CN118034321A - Intelligent vehicle formation driving path planning method based on piloting follower - Google Patents
Intelligent vehicle formation driving path planning method based on piloting follower Download PDFInfo
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Abstract
The invention provides a planning method of an intelligent vehicle formation driving path based on a pilot follower, which relates to the technical field of vehicle formation and comprises the following steps: step 1): determining the positioning and path planning of the intelligent vehicle; step 2): aiming at the requirement of formation control, establishing a pilot-following formation motion system model; step 3): determining a formation transformation obstacle avoidance strategy; step 4): constructing an intelligent vehicle formation experiment platform to complete path planning; the planning method provides guidance for the intelligent vehicle formation driving path, better guides the intelligent vehicle formation driving path and provides method guidance for intelligent vehicle formation driving.
Description
Technical Field
The invention relates to the technical field of vehicle formation, in particular to a planning method of an intelligent vehicle formation driving path based on a pilot follower.
Background
Formation motion control is receiving widespread attention of researchers in various fields as a typical problem in Multi-agent system (Multi-AGENTSYSTEM, MAS) distributed cooperative control. An agent is generally a physical or abstract entity that senses the environment in which it is located and can correctly invoke knowledge of itself to react appropriately to the environment. Multiple agent systems are not strictly defined, and generally refer to a complex system that is composed of multiple agents and their corresponding organization rules and information interaction protocols and is capable of performing specific tasks.
The multi-intelligent vehicle system has the characteristics of high efficiency, strong expansibility and high flexibility, and has obvious advantages in the aspects of material transportation, regional patrol, environment detection and the like. The multi-intelligent vehicle system not only needs to consider the formation of formation when executing tasks, but also needs to consider the obstacle avoidance of dynamic and static obstacles of each intelligent vehicle in the formation driving process, so that the cooperative formation and motion planning are the key for researching the multi-intelligent vehicle system.
Chinese patent CN201710083950.9 discloses an intelligent vehicle formation driving method, comprising: judging the type of vehicles in formation, and registering the vehicle information; the pilot vehicle acquires own vehicle information and sends the own vehicle information to the following vehicle; the following vehicle analyzes the pilot vehicle information autonomous planning path sent by the pilot vehicle, and the following vehicle is controlled; the following vehicle packages and sends the information of the own vehicle to the pilot vehicle; the pilot vehicle analyzes the received following vehicle information and adjusts the self-action. However, the current method for intelligent vehicle formation driving is single, and a perfect driving path method is not formed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a planning method for intelligent vehicle formation driving paths based on pilot followers.
To achieve the above and other objects, the present invention is achieved by comprising the following technical solutions: the invention firstly provides a planning method of an intelligent vehicle formation driving path based on a pilot follower, which comprises the following steps:
Step 1): determining the positioning and path planning of the intelligent vehicle;
step 2): aiming at the requirement of formation control, establishing a pilot-following formation motion system model;
Step 3): determining a formation transformation obstacle avoidance strategy;
Step 4): and (5) building an intelligent vehicle formation experiment platform to complete path planning.
Further, the step 1) specifically comprises the following steps: determining the positioning and path planning of an intelligent vehicle according to a pilot-follow formation control strategy, adopting an odometer, an IMU module and a laser radar sensor for positioning, firstly using an odometer model to complete primary pose prediction, secondly establishing a laser radar movement distortion removal and observation model, and constructing a grid map corresponding to the current environment; the AMCL algorithm is utilized to process the problems of robot kidnapping and particle redundancy so as to realize the global positioning of the intelligent vehicle; for path planning, a grid map under a real physical environment is created, so that an optimized A-based algorithm is adopted to explore a progressive optimal path for pilot intelligent vehicles in formation.
Further, the step 2) specifically comprises the following steps: aiming at the requirement of formation control, a pilot-following formation motion system model is established, and the formation generation and maintenance problems in formation motion are converted into track tracking control problems.
