientation cost weight determination module is used for determining that the cost weight of the distance cost is a first preset value and the cost weight of the orientation cost is a second preset value when the current posture of the vehicle shows that an included angle between the orientation of the vehicle and the expected orientation is smaller than a preset angle threshold, otherwise, determining that the cost weight of the distance cost is a third preset value and the cost weight of the orientation cost is a fourth preset value; the first preset value is larger than the second preset value, and the third preset value is smaller than the fourth preset value; the obstacle avoidance cost weight value determining module is used for acquiring the spacing distance between the vehicle and an obstacle and determining the cost weight value of the obstacle avoidance cost according to the comparison value of the spacing distance and the predicted distance correlation value; the direction cost weight value configuration module is used for configuring the cost weight value of the direction cost to be a fifth preset value; and a predicted travel time determination module for obtaining a vertical distance between a current position and a desired position of the vehicle, and determining the predicted travel time according to either: the size relation between the vertical distance and a preset distance threshold value and the size relation between the spacing distance and a prediction distance correlation value.
According to the technical scheme, the method is different from the traditional robot dynamic window method, the ackerman steering kinematics model is used for predicting the track, and the method basically accords with the actual vehicle kinematics characteristics under the condition of low-speed parking. In addition, all the driving related costs are used as each weight to be integrally analyzed, the optimal driving track is determined according to the comparison result of the finally obtained numerical values, the vehicle parking is controlled based on the optimal track, the whole process can be automatically executed, and the problems that the mine car cannot avoid obstacles and cannot automatically control the vehicle to drive in the prior art are solved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating a method of parking unmanned vehicles in a mine area according to the present invention;
FIG. 2 is a graph illustrating the simulation effect of the driving trajectory of the parking method of unmanned vehicles in a mining area according to the present invention;
FIG. 3 is a diagram illustrating the effect of the present invention in determining the following costs in predicted travel trajectories; and
fig. 4 is a flow chart illustrating another method of parking unmanned vehicles in a mine area according to the present 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. Fig. 1 is a flowchart of a parking method of a mine unmanned vehicle according to the present invention, and as shown in fig. 1, the parking method of the mine unmanned vehicle includes:
s101, determining the cost weight and the predicted running time related to the following items according to the current posture and the current position of the vehicle: distance cost, orientation cost, direction cost and obstacle avoidance cost.
In order to avoid collision, the present invention uses a preset range (for example, 5m) of the center position of the front axle as a collision danger range, and uses a preset range (for example, 4.5m) of the center position of the rear axle as a collision danger range, wherein the distance from the vehicle to the obstacle is the distance from the obstacle to the track range.
And S102, traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle, and predicting each driving track of the vehicle within the prediction time by using an Ackerman model.
Wherein the traversed trajectory plot is shown in fig. 2, and fig. 2 is a simulated effect plot, wherein the (-10, 25) position is the position of the obstacle, and the (0, 0) position is the excavator position, i.e. the position in the desired direction of travel of the vehicle. The travel locus is as shown in the figure, and the uppermost is the outermost contour of the vehicle.
S103, determining the following costs in each predicted driving track: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value of each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track.
And processing each item of cost in the driving track by utilizing normalization to obtain the cost, wherein the smaller the sum of the products is, the closer the driving track is to the expected position is, and the better the simulated route is.
Preferably, specifically, given a set of information of speed and front wheel steering angle, a corresponding track (including a front axle center and a rear axle center) is generated within a specified time according to a current attitude of the vehicle (low-speed scene), and the determining the cost weights and the predicted travel time according to the current attitude and the current position of the vehicle comprises:
when the current posture of the vehicle shows that an included angle between the orientation of the vehicle and the expected orientation is smaller than a preset angle threshold value of 70 degrees, determining that the cost weight W1 of the distance cost is a first preset value 170 and the cost weight W2 of the orientation cost is a second preset value 2, otherwise determining that the cost weight W1 of the distance cost is a third preset value 1 and the cost weight of the orientation cost W2 is a fourth preset value 100; the first preset value is larger than the second preset value, and the third preset value is smaller than the fourth preset value. The smaller the cost of W1, the smaller the distance between the vehicle and the desired heading. The smaller the cost of W2, the closer the final orientation of the vehicle is to the desired orientation.
