CN116719317A - Unmanned vehicle emergency obstacle avoidance method based on improved model predictive control - Google Patents
Unmanned vehicle emergency obstacle avoidance method based on improved model predictive control Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned vehicles and discloses an unmanned vehicle emergency obstacle avoidance method based on improved model predictive control, wherein an unmanned vehicle sensor acquires surrounding environment information, the surrounding environment information is input into an improved model, measures required to be taken by the unmanned vehicle are calculated, and the unmanned vehicle is controlled to avoid an obstacle according to a calculation result; the state prediction adopts an improved Kalman filtering-based state estimation algorithm to predict the state of the unmanned vehicle, the Kalman filtering algorithm is a common state estimation algorithm, a more accurate state estimation value is obtained through fusion of the prediction and measurement values of the system state, and in the improved Kalman filtering algorithm, an extended Kalman filtering algorithm (EKF) and an unscented Kalman are used for improving a state pre-filtering algorithm (UKF), so that the characteristics and environmental factors of the unmanned vehicle are required to be fully considered, an advanced control algorithm and a sensor technology are combined to achieve a better obstacle avoidance effect, and the unmanned vehicle emergency obstacle avoidance method is more accurate and rapid.
Description
Technical Field
The invention relates to the technical field of unmanned vehicles, in particular to an unmanned vehicle emergency obstacle avoidance method based on improved model predictive control.
Background
The unmanned vehicle has gained wide attention in academia and industry because of the advantages of reducing traffic accidents, casualties, relieving and reducing traffic jams, reducing the energy consumed by users in driving, and the like. The implementation of unmanned vehicles relates to various fields including information and sensing technology, track tracking technology and obstacle avoidance technology. The obstacle avoidance method for the unmanned vehicle has very important significance in various situations. The obstacle avoidance capability is the basis of the unmanned vehicle, and only the unmanned vehicle with good obstacle avoidance capability can really have practicability. The unmanned vehicle obstacle avoidance is realized through a path planning algorithm. Path planning refers to finding a path from a given starting point to a target point so that an unmanned vehicle can safely bypass obstacles in the environment to reach the target point without collision. With the rapid development of technology, unmanned vehicle technology has gradually become a research hotspot. In unmanned vehicle technology, automatic obstacle avoidance is a very important technical problem. At present, various obstacle avoidance algorithms are proposed by many researchers, but the existing algorithms often have the problems of low precision, low response speed and the like. For this purpose, a corresponding technical solution needs to be designed to solve.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an unmanned vehicle emergency obstacle avoidance method based on improved model predictive control, which solves the technical problems of low precision and low response speed existing in the existing algorithm.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: an unmanned vehicle emergency obstacle avoidance method based on improved model predictive control comprises the following steps:
step 1: the unmanned vehicle sensor acquires surrounding environment information, inputs the environment information into the improved model, calculates measures required to be taken by the unmanned vehicle, and controls the unmanned vehicle to avoid the obstacle according to a calculation result;
step 2: state prediction, namely predicting the unmanned vehicle state by adopting an improved Kalman filtering-based state estimation algorithm, wherein the Kalman filtering algorithm is a commonly used state estimation algorithm, and a more accurate state estimation value is obtained through fusion of the prediction and measurement value of the system state;
step 3: the path planning needs to plan a path capable of safely avoiding the obstacle according to the predicted unmanned vehicle state and surrounding environment information, and the path planning algorithm comprises an A-algorithm and a Dijkstra algorithm;
step 4: model predictive control, which is to control the unmanned vehicle to follow a planned path and avoid obstacles, and is based on a model, and a mathematical model of the system is predicted and optimized to obtain a better control strategy, including a linear model predictive control algorithm (LMPC), a nonlinear model predictive control algorithm (NMPC) and the like;
step 5: real-time control, the unmanned vehicle needs to execute a model predictive control algorithm in real time so as to ensure that the unmanned vehicle can still safely and stably avoid obstacles in a complex environment.
