CN115202338A - Collaborative motion control of multiple intelligent vehicles for communication, navigation and obstacle avoidance based on ROS - Google Patents
Collaborative motion control of multiple intelligent vehicles for communication, navigation and obstacle avoidance based on ROS Download PDFInfo
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- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0238—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors
- G05D1/024—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using obstacle or wall sensors in combination with a laser
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
- G05D1/0251—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision
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Abstract
The invention provides a cooperative motion control algorithm among multiple intelligent vehicles for communication, navigation and obstacle avoidance based on a Robot Operating System (ROS for short). All intelligent vehicles can process road condition information through the binocular camera module to obtain a data set formed by road condition pictures, and transmit the data set to a jetson TX2 main board in a python code mode, and the jetson TX2 main board carries out Simultaneous Localization and Mapping, namely SLAM (positioning and map building) environment building for short. When a navigator intelligent vehicle (called a navigator for short) encounters an obstacle, the main board receives abnormal data set information, an obstacle avoidance instruction is sent to the main controller through a python code in the ROS node, and the intelligent vehicle controller receives the instruction to change the rotating speed of the steering engine and the rotating speed of the double motors, so that obstacle avoidance is completed. Meanwhile, a jetson TX2 main board of a follower intelligent vehicle (called a follower for short) subscribes to speed and pose information of a pilot through a TCP/IP protocol, the follower calculates the rotating speeds of a motor and a steering engine through a cooperative motion control algorithm to realize cooperative motion, and an obstacle and the pilot are distinguished according to the characteristics of the pilot to realize obstacle avoidance. The key point of the invention is to design an intelligent vehicle obstacle avoidance algorithm and a cooperative motion control algorithm of multiple intelligent vehicles, which can be used for realizing obstacle avoidance and cooperative motion control of a military small unmanned chariot.
Description
Technical Field
The invention relates to the technical field of intelligent vehicles, in particular to a communication, navigation and obstacle avoidance intelligent vehicle based on a Robot Operating System.
Background
The research of multi-vehicle cooperative motion control becomes the research enthusiasm of the current intelligent traffic system, the related research of the cooperative principle technology is one of the hot and difficult problems of the current development and research, and the multi-vehicle cooperative system has been more and more emphasized because of the incomparable superiority of the single-vehicle system. The multi-vehicle cooperation system is not only simple combination of a single intelligent vehicle, the cooperation process is realized, the fusion of a plurality of subject technologies is involved, and the functions and the characteristics of the multi-vehicle cooperation system are far superior to linear superposition of a single vehicle system.
Compared with a single intelligent vehicle, the multi-vehicle cooperation system has many advantages: and (1) the multi-vehicle cooperative system has higher working efficiency. (2) The multi-vehicle cooperation system can obtain more accurate and rich information. (3) Cooperative members of the multi-vehicle cooperative system cooperate through division of labor, and the robustness of the system is improved to a great extent. And (4) the multi-vehicle cooperative system has high expandability and flexibility.
Disclosure of Invention
The purpose of the invention is: the utility model provides a ROS system intelligent vehicle realizes communication, navigation, the obstacle avoidance and the cooperative motion control between a plurality of intelligent vehicle, further promotes the intellectuality.
In order to solve the technical problems, the ROS system intelligent vehicle comprises a four-wheel trolley, a jetson TX2 main board, a stm32 single chip microcomputer, a motor driving module, two direct current motors of the motor driving module, a steering engine, a laser radar and a binocular camera, wherein the jetson TX2 main board, the stm32 single chip microcomputer, the motor driving module and the binocular camera are mounted on the four-wheel trolley.
The intelligent vehicle chassis of claim 1, wherein: the communication systems of all the intelligent vehicles use a jetson TX2 mainboard carrying a ROS system. Intelligence vehicle chassis control system, its characterized in that: the vision module consists of two cameras, and the operator binocular camera is connected with the jetson TX2 main board through a USB interface. Intelligence vehicle chassis control system, its characterized in that: the SLAM navigation positioning module comprises an RPLIDAR A1 laser radar which is connected with a jetson TX2 main board through a USB interface.
The smart car of claim 1, based on ackermann kinematics analysis, let u 1 、u 2 The rotating speed of the front inner wheel and the rotating speed of the front outer wheel are respectively; u. u 3 、u 4 The rotating speeds of the rear inner wheel and the rear outer wheel are respectively, and u is the current vehicle speed; r 1 、R 2 The front wheel and the rear wheel are respectively vertical to the rotation center of the intelligent vehicle.