Obtaining a separation distance between the vehicle and an obstacle, and determining a cost weight W4 of the obstacle avoidance cost according to the comparison of the separation distance and a predicted distance correlation value, wherein the separation distance is the distance between the vehicle position and the obstacle; the vehicle position may be defined as a center position of the vehicle
Configuring a cost weight W3 of the directional cost as a fifth preset value 2; and
obtaining a vertical distance between a current position and a desired position of the vehicle, and determining the predicted travel time according to either: the size relation between the vertical distance and a preset distance threshold value and the size relation between the spacing distance and a prediction distance correlation value.
Preferably, the determining the cost weight of the obstacle avoidance cost according to the comparison magnitude of the separation distance and the predicted distance correlation value may include: when the separation distance is greater than twice the predicted distance (16m), determining the cost weight W4 of the obstacle avoidance cost to be 0, otherwise, configuring the cost weight W4 of the obstacle avoidance cost to be associated with the user demand.
Preferably, the method of determining the predicted travel time may include: when the vertical distance is greater than a preset distance threshold or the separation distance is less than twice a predicted distance dist, configuring the predicted travel time as a first preset time value 2 s; otherwise, the predicted travel time is configured to be a second preset time value 4s, wherein the first preset time value 2s is smaller than the second preset time value 4 s.
Preferably, as shown in fig. 3, the following costs are determined for each predicted driving trajectory: the distance cost, the orientation cost, the direction cost and the obstacle avoidance cost comprise:
determining a vertical distance from the tail end a of the predicted driving track to a desired position (excavator) as a distance cost, namely a straight-line vertical distance value from a point a to the desired position, wherein a point O is an initial position of the predicted driving track;
taking an included angle between the orientation (vertically upward from the point a in fig. 3) of the vehicle at the end a position of the predicted driving track and the expected orientation as an orientation cost;
taking an included angle between an orientation (vertically upward from a point a in fig. 3) of the vehicle at an end position of a predicted travel track and a preset direction (direction of a connecting line W-a) as a directional cost, wherein the preset direction is configured to be a direction of the connecting line of the end of the predicted travel track and the expected position;
calculating the obstacle avoidance cost by the following formula:
wherein, r isiIn order to compare the minimum distances between all the front axle center positions and all the rear axle center positions of the vehicle in the ith running track and the obstacles, n is the number of the running tracks, rjAnd the distances from the obstacles to all the front axle center positions and the rear axle center positions of the vehicle in the j-th driving track in the n driving tracks. Wherein in the predicted path meeting the no-collision condition, the obstacle cost condition is calculated by adopting the formula.
Preferably, as shown in fig. 4, after controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal trajectory, the parking method for the unmanned vehicle in the mining area further includes: judging whether the vehicle reaches a terminal, and controlling the vehicle to stop under the condition of reaching the terminal; and if not, continuing to execute the steps of determining various related cost weights and predicting the running time according to the current posture and the current position of the vehicle until the vehicle reaches the terminal.
The invention also provides a parking system of the unmanned vehicle in the mining area, which comprises the following components: the weight time determining unit is used for determining the cost weight and the predicted running time related to the following items according to the current posture and the current position of the vehicle: distance cost, orientation cost, direction cost and obstacle avoidance cost; the track determining unit is used for traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle and predicting each driving track of the vehicle within the prediction time by utilizing an Ackerman model; a vehicle control unit for determining the following costs in each predicted travel trajectory: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value aiming at each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track.