Preferably, the improved model includes a state prediction module and a control module.
Preferably, the state prediction module predicts the unmanned vehicle state by adopting an improved state estimation algorithm based on Kalman filtering.
Preferably, the control module controls the unmanned aerial vehicle by using an improved model predictive control algorithm.
Preferably, the model predictive control algorithm includes an objective function, constraints, and predictive model operations.
Preferably, the measures required by the unmanned aerial vehicle include acceleration, deceleration, steering and the like.
Preferably, the controlling the unmanned vehicle to avoid the obstacle includes avoiding collision with the obstacle, avoiding collision with other vehicles, and the like.
Preferably, the environmental information includes a position, a size, a shape, and the like of the obstacle.
Preferably, the unmanned vehicle sensor comprises a laser radar and a camera.
(III) beneficial effects
The unmanned vehicle emergency obstacle avoidance method based on the improved model predictive control is realized by fully considering the characteristics and environmental factors of the unmanned vehicle and combining an advanced control algorithm and a sensor technology to realize a better obstacle avoidance effect, is more accurate and rapid, and has high practical value and wide application prospect.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
The embodiment of the invention provides a technical scheme that: an unmanned vehicle emergency obstacle avoidance method based on improved model predictive control comprises the following steps:
step 1: the unmanned vehicle sensor acquires surrounding environment information, inputs the environment information into the improved model, calculates measures required to be taken by the unmanned vehicle, and controls the unmanned vehicle to avoid the obstacle according to a calculation result;
step 2: state prediction, namely predicting the unmanned vehicle state by adopting an improved Kalman filtering-based state estimation algorithm, wherein the Kalman filtering algorithm is a commonly used state estimation algorithm, and a more accurate state estimation value is obtained through fusion of the prediction and measurement value of the system state;
step 3: the path planning needs to plan a path capable of safely avoiding the obstacle according to the predicted unmanned vehicle state and surrounding environment information, and the path planning algorithm comprises an A-algorithm and a Dijkstra algorithm;
step 4: model predictive control, which is to control the unmanned vehicle to follow a planned path and avoid obstacles, and is based on a model, and a mathematical model of the system is predicted and optimized to obtain a better control strategy, including a linear model predictive control algorithm (LMPC), a nonlinear model predictive control algorithm (NMPC) and the like;
step 5: real-time control, the unmanned vehicle needs to execute a model predictive control algorithm in real time so as to ensure that the unmanned vehicle can still safely and stably avoid obstacles in a complex environment.
Further refinements, the improved model includes a state prediction module and a control module.
By separating state prediction and control, the unmanned vehicle can be better controlled and an obstacle avoidance path can be planned, so that the obstacle avoidance precision and response speed of the unmanned vehicle are improved.
Further improved, the state prediction module predicts the unmanned vehicle state by adopting an improved state estimation algorithm based on Kalman filtering.
The Kalman filtering algorithm is a commonly used state estimation algorithm, the precision and stability of state prediction can be improved, and the improved state estimation algorithm based on Kalman filtering can be adopted to improve the precision and stability of state prediction so as to more accurately plan obstacle avoidance paths.
Further improved, the control module controls the unmanned aerial vehicle by adopting an improved model predictive control algorithm.
The model predictive control algorithm is an advanced control algorithm, a better control strategy can be obtained, and the improved model predictive control algorithm can further improve the obstacle avoidance effect of the unmanned vehicle, so that the unmanned vehicle can be better controlled to carry out obstacle avoidance operation.
Further refinements, the model predictive control algorithm includes an objective function, constraints, and predictive model operations.
The unmanned vehicle can be better controlled to carry out obstacle avoidance operation by a model prediction control algorithm formed by an objective function, constraint conditions, prediction model operation and the like, so that the obstacle avoidance effect is further improved.
Further improvements include acceleration, deceleration, steering, etc. of the unmanned vehicle.
The measures to be taken by the unmanned vehicle, including acceleration, deceleration, steering and the like, are calculated, so that complex road conditions and obstacles can be better dealt with, and the obstacle avoidance effect of the unmanned vehicle is improved.