The intelligent vehicle formation control method is based on the pilot-following method to carry out formation control on the intelligent vehicle system, so that pilot vehicles are selected as formation reference points. The formation movement control requires the intelligent garage system to complete formation generation, maintenance and transformation under the constraint of the current working environment, so that the following vehicles are required to reach the expected pose of the formation under the regulation and control of the formation controller within a certain time, the formation synchronization with the piloted vehicles is completed, and the formation movement is realized.
Further, the step 3) specifically comprises the following steps: according to the requirement of formation obstacle avoidance, the constraint of the initial formation and the working environment is considered, and a corresponding formation transformation obstacle avoidance strategy is provided.
Intelligent vehicle formation obstacle avoidance means that the intelligent vehicle formation should maintain a given formation as much as possible and safely avoid obstacles in the environment during the process of moving the intelligent vehicle formation as a whole to a target area. The intelligent vehicle formation needs to make different obstacle avoidance decisions according to different working environments, so as to avoid collision threats from obstacles and other vehicles as much as possible. The method is characterized in that the whole obstacle avoidance is adopted to keep the formation unchanged, and the formation unchanged obstacle avoidance refers to that when a multi-machine formation executes a task, the existing formation is kept unchanged, and the obstacle is avoided by adopting a mode of partial penetration or whole detouring.
Further, the step 4) specifically comprises the following steps: and (3) building an intelligent vehicle formation experiment platform, and performing formation motion control and formation transformation obstacle avoidance experiments in an obstacle environment.
In the operation process of formation experiments, formation of vehicles is generated and maintained, formation isomorphic transformation obstacle avoidance and heterogeneous transformation obstacle avoidance are achieved, formation smoothly passes through an obstacle area, and obstacle avoidance tasks are completed. Therefore, the effectiveness and applicability of the formation control algorithm and the obstacle avoidance strategy provided by the application are fully proved under the actual working environment.
Compared with the prior art, the invention provides a planning method of an intelligent vehicle formation driving path based on a pilot follower, which has the following beneficial effects: the method is based on the intelligent vehicle formation driving path method of the pilot follower, the pilot-following formation control strategy is generalized, the positioning perception of the intelligent vehicle formation is determined, path planning is carried out, a formation control model is established, and a formation obstacle avoidance method is selected. The intelligent vehicle formation driving path guidance method is based on the design of the intelligent vehicle formation driving path of the pilot follower, provides method guidance for the intelligent vehicle formation driving path, better guides the intelligent vehicle formation driving path, further improves the method and provides method guidance for the intelligent vehicle formation driving.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention.
The pilot-following method can be applied to formation control, and the basic idea of pilot-following is to select one from a multi-vehicle system to be a pilot and the other to be a follower. The pilot controls the movement of the entire formation by navigating along a set path. The pilot follower method has the advantages of simple structure and easy realization, and the formation can be realized by only setting the expected path and behavior of the pilot follower in the formation and following vehicles by preset offset.
The invention provides an intelligent vehicle formation driving method based on a pilot follower, which comprises the following steps:
Step 1): determining the positioning and path planning of the intelligent vehicle;
step 2): aiming at the requirement of formation control, establishing a pilot-following formation motion system model;
Step 3): determining a formation transformation obstacle avoidance strategy;
Step 4): and (5) building an intelligent vehicle formation experiment platform to complete path planning.
Wherein the step 1) comprises the following steps: according to a pilot-follow formation control strategy, determining the positioning and path planning of the intelligent vehicle, for positioning, adopting an odometer, an IMU module and a laser radar sensor, firstly using an odometer model to complete primary pose prediction, secondly establishing a laser radar motion distortion removal and observation model, constructing a grid map corresponding to the current environment, and utilizing an AMCL algorithm to process the problems of robot kidnapping and particle redundancy so as to realize the global positioning of the intelligent vehicle. For path planning, a grid map under a real physical environment is created, so that an optimized A-based algorithm is adopted to explore a progressive optimal path for pilot intelligent vehicles in formation.
Further, the step 2) specifically comprises the following steps: aiming at the requirement of formation control, a pilot-following formation motion system model is established, and the formation generation and maintenance problems in formation motion are converted into track tracking control problems.
