CN113985895A - AGV path tracking method based on optimization - Google Patents
AGV path tracking method based on optimization Download PDFInfo
- Publication number
- CN113985895A CN113985895A CN202111438891.5A CN202111438891A CN113985895A CN 113985895 A CN113985895 A CN 113985895A CN 202111438891 A CN202111438891 A CN 202111438891A CN 113985895 A CN113985895 A CN 113985895A
- Authority
- CN
- China
- Prior art keywords
- path
- agv
- angle
- point
- eso
- 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
- 238000005457 optimization Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 15
- 230000001133 acceleration Effects 0.000 claims description 4
- 101710163391 ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase Proteins 0.000 claims description 2
- 230000001629 suppression Effects 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 5
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- 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
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
Abstract
The invention discloses an AGV path tracking method based on optimization, wherein each path point comprises the position of a current point and an expected yaw angle of the point, the expected yaw angle is the tangent angle of a curve where the path point is located, the current closest point of the pose where the AGV is located is calculated, finally, the corresponding path length is calculated according to the running speed and the target time of the AGV, and all path points from the closest point to the path length are output. According to the invention, the transverse control law of the vehicle is adjusted on line in real time by using an optimization tool according to the factors of the path and the running speed of the vehicle, so that the accurate control of path tracking and the robustness of running speed change are realized; and mainly relate to the AGV field, the main effect is that improve links such as commodity circulation transport, production AGV path tracking's precision, improve horizontal control to the robustness to the path change and the robustness to vehicle longitudinal speed change, this online optimization strategy can be adjusted according to actual conditions simultaneously, can realize optimal control effect.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to an AGV path tracking method based on optimization.
Background
In the field of production logistics and storage logistics, as labor cost rises and robotics develops rapidly, an AGV gradually becomes a trend, the AGV integrates positioning, sensing, navigation, control and other technologies, wherein a path tracking technology belongs to a basic and important technology, a control method based on a pre-aiming point is generally adopted in the traditional path tracking technology, the pre-aiming distance is usually fixed or changes according to the running speed, the tracking path of the AGV is generally a straight line, an arc, a Bezier curve and the like, and the traditional method for tracking the curve is usually poor due to the fact that the pre-aiming distance does not consider the path change.
Disclosure of Invention
The invention aims to provide an AGV path tracking method based on optimization so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: an optimization-based AGV path tracking method comprises the following steps:
A. path discretization into path points: each path point comprises the position of the current point and an expected yaw angle of the point, the expected yaw angle is the tangent angle of the curve where the path point is located, the current closest point of the pose where the AGV is located is calculated, finally, the corresponding optimized path length is calculated according to the running speed and the target time of the AGV, and all path points from the closest point to the optimized path length are output;
B. optimizing the AGV heading angle: then, the value range of the AGV heading angle to be determined is solved according to the angular velocity limit and the angular acceleration limit of the AGV heading, and finally, an optimization problem is solved according to the performance index and the limiting condition, so that the optimal heading for path tracking is obtained, wherein the optimization problem is as follows: wherein, theta is an AGV heading angle to be confirmed; n is the number of path points; diWhen the AGV heading angle is theta, the shortest distance from the ith path point to the vehicle running path is obtained; thetaiIs the tangent angle of the curve where the ith path point is located; thetaminAnd thetamaxIs the upper and lower bound of the AGV heading angle constraint; w1And W2The weights of the distance error and the course error are respectively;
C. course control: firstly, estimating equivalent disturbance and angular velocity values of a controlled object by using a first-order linear ESO (extended state observer), wherein the input of the ESO is input u (a last moment command course angle) of the controlled object and output y (an actual angular velocity) of the controlled object; and designing a PD control law according to the angular speed observed by the ESO, the actual course angle and the expected course angle, and finally obtaining the current expected course angle according to the uncertainty of the equivalent interference feedforward compensation system observed by the ESO.