Further improved, the controlling the unmanned vehicle to avoid the obstacle comprises avoiding collision with the obstacle, avoiding collision with other vehicles, and the like.
The unmanned vehicle is controlled to avoid the obstacle, and the unmanned vehicle is prevented from colliding with obstacles, colliding with other vehicles and the like, so that the safety of the unmanned vehicle and surrounding vehicles can be better guaranteed, and the obstacle avoiding effect of the unmanned vehicle is improved.
Further improved, the environmental information includes the position, size, shape, etc. of the obstacle.
In particular improvement, the unmanned vehicle sensor comprises a laser radar and a camera.
The laser radar, the camera and other sensors can acquire the detailed information of the surrounding environment, including the position, the size, the shape and the like of the obstacle, so that the collision with the obstacle and other vehicles is better avoided, and the obstacle avoidance effect of the unmanned vehicle is improved.
In summary, the characteristics and environmental factors of the unmanned vehicles need to be fully considered, and an advanced control algorithm and a sensor technology are combined to realize a better obstacle avoidance effect, so that the unmanned vehicle emergency obstacle avoidance method is more accurate and rapid, and has high practical value and wide application prospect.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (9)
1. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control is characterized by comprising the following steps of:
step 1: the unmanned vehicle sensor acquires surrounding environment information, inputs the environment information into the improved model, calculates measures required to be taken by the unmanned vehicle, and controls the unmanned vehicle to avoid the obstacle according to a calculation result;
step 2: state prediction, wherein an improved Kalman filtering-based state estimation algorithm is adopted to predict the state of the unmanned vehicle, the Kalman filtering algorithm is a commonly used state estimation algorithm, a more accurate state estimation value is obtained through fusion of the prediction and measurement value of the system state, and in the improved Kalman filtering algorithm, an extended Kalman filtering algorithm (EKF) and unscented Kalman are used to improve a state pre-filtering algorithm (UKF);
step 3: the path planning needs to plan a path capable of safely avoiding the obstacle according to the predicted unmanned vehicle state and surrounding environment information, and the path planning algorithm comprises an A-algorithm and a Dijkstra algorithm;
step 4: model predictive control, which is to control the unmanned vehicle to follow a planned path and avoid obstacles, and is based on a model, and a mathematical model of the system is predicted and optimized to obtain a better control strategy, including a linear model predictive control algorithm (LMPC), a nonlinear model predictive control algorithm (NMPC) and the like;
step 5: real-time control, the unmanned vehicle needs to execute a model predictive control algorithm in real time so as to ensure that the unmanned vehicle can still safely and stably avoid obstacles in a complex environment.
2. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 1, wherein: the improved model includes a state prediction module and a control module.
3. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 2, wherein: the state prediction module predicts the unmanned vehicle state by adopting an improved state estimation algorithm based on Kalman filtering.
4. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 2, wherein: the control module adopts an improved model predictive control algorithm to control the unmanned vehicle.
5. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 4, wherein: the model predictive control algorithm includes an objective function, constraints, and predictive model operations.
6. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 1, wherein: the measures which the unmanned vehicle needs to take are calculated include acceleration, deceleration, steering and the like.
7. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 1, wherein: the control of the unmanned vehicle to avoid the obstacle comprises avoiding collision with the obstacle, avoiding collision with other vehicles and the like.
8. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 1, wherein: the environmental information includes the position, size, shape, etc. of the obstacle.
9. The unmanned vehicle emergency obstacle avoidance method based on improved model predictive control of claim 1, wherein: the unmanned vehicle sensor comprises a laser radar and a camera.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117325879A (en) * | 2023-10-07 | 2024-01-02 | 盐城工学院 | Four-wheel distributed electric drive automobile state evaluation method and system |
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| CN115933648A (en) * | 2022-11-24 | 2023-04-07 | 清华大学深圳国际研究生院 | Robot dynamic obstacle avoidance method and system |
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