The vehicle model determines u 3 +u 4 =2u, obtained by simultaneous equation set,
the pilot obstacle avoidance algorithm of claim 2 as follows:
the length and width of the trolley are set to be l and w (known) in sequence. The vertical distance between the center of mass of the trolley and the obstacle is set as s (known), and the distance between the center of mass of the trolley and the leftmost point of the obstacle is set as m (set by a user). The passing posture of the trolley is that the end point of the barrier is on the vertical line of the intersection line of the front wheel and the rear wheel of the trolley. When the trolley passes through, the distance between the end point of the barrier and the intersection line of the front wheel and the rear wheel of the trolley is named as the anti-collision distance, and is set as y. The vertical distance between the center of mass of the trolley and the obstacle is x. The time required for the steering engine of the trolley to reach the proper turning angle is the steering engine reaction time, and is set as T.
Simultaneous equations, simplified to
In the formula (I), the compound is shown in the specification,
the angle required to be rotated by the steering engine is t, and when the connecting line of the center of mass of the trolley and the end point of the obstacle is vertical to the intersection line of the front wheel and the rear wheel, the time required for the trolley to pass through the obstacle is regarded as the time required by the trolley.
The multi-intelligent-vehicle cooperative motion control, navigator-follower cooperative kinematics algorithm according to claim 3 is characterized by comprising the following steps:
step 1: the method realizes the kinematics analysis of the single intelligent vehicle and adopts the mass center of the intelligent vehicle as the inertial coordinate system O-XYZ of the origin of the coordinate system, and assumes that the wheels meet 'pure rolling and no sliding' in operation, and neglects the longitudinal sliding and the lateral sliding of the wheels, and the mass center of the vehicle body is positioned at the geometric center. Calculating the linear velocity and the angular velocity of the wheel center:
v L ＝ω r ·r (11)
v R ＝ω R ·r (12)
and the calculated linear velocity and centroid angular velocity of the wheels of the intelligent vehicle are as follows:
step 2: the center (namely geometric center) of the central axis of two wheels of the navigator intelligent vehicle and the follower intelligent vehicle is taken as reference, the distance between the two points is L, the deflection angle is phi and is taken as formation parameter of the navigator following method, and the pose relationship between the navigator and an ideal follower is as follows:
Y V ＝Y L +L·sin(φ+θ L ) (16)
and step 3: in the actual cooperative motion process, the follower intelligent vehicle needs to continuously adjust the self pose to the ideal follower pose by applying a motion control instruction. Therefore, the actual pose relationship between the pilot and the follower should be
θ＝θ L -θ F (19)
The ROS-based multi-intelligent-vehicle cooperative motion, pilot-follower cooperative motion control algorithm of claim 1: setting the initial distance and the initial deflection angle between the pilot and the follower as L,
Respectively setting the current distance and deflection angle of a pilot and a follower as L 1 、
The maximum speed of the trolley is u m 。
Shrinking and shrinking the collar at maximum speed for the followerThe time limit T required for the range L of the aircraft,
this time limit is set to 3s for ease of calculation.
(1) When L is 1 When greater than L, the required vehicle speed
Then from the front u and u 3 And u 4 A relationship of (1) can obtain u 3 And u 4 。
(2) When the temperature is higher than the set temperature
Required steering engine speed
Drawings
FIG. 1 is a schematic diagram of a smart vehicle architecture;
FIG. 2 is a schematic diagram of obstacle avoidance for the intelligent vehicle (note: the vehicle in the figure is the intelligent vehicle);
FIG. 3 is an environmental awareness building of a smart vehicle Simultaneous Localization and Mapping;
fig. 4 is a schematic diagram of obstacle avoidance and cooperative motion control of the intelligent vehicle.
Detailed Description
The technical solution in the examples of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described examples are only some of the embodiments of the invention.