ESO is an extended state observer, the formula is as follows:
l1and l2Is a parameter of ESO, y is an actual angular velocity, u is a controlled variable, z1Is an angular velocity estimate, z2Is an interference estimate. Preferably, curves such as straight lines and circular arcs are uniformly written in the form of bezier curves so that all paths are mathematically consistent, and the paths are discretized into path points.
Preferably, the performance index is designed according to the path generation path point, the current position and the AGV heading angle to be determined.
Preferably, according to the optimally output AGV command course angle, the command angular speed of the AGV chassis is output by course control, and aiming at the problem that different course angle characteristics of AGV loads are different, the ADRC is adopted in the design of a control algorithm to realize the tracking and interference suppression functions of the command course angle so as to realize accurate control.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the transverse control law of the vehicle is adjusted on line in real time by using an optimization tool according to the factors of the path and the running speed of the vehicle, so that the accurate control of path tracking and the robustness of running speed change are realized; and mainly relate to the AGV field, the main effect is that improve links such as commodity circulation transport, production AGV path tracking's precision, improve horizontal control to the robustness to the path change and the robustness to vehicle longitudinal speed change, this online optimization strategy can be adjusted according to actual conditions simultaneously, can realize optimal control effect.
Drawings
FIG. 1 is a flow chart of the path tracking optimization of the present invention;
FIG. 2 is a block diagram of a control module according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description herein, it is to be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientations and positional relationships indicated in the drawings to facilitate the description of the patent and to simplify the description, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be considered limiting of the patent. In the description of the present application, it should be noted that unless otherwise explicitly stated or limited, the terms "mounted," "connected," and "disposed" are to be construed broadly and can, for example, be fixedly connected, disposed, detachably connected, disposed, or integrally connected and disposed. The specific meaning of the above terms in this patent may be understood by those of ordinary skill in the art as appropriate.
Referring to fig. 1-2, an optimized AGV path tracking method includes the following steps:
A. path discretization into path points: each path point comprises the position of the current point and an expected yaw angle of the point, the expected yaw angle is the tangent angle of the curve where the path point is located, the current closest point of the pose where the AGV is located is calculated, finally, the corresponding optimized path length is calculated according to the running speed and the target time of the AGV, and all path points from the closest point to the optimized path length are output;
B. optimizing the AGV heading angle: then, the value range of the AGV heading angle to be determined is solved according to the angular velocity limit and the angular acceleration limit of the AGV heading, and finally, an optimization problem is solved according to the performance index and the limiting condition, so that the optimal heading for path tracking is obtained, wherein the optimization problem is as follows: wherein, theta is an AGV heading angle to be confirmed; n is the number of path points; diWhen the AGV heading angle is theta, the shortest distance from the ith path point to the vehicle running path is obtained; thetaiIs the tangent angle of the curve where the ith path point is located; thetaminAnd thetamaxIs the upper and lower bound of the AGV heading angle constraint; w1And W2The weights of the distance error and the course error are respectively;
C. course control: firstly, estimating equivalent disturbance and angular velocity values of a controlled object by using a first-order linear ESO (extended state observer), wherein the input of the ESO is input u (a last moment command course angle) of the controlled object and output y (an actual angular velocity) of the controlled object; and designing a PD control law according to the angular speed observed by the ESO, the actual course angle and the expected course angle, and finally obtaining the current expected course angle according to the uncertainty of the equivalent interference feedforward compensation system observed by the ESO.
ESO is an extended state observer, the formula is as follows:
l1and l2Is a parameter of ESO, y is an actual angular velocity, u is a controlled variable, z1Is an angular velocity estimate, z2Is an interference estimate.