Preferably, the weight time determination unit includes: the distance orientation cost weight determination module is used for determining that the cost weight of the distance cost is a first preset value and the cost weight of the orientation cost is a second preset value when the current posture of the vehicle shows that an included angle between the orientation of the vehicle and the expected orientation is smaller than a preset angle threshold, otherwise, determining that the cost weight of the distance cost is a third preset value and the cost weight of the orientation cost is a fourth preset value; the first preset value is larger than the second preset value, and the third preset value is smaller than the fourth preset value; the obstacle avoidance cost weight value determining module is used for acquiring the spacing distance between the vehicle and an obstacle and determining the cost weight value of the obstacle avoidance cost according to the comparison value of the spacing distance and the predicted distance correlation value; the direction cost weight value configuration module is used for configuring the cost weight value of the direction cost to be a fifth preset value; and a predicted travel time determination module for obtaining a vertical distance between a current position and a desired position of the vehicle, and determining the predicted travel time according to either: the size relation between the vertical distance and a preset distance threshold value and the size relation between the spacing distance and a prediction distance correlation value.
Compared with the prior art, the parking method of the mine unmanned vehicle has the same distinguishing technical characteristics and technical effects as the parking system of the mine unmanned vehicle.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. A parking method for a mine unmanned vehicle, the parking method comprising:
according to the current posture and the current position of the vehicle, determining the cost weight and the predicted running time related to the following items: distance cost, orientation cost, direction cost and obstacle avoidance cost;
traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle, and predicting each driving track of the vehicle within the prediction time by using an Ackerman model;
determining the following costs in each predicted driving trajectory: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value of each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track.
2. The method of parking unmanned vehicles in mining areas of claim 1, wherein the determining cost weights and predicted travel time according to the current pose and current position of the vehicle comprises:
when the current posture of the vehicle shows that an included angle between the orientation of the vehicle and the expected orientation is smaller than a preset angle threshold, determining that the cost weight of the distance cost is a first preset value and the cost weight of the orientation cost is a second preset value, otherwise, determining that the cost weight of the distance cost is a third preset value and the cost weight of the orientation cost is a fourth preset value; the first preset value is larger than the second preset value, and the third preset value is smaller than the fourth preset value;
obtaining the spacing distance between the vehicle and an obstacle, and determining a cost weight value of the obstacle avoidance cost according to the comparison value of the spacing distance and a predicted distance correlation value;
configuring a cost weight value of the directional cost as a fifth preset value; and
obtaining a vertical distance between a current position and a desired position of the vehicle, and determining the predicted travel time according to either: the size relation between the vertical distance and a preset distance threshold value and the size relation between the spacing distance and a prediction distance correlation value.
3. The parking method of unmanned vehicles in mining areas according to claim 2, wherein the determining the cost weight of the obstacle avoidance cost according to the comparison between the separation distance and the predicted distance correlation value comprises:
when the separation distance is larger than twice of the predicted distance, determining that the cost weight value of the obstacle avoidance cost is 0, otherwise, configuring the cost weight value of the obstacle avoidance cost to be associated with the user requirement.
4. The method of parking unmanned vehicles in mine areas of claim 2, wherein the method of determining the predicted travel time comprises:
when the vertical distance is greater than a preset distance threshold or the spacing distance is less than twice a predicted distance, configuring the predicted travel time as a first preset time value; otherwise, the predicted travel time is configured to be a second preset time value, wherein the first preset time value is smaller than the second preset time value.
5. The method of parking unmanned vehicles in mine areas of claim 1, wherein the following costs are determined for each predicted driving trajectory: the distance cost, the orientation cost, the direction cost and the obstacle avoidance cost comprise:
determining a vertical distance from the end of the predicted travel trajectory to a desired position as a distance cost;
taking an included angle between the orientation of the vehicle at the end position of the predicted driving track and the expected orientation as an orientation cost;
taking an included angle between the orientation of the vehicle at the end position of the predicted driving track and a preset direction as a direction cost, wherein the preset direction is configured to be a direction of a connecting line of the end of the predicted driving track and the expected position;
calculating the obstacle avoidance cost by the following formula:
wherein, r isiIn order to compare the minimum distances between all the front axle center positions and all the rear axle center positions of the vehicle in the ith running track and the obstacles, n is the number of the running tracks, rjAnd the distances from the obstacles to all the front axle center positions and the rear axle center positions of the vehicle in the j-th driving track in the n driving tracks.