The technical scheme for solving the technical problems is as follows:
the invention aims to provide an intelligent vehicle based on an ROS system, which realizes the communication, navigation, obstacle avoidance and cooperative motion control among a plurality of intelligent vehicles through a binocular camera and a laser radar. Utilize the integration technology of three-dimensional vision principle and laser radar, realize keeping safe distance between the car and the back car can in time adjust speed when appearing the security situation on the highway and realize slowing down and keep away the danger, propose this technical scheme, as shown in fig. 1, include the following step:
step 1, installing a binocular camera and a laser radar. The data are transmitted to a jetson TX2 main board through a USB serial port, the main board fuses and supplements sparse point clouds generated by a stereoscopic vision system and a laser radar respectively to construct a global map, and therefore SLAM (positioning and map construction) of the intelligent vehicle is achieved;
(1) FIG. 2 is a schematic diagram showing the installation positions of a camera and a laser radar on the roof of a vehicle, wherein the laser radar is located in the middle of the roof of the vehicle, and a binocular camera is installed on the head of the vehicle;
(3) Calibrating the binocular cameras to obtain internal parameters fu, fv, u0 and v0 of each camera and rotational and translational parameters R and T between the binocular cameras, and performing distortion correction on the obtained images; acquiring external parameter coefficients RextrnsiCl and textriniCl between a left camera and a laser radar;
(5) Matching the image feature points by using a Hamming distance principle, completing the matching of the left image and the right image feature points at the time t, matching the right image at the time t and the time t-1, matching the right image at the time t-1 and the left image feature points, and matching the left image at the time t-1 and the time t to form annular matching;
calculating three-dimensional coordinates (x, y and z) of the matched feature points according to a stereoscopic vision distance measurement principle, fusing sparse point clouds generated by a binocular camera and sparse point clouds generated by a laser radar together according to external parameter coefficients RextrinsicL and textinsicL of the left camera and the laser radar and external parameter coefficients R and T between the binocular camera, and mutually supplementing the point clouds generated by the laser radar and the point clouds generated by the binocular camera on a far scene to form a global SLAM;
and 2, starting from the set initial motion state by the pilot, constructing SLAM map navigation by the camera and the laser radar, acquiring information of the pilot and extracting the information to generate plane graphic points when the pilot moves to the position near the obstacle, and transmitting the acquired obstacle information and the distance information between the obstacle and the intelligent vehicle to the chassis of the intelligent vehicle through the USB interface.
And 3, after the pilot acquires the distance information of the obstacle, the pilot obtains the steering engine rotation angle required to be adjusted by resolving through the obstacle avoidance algorithm
And issuing an obstacle avoidance instruction to the stm32 single chip microcomputer through the micro usb interface according to the steering engine corner information. And the stm32 single chip microcomputer changes the steering angle of the steering engine to finish obstacle avoidance.
And 4, the pilot and the follower communicate through a jetson TX2 mainboard, when the pilot needs to avoid the obstacle, the follower subscribes to the speed and pose information of the pilot through a TCP/IP communication protocol, and the follower performs real-time calculation through the cooperative motion control algorithm to obtain the rotating speed of the steering engine
And motor speed
And then the jetson TX2 mainboard sends the received speed information to the stm32 single chip microcomputer through a micro usb interface. After the stm32 single chip microcomputer carries out a cooperative motion control algorithm, the rotating speed of a steering engine and the rotating speed of a motor which are required to be corrected by the intelligent vehicle are obtained, the rotating speed of the motor is corrected by the stm32 single chip microcomputer through the motor driving module, the rotating speed of the steering engine is changed, the rotating speed of the steering engine and the rotating speed of the motor are changed, and cooperative motion is completed. The follower distinguishes the barrier from the pilot through the characteristic of the pilot, and the steering engine is adjusted to rotate by the angle
And obstacle avoidance is realized.
Claims (4)
1. The utility model provides a communication, navigation, keep away cooperative motion control of many intelligent cars of barrier based on ROS which characterized in that, includes that the intelligent car keeps away the barrier, many intelligent cars cooperative motion control, many intelligent cars keep away the cooperative motion control behind the barrier.
2. The intelligent vehicle obstacle avoidance algorithm of claim 1, wherein when a navigator encounters an obstacle in front of the obstacle, the length and width of the obstacle are extracted as a planar rectangular end point diagram after the navigator detects the distance to the obstacle. When the obstacle is detected, the trolley starts to avoid the obstacle. At this time, the steering engine needs to rotate by an angle of
The time required for the pilot to pass through the obstacle is
3. The cooperative motion control algorithm of multiple intelligent vehicles according to claim 1, wherein when the speed of a pilot changes or the cooperative following angle and the cooperative following distance change due to road unevenness. The follower can keep the following distance and the following angle relatively unchanged with the pilot, and the cooperative motion control is realized. The follower calls the cooperative motion control algorithm to change the self speed into
The rotating speed of the steering engine required to be adjusted is
4. The cooperative motion control after obstacle avoidance for multiple intelligent vehicles as claimed in claim 1, whenWhen the pilot avoids the obstacle, the follower can adjust the self speed to
A stable following distance and a stable following angle are maintained. The follower distinguishes the barrier from the pilot through the characteristic of the pilot, and the steering engine is adjusted to rotate by the angle
And obstacle avoidance is realized. And the pilot and the follower cooperate to avoid the obstacle and cooperate to move and control.
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| US20210200242A1 (en) * | 2019-12-27 | 2021-07-01 | Automotive Research & Testing Center | Self-driving coordination system and control method thereof |
| CN111813121A (en) * | 2020-07-13 | 2020-10-23 | 陕西理工大学 | Multi-mobile-robot formation obstacle avoidance method based on distance-angle priority |
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