The first embodiment is as follows:
an optimization-based AGV path tracking method comprises the following steps:
A. path discretization into path points: each path point comprises the position of the current point and an expected yaw angle of the point, the expected yaw angle is the tangent angle of the curve where the path point is located, the current closest point of the pose where the AGV is located is calculated, finally, the corresponding optimized path length is calculated according to the running speed and the target time of the AGV, and all path points from the closest point to the optimized path length are output;
B. optimizing the AGV heading angle: then, the value range of the AGV heading angle to be determined is solved according to the angular velocity limit and the angular acceleration limit of the AGV heading, and finally, an optimization problem is solved according to the performance index and the limiting condition, so that the optimal heading for path tracking is obtained, wherein the optimization problem is as follows: wherein, theta is an AGV heading angle to be confirmed; n is the number of path points; diWhen the AGV heading angle is theta, the shortest distance from the ith path point to the vehicle running path is obtained; thetaiIs the tangent angle of the curve where the ith path point is located; thetaminAnd thetamaxIs the upper and lower bound of the AGV heading angle constraint; w1And W2The weights of the distance error and the course error are respectively;
C. course control: firstly, estimating equivalent disturbance and angular velocity values of a controlled object by using a first-order linear ESO (extended state observer), wherein the input of the ESO is input u (a last moment command course angle) of the controlled object and output y (an actual angular velocity) of the controlled object; and designing a PD control law according to the angular speed observed by the ESO, the actual course angle and the expected course angle, and finally obtaining the current expected course angle according to the uncertainty of the equivalent interference feedforward compensation system observed by the ESO.
ESO is an extended state observer, the formula is as follows:
l1and l2Is a parameter of ESO, y is an actual angular velocity, u is a controlled variable, z1Is an angular velocity estimate, z2Is an interference estimate.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. An AGV path tracking method based on optimization is characterized in that: the path tracking method comprises the following steps:
A. path discretization into path points: each path point comprises the position of the current point and an expected yaw angle of the point, the expected yaw angle is the tangent angle of the curve where the path point is located, the current closest point of the pose where the AGV is located is calculated, finally, the corresponding optimized path length is calculated according to the running speed and the target time of the AGV, and all path points from the closest point to the optimized path length are output;
B. optimizing the AGV heading angle: then, the value range of the AGV heading angle to be determined is solved according to the angular velocity limit and the angular acceleration limit of the AGV heading, and finally, the optimization problem is solved according to the performance index and the limiting condition, so that the optimal heading for path tracking is obtained, wherein the optimization problem is as follows: wherein, theta is an AGV heading angle to be confirmed; n is the number of path points; diWhen the AGV heading angle is theta, the shortest distance from the ith path point to the vehicle running path is obtained; thetaiIs the tangent angle of the curve where the ith path point is located; thetaminAnd thetamaxIs the upper and lower bound of the AGV heading angle constraint; w1And W2The weights of the distance error and the course error are respectively;
C. course control: firstly, estimating equivalent disturbance and angular velocity values of a controlled object by using a first-order linear ESO (extended state observer), wherein the input of the ESO is input u (a last moment command course angle) of the controlled object and output y (an actual angular velocity) of the controlled object; designing a PD control law according to the angular speed observed by the ESO, the actual course angle and the expected course angle, and finally obtaining the current expected course angle according to the uncertainty of an equivalent interference feedforward compensation system observed by the ESO;
ESO is an extended state observer, the formula is as follows:
l1and l2Is a parameter of ESO, y is an actual angular velocity, u is a controlled variable, z1Is an angular velocity estimate, z2Is an interference estimate.
2. The optimized AGV path tracking method according to claim 1, further comprising: curves such as straight lines, circular arcs and the like are uniformly written in the form of Bezier curves so that all paths are consistent in mathematical form, and the paths are discretized into path points.
3. The optimized AGV path tracking method according to claim 1, further comprising: and generating path points and the current position according to the path and designing a performance index of the AGV course angle to be determined.