6. The parking method of the mine unmanned vehicle according to claim 1, wherein after controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal trajectory, the parking method of the mine unmanned vehicle further comprises:
judging whether the vehicle reaches a terminal, and controlling the vehicle to stop under the condition of reaching the terminal; and if not, continuing to execute the steps of determining various related cost weights and predicting the running time according to the current posture and the current position of the vehicle until the vehicle reaches the terminal.
7. A parking system for a mine unmanned vehicle, the parking system comprising:
the weight time determining unit is used for determining the cost weight and the predicted running time related to the following items according to the current posture and the current position of the vehicle: distance cost, orientation cost, direction cost and obstacle avoidance cost;
the track determining unit is used for traversing each front wheel steering angle of the vehicle according to the current speed of the vehicle and predicting each driving track of the vehicle within the prediction time by utilizing an Ackerman model;
a vehicle control unit for determining the following costs in each predicted travel trajectory: distance cost, orientation cost, direction cost and obstacle avoidance cost; and calculating to obtain the sum of products of each cost and the cost weight value aiming at each predicted driving track, comparing to obtain the minimum value of the sum of the products and the driving track corresponding to the minimum value as the optimal track, and controlling the vehicle to park based on the front wheel steering angle corresponding to the optimal track.
8. The parking system of unmanned vehicles in mining areas of claim 7, wherein the weight time determination unit comprises:
the distance orientation cost weight determination module is used for determining that the cost weight of the distance cost is a first preset value and the cost weight of the orientation cost is a second preset value when the current posture of the vehicle shows that an included angle between the orientation of the vehicle and the expected orientation is smaller than a preset angle threshold, otherwise, determining that the cost weight of the distance cost is a third preset value and the cost weight of the orientation cost is a fourth preset value; the first preset value is larger than the second preset value, and the third preset value is smaller than the fourth preset value;
the obstacle avoidance cost weight value determining module is used for acquiring the spacing distance between the vehicle and an obstacle and determining the cost weight value of the obstacle avoidance cost according to the comparison value of the spacing distance and the predicted distance correlation value;
the direction cost weight value configuration module is used for configuring the cost weight value of the direction cost to be a fifth preset value; and
a predicted travel time determination module configured to obtain a vertical distance between a current position and a desired position of the vehicle, and determine the predicted travel time according to any one of: the size relation between the vertical distance and a preset distance threshold value and the size relation between the spacing distance and a prediction distance correlation value.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011307154.7A CN112572416A (en) | 2020-11-20 | 2020-11-20 | Parking method and system for unmanned vehicle in mining area |
| CN202110429802.4A CN113022553B (en) | 2020-11-20 | 2021-04-21 | Parking method and system for unmanned vehicle in mining area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011307154.7A CN112572416A (en) | 2020-11-20 | 2020-11-20 | Parking method and system for unmanned vehicle in mining area |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112572416A true CN112572416A (en) | 2021-03-30 |
Family
ID=75122967
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011307154.7A Pending CN112572416A (en) | 2020-11-20 | 2020-11-20 | Parking method and system for unmanned vehicle in mining area |
| CN202110429802.4A Active CN113022553B (en) | 2020-11-20 | 2021-04-21 | Parking method and system for unmanned vehicle in mining area |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110429802.4A Active CN113022553B (en) | 2020-11-20 | 2021-04-21 | Parking method and system for unmanned vehicle in mining area |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN112572416A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114018276A (en) * | 2021-09-14 | 2022-02-08 | 武汉光庭信息技术股份有限公司 | Optimal parking space determining method and system, electronic device and storage medium |