The intelligent vehicle formation control method is based on the pilot-following method to carry out formation control on the intelligent vehicle system, so that pilot vehicles are selected as formation reference points. The formation movement control requires the intelligent garage system to complete formation generation, maintenance and transformation under the constraint of the current working environment, so that the following vehicles are required to reach the expected pose of the formation under the regulation and control of the formation controller within a certain time, the formation synchronization with the piloted vehicles is completed, and the formation movement is realized.
Further, the step 3) specifically comprises the following steps: according to the requirement of formation obstacle avoidance, the constraint of the initial formation and the working environment is considered, and a corresponding formation transformation obstacle avoidance strategy is provided.
Intelligent vehicle formation obstacle avoidance means that the intelligent vehicle formation should maintain a given formation as much as possible and safely avoid obstacles in the environment during the process of moving the intelligent vehicle formation as a whole to a target area. The intelligent vehicle formation needs to make different obstacle avoidance decisions according to different working environments, so as to avoid collision threats from obstacles and other vehicles as much as possible. The method is characterized in that the whole obstacle avoidance is adopted to keep the formation unchanged, and the formation unchanged obstacle avoidance refers to that when a multi-machine formation executes a task, the existing formation is kept unchanged, and the obstacle is avoided by adopting a mode of partial penetration or whole detouring.
Further, the step 4) specifically comprises the following steps: and (3) building an intelligent vehicle formation experiment platform, and performing formation motion control and formation transformation obstacle avoidance experiments in an obstacle environment.
In the operation process of formation experiments, formation generation and maintenance are completed by the multi-machine formation, formation isomorphic transformation obstacle avoidance and heterogeneous transformation obstacle avoidance are realized, formation smoothly passes through an obstacle area, and an obstacle avoidance task is completed. Therefore, the effectiveness and applicability of the formation control algorithm and the obstacle avoidance strategy provided by the application are fully proved under the actual working environment.
The result shows that the method can provide method guidance for the intelligent vehicle formation driving path based on the pilot follower method and provide a theoretical basis for the practical application of the intelligent formation driving path.
Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value. The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (6)
1. A method for planning a travel path of an intelligent vehicle formation based on a pilot follower, the method comprising the steps of:
Step 1): determining the positioning and path planning of the intelligent vehicle;
step 2): aiming at the requirement of formation control, establishing a pilot-following formation motion system model;
Step 3): determining a formation transformation obstacle avoidance strategy;
Step 4): and (5) building an intelligent vehicle formation experiment platform to complete path planning.
2. The method according to claim 1, characterized in that: in step 1), according to a pilot-follow formation control strategy, determining the positioning and path planning of an intelligent vehicle, wherein the positioning adopts an odometer, an IMU module and a laser radar sensor, firstly, a primary pose prediction is completed by using the odometer model, secondly, a laser radar motion distortion removal and observation model is built, a grid map corresponding to the current environment is built, and the problem of robot kidnapping and particle redundancy is processed by using an AMCL algorithm to realize the global positioning of the intelligent vehicle.
3. The method according to claim 1, characterized in that: in step 1), the path planning is implemented by creating a grid map in a real physical environment, and the path planning adopts an a-algorithm.
4. The method according to claim 1, characterized in that: in the step 2), the formation control needs to build a pilot-following formation motion system model, and the formation generation and maintenance problems in the formation motion are converted into track tracking control problems.
5. The method according to claim 1, characterized in that: in step 3), the obstacle avoidance strategy includes keeping the formation unchanged during overall obstacle avoidance, wherein the formation unchanged during task execution of the multi-machine formation is that the obstacle avoidance strategy is provided according to the requirement of formation obstacle avoidance by adopting a mode of partial penetration or total detouring in order to keep the existing formation unchanged, and the constraint of the initial formation and the working environment is considered.
6. The method according to claim 1, characterized in that: in the step 4), the building of the intelligent vehicle formation experiment platform comprises the steps of performing formation motion control and formation transformation obstacle avoidance experiments in an obstacle environment.
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