4. The optimized AGV path tracking method according to claim 1, further comprising: according to the optimized output AGV command course angle, the course control outputs the command angular speed of an AGV chassis, aiming at the problem that different course angle characteristics of AGV loads are different, the design of a control algorithm adopts ADRC to realize the tracking and interference suppression functions of the command course angle so as to realize accurate control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111438891.5A CN113985895A (en) | 2021-11-29 | 2021-11-29 | AGV path tracking method based on optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111438891.5A CN113985895A (en) | 2021-11-29 | 2021-11-29 | AGV path tracking method based on optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113985895A true CN113985895A (en) | 2022-01-28 |
Family
ID=79732583
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111438891.5A Pending CN113985895A (en) | 2021-11-29 | 2021-11-29 | AGV path tracking method based on optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113985895A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090276111A1 (en) * | 2008-05-05 | 2009-11-05 | Southwest Research Institute | Autonomous Ground Vehicle Control System For High-Speed And Safe Operation |
| CN101885350A (en) * | 2010-07-15 | 2010-11-17 | 南京航空航天大学 | Adaptive tracking control method of paths of comprehensive automatic guided vehicle |
| WO2018003814A1 (en) * | 2016-06-29 | 2018-01-04 | 日本電産株式会社 | Mobile body guidance system, mobile body, guidance device, and computer program |
| CN108958258A (en) * | 2018-07-25 | 2018-12-07 | 吉林大学 | A kind of track follow-up control method, control system and the relevant apparatus of unmanned vehicle |
| CN109489667A (en) * | 2018-11-16 | 2019-03-19 | 楚天智能机器人（长沙）有限公司 | A kind of improvement ant colony paths planning method based on weight matrix |
| CN110989625A (en) * | 2019-12-25 | 2020-04-10 | 湖南大学 | Vehicle path tracking control method |
| CN111007861A (en) * | 2019-12-24 | 2020-04-14 | 北京旷视机器人技术有限公司 | Trajectory tracking control method, device and system and mobile robot |
| CN111258323A (en) * | 2020-03-30 | 2020-06-09 | 华南理工大学 | Intelligent vehicle trajectory planning and tracking combined control method |
| CN111552310A (en) * | 2019-02-08 | 2020-08-18 | 沃科波特有限公司 | Aircraft and motion planning method and system thereof |
| CN112148024A (en) * | 2020-08-20 | 2020-12-29 | 中国人民解放军海军航空大学 | Unmanned aerial vehicle real-time online flight path planning method based on self-adaptive pseudo-spectral method |
| CN112965476A (en) * | 2021-01-22 | 2021-06-15 | 西安交通大学 | High-speed unmanned vehicle trajectory planning system and method based on multi-window sampling |
| CN113204236A (en) * | 2021-04-14 | 2021-08-03 | 华中科技大学 | Intelligent agent path tracking control method |
| CN113641159A (en) * | 2021-08-16 | 2021-11-12 | 佛山市毕佳索智能科技有限公司 | Trailer control method based on AGV |
-
2021
- 2021-11-29 CN CN202111438891.5A patent/CN113985895A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090276111A1 (en) * | 2008-05-05 | 2009-11-05 | Southwest Research Institute | Autonomous Ground Vehicle Control System For High-Speed And Safe Operation |
| CN101885350A (en) * | 2010-07-15 | 2010-11-17 | 南京航空航天大学 | Adaptive tracking control method of paths of comprehensive automatic guided vehicle |
| WO2018003814A1 (en) * | 2016-06-29 | 2018-01-04 | 日本電産株式会社 | Mobile body guidance system, mobile body, guidance device, and computer program |
| CN108958258A (en) * | 2018-07-25 | 2018-12-07 | 吉林大学 | A kind of track follow-up control method, control system and the relevant apparatus of unmanned vehicle |
| CN109489667A (en) * | 2018-11-16 | 2019-03-19 | 楚天智能机器人（长沙）有限公司 | A kind of improvement ant colony paths planning method based on weight matrix |
| CN111552310A (en) * | 2019-02-08 | 2020-08-18 | 沃科波特有限公司 | Aircraft and motion planning method and system thereof |
| CN111007861A (en) * | 2019-12-24 | 2020-04-14 | 北京旷视机器人技术有限公司 | Trajectory tracking control method, device and system and mobile robot |