| CN114415652A (en) * | 2021-11-09 | 2022-04-29 | 南京南自信息技术有限公司 | Wheel type robot path planning method |
| CN114940164A (en) * | 2022-05-20 | 2022-08-26 | 重庆邮电大学 | Parking scene-oriented unmanned vehicle driving track optimization method and system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108549378B (en) * | 2018-05-02 | 2021-04-20 | 长沙学院 | Mixed path planning method and system based on grid map |
| US11117569B2 (en) * | 2018-06-27 | 2021-09-14 | Baidu Usa Llc | Planning parking trajectory generation for self-driving vehicles using optimization method |
| CN110077397B (en) * | 2019-05-14 | 2020-08-04 | 芜湖汽车前瞻技术研究院有限公司 | Intelligent vehicle obstacle avoidance trajectory planning method and device |
| CN111006666B (en) * | 2019-11-21 | 2021-10-29 | 深圳市优必选科技股份有限公司 | Robot path planning method and device, storage medium and robot |
| CN110954123B (en) * | 2019-12-10 | 2021-05-04 | 电子科技大学 | Path planning method based on Ackerman constraint |
-
2020
- 2020-11-20 CN CN202011307154.7A patent/CN112572416A/en active Pending
-
2021
- 2021-04-21 CN CN202110429802.4A patent/CN113022553B/en active Active
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114018276A (en) * | 2021-09-14 | 2022-02-08 | 武汉光庭信息技术股份有限公司 | Optimal parking space determining method and system, electronic device and storage medium |
| CN114415652A (en) * | 2021-11-09 | 2022-04-29 | 南京南自信息技术有限公司 | Wheel type robot path planning method |
| CN114415652B (en) * | 2021-11-09 | 2024-03-26 | 南京南自信息技术有限公司 | Path planning method for wheeled robot |
| CN114940164A (en) * | 2022-05-20 | 2022-08-26 | 重庆邮电大学 | Parking scene-oriented unmanned vehicle driving track optimization method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113022553B (en) | 2022-07-12 |
| CN113022553A (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113022553B (en) | Parking method and system for unmanned vehicle in mining area | |
| CN108256233B (en) | Intelligent vehicle trajectory planning and tracking method and system based on driver style | |
| CN110471408B (en) | Unmanned vehicle path planning method based on decision process | |
| CN113386795A (en) | Intelligent decision-making and local track planning method for automatic driving vehicle and decision-making system thereof | |
| US20190204842A1 (en) | Trajectory planner with dynamic cost learning for autonomous driving | |
| CN107933548A (en) | A kind of parking stall recognition methods of automatic parking and system | |
| CN111770865B (en) | Vehicle travel control device | |
| Fritz et al. | CHAUFFEUR Assistant: a driver assistance system for commercial vehicles based on fusion of advanced ACC and lane keeping | |
| CN114391088A (en) | Trajectory planner | |
| CN113848914B (en) | Method for planning local path by collision coefficient artificial potential field method in dynamic environment | |
| CN113978452B (en) | Automatic parallel parking path planning method | |
| CN110782707A (en) | Automobile lane change control method and device, automobile and storage medium | |
| CN112896188B (en) | Automatic driving decision control system considering front vehicle encounter | |
| CN106218612B (en) | A kind of method, apparatus and terminal of vehicle safety travel | |
| CN113291318B (en) | Unmanned vehicle blind area turning planning method based on partially observable Markov model | |
| CN113895463B (en) | Path planning method suitable for turning around of automatic driving vehicle | |
| CN113110489B (en) | Trajectory planning method and device, electronic equipment and storage medium | |
| Hang et al. | Human-like lane-change decision making for automated driving with a game theoretic approach | |
| WO2022004042A1 (en) | Vehicle control device and vehicle control system | |
| CN114368368B (en) | Vehicle control system and method | |
| CN116465427B (en) | Intelligent vehicle lane changing obstacle avoidance path planning method based on space-time risk quantification | |
| CN115140048B (en) | Automatic driving behavior decision and trajectory planning model and method | |
| CN115092183B (en) | Active obstacle avoidance control method and system for vehicle based on potential field force | |
| Wei et al. | Driver-centred autonomous vehicle motion control within a blended corridor | |
| CN114919362A (en) | Suspension system control method and device and vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210330 |