| CN110989625A (en) * | 2019-12-25 | 2020-04-10 | 湖南大学 | Vehicle path tracking control method |
| CN111258323A (en) * | 2020-03-30 | 2020-06-09 | 华南理工大学 | Intelligent vehicle trajectory planning and tracking combined control method |
| CN112148024A (en) * | 2020-08-20 | 2020-12-29 | 中国人民解放军海军航空大学 | Unmanned aerial vehicle real-time online flight path planning method based on self-adaptive pseudo-spectral method |
| CN112965476A (en) * | 2021-01-22 | 2021-06-15 | 西安交通大学 | High-speed unmanned vehicle trajectory planning system and method based on multi-window sampling |
| CN113204236A (en) * | 2021-04-14 | 2021-08-03 | 华中科技大学 | Intelligent agent path tracking control method |
| CN113641159A (en) * | 2021-08-16 | 2021-11-12 | 佛山市毕佳索智能科技有限公司 | Trailer control method based on AGV |
Non-Patent Citations (3)
| Title |
|---|
| RONGRONG YU: "A novel trajectory tracking control of AGV based on Udwadia-Kalaba approach", IEEE/CAA JOURNAL OF AUTOMATICA SINICA, 9 November 2016 (2016-11-09) * |
| 康照奇: "基于组合路径规划和曲率前馈跟踪的工业AGV控制方法研究", 中国优秀硕士学位论文全文数据库信息科技辑, no. 1, 15 January 2022 (2022-01-15) * |
| 魏书平: "基于动态融合策略的AGV位置与姿态在线估计", 中国优秀硕士学位论文全文数据库信息科技辑, no. 1, 15 January 2019 (2019-01-15) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3932761A1 (en) | Vehicle abnormal lane change control method, device and system | |
| CN110568758B (en) | Parameter self-adaptive transverse motion LQR control method for automatically driving automobile | |
| CN111240187B (en) | Vehicle track tracking control algorithm based on vehicle error model | |
| CN111731381B (en) | Vehicle transverse self-adaptive control method and device and vehicle | |
| CN109664884B (en) | Extension self-adaptive lane keeping control method under variable vehicle speed | |
| CN112693449B (en) | Transverse and longitudinal coupling control method under limit working condition of unmanned vehicle | |
| CN112733270A (en) | System and method for predicting vehicle running track and evaluating risk degree of track deviation | |
| CN112622903A (en) | Longitudinal and transverse control method for autonomous vehicle in vehicle following driving environment | |
| CN110262229B (en) | Vehicle self-adaptive path tracking method based on MPC | |
| CN110687797B (en) | Self-adaptive MPC parking transverse control method based on position and posture | |
| CN113635920B (en) | Weight self-adaptive transverse and longitudinal coupling tracking control method and system | |
| CN113625702B (en) | Unmanned vehicle simultaneous path tracking and obstacle avoidance method based on quadratic programming | |
| CN112622895B (en) | Prediction control method applied to trajectory control of automatic driving | |
| CN113608530A (en) | Parameter self-tuning LQR (Low-rank Quadrature) path tracking method with PID (proportion integration differentiation) corner compensation | |
| CN112109732A (en) | Intelligent driving self-adaptive curve pre-aiming method | |
| CN110428603A (en) | Following Car travel control method and device in container truck formation | |
| Yuan et al. | Research on model predictive control-based trajectory tracking for unmanned vehicles | |
| CN112462777A (en) | Ship formation path active coordination system and method considering maneuverability difference | |
| Wang et al. | Path tracking control for autonomous harvesting robots based on improved double arc path planning algorithm | |
| CN109606364B (en) | Layered self-learning extensible neural network lane keeping control method | |
| CN113985895A (en) | AGV path tracking method based on optimization | |
| CN115167135A (en) | Feedback and model feedforward cascade unmanned vehicle self-tendency optimal position and posture control system | |
| CN116540527B (en) | Mining truck model prediction speed change track tracking control method | |
| CN116691668A (en) | Transverse control method for large intelligent vehicle | |
| CN113671950B (en) | Vehicle track tracking control method based on pose convergence algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |