CN115586770A - Robot path planning method for deploying ROS based on Loongson embedded system - Google Patents
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Abstract
The invention discloses a robot path planning method for deploying ROS based on a Loongson embedded system, which comprises the following steps: performing ROS source code compiling and mounting on the Loongson chip; establishing a linear track model according to the non-omnidirectional motion characteristic of the robot; establishing a speed search space according to four constraints of a planned track, a distance and an angle between the planned track and an obstacle and an acceleration range, and primarily establishing a sampling track in the speed search space; calculating a cost function of the robot path planning by sampling the speed and adding an acceleration function; selecting an optimal track within a time interval t, and moving the robot at a sampling speed corresponding to the optimal track
A period of time; and repeating the steps until the distance between the robot and the target point is smaller than a set value, and stopping searching. The invention effectively solves the problem that the robot collides with the obstacle when the obstacle is avoided by the existing path planning algorithm, improves the accuracy of path planning and obstacle avoidance,the development of domestic chips in the field of mobile robots is promoted.
Description
Technical Field
The invention belongs to the technical field of mobile robots, relates to mobile robot path planning, and particularly relates to a robot path planning method for deploying ROS based on a Loongson embedded system.
Background
The ROS is an open source software framework for implementing robot programming and developing complex robot applications, and, like all operating systems, provides a hardware abstraction layer for building robot applications. With its existence, developers do not need to consider differences in the underlying hardware.
In the ROS, a dynamic window algorithm is used for path planning and obstacle avoidance by default, but when a mobile robot uses the dynamic window algorithm for obstacle avoidance, the problem of collision with obstacles usually occurs, and under the situation that scientific and technological competition is more intense, the performance of a domestic chip still has certain problems compared with the performance of a foreign chip applied to the direction of robot navigation and path planning, so that the robot path planning method based on the godson embedded system for deploying the ROS is provided for solving the existing problems of the dynamic window algorithm and promoting the development of the mobile robot based on the domestic chip, and a foundation is laid for the domestic application in the field of robots.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problem that a robot collides with an obstacle when the obstacle is avoided by using an original path planning algorithm in the prior art, the robot path planning method for deploying the ROS based on the godson embedded system is provided, so that the limitation of the existing path planning algorithm is solved, and the accuracy of path planning and obstacle avoidance is improved.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a robot path planning method for deploying ROS based on a loongson embedded system, comprising the following steps:
s1: performing ROS source code compiling and mounting on the Loongson chip;
s2: establishing a linear track model according to the non-omnidirectional motion characteristic of the robot;
s3: establishing a speed search space according to four constraints of a planned track, a distance and an angle between the planned track and an obstacle and an acceleration range, and primarily establishing a sampling track in the speed search space;
s4: on the basis of the preliminarily established sampling track, calculating a cost function of the robot path planning through sampling speed and adding an acceleration function;
s5: based on the cost function of the robot path planning, the optimal track in the time interval t is selected, and the robot is moved at the sampling speed corresponding to the optimal track
A period of time;
s6: and repeating the steps S3 to S5 until the distance between the robot and the target point is less than a set value, stopping searching and obtaining the optimal track.
Further, the step S1 specifically includes:
a1: installation of ROS dependence;
a2: configuring the rossdep by using the rossdep update;
a3: creating a catkin working space;
a4: downloading an ROS-Comm source code;
a5: using rossdep to install the dependency needed in the process of compiling the source code;
a6: use of/src/cat/bin/cat _ make _ isolated- -install
-DCMAKE _ BUILD _ TYPE = Release compiled source code;
a7: writing the path into a terminal start script so as to use an ROS interface in the terminal;
a8: and the ROS is installed on the Loongson chip.
Further, the establishing of the linear trajectory model in step S2 includes the following steps:
b1: in the very short time of the robot motion, the track is regarded as uniform linear motion;
b2: because the robot can only advance and rotate, the advancing speed direction is consistent with the course angle of the robot, meanwhile, the control variable is the acceleration for controlling the advancing and the angular acceleration for controlling the steering, a linear track model is established, and a motion difference equation is as follows:
wherein Δ t is a time variation, x (t) denotes a displacement of the robot in the forward direction at time t, x (t + Δ t) denotes a displacement of the robot in the forward direction at time t + Δ t, y (t) denotes a displacement of the robot in the direction perpendicular to x (t) at time t, y (t + Δ t) denotes a displacement of the robot in the direction perpendicular to x (t + Δ t) at time t + Δ t, θ (t) denotes an angle between the robot and a reference line at time t, θ (t + Δ t) denotes an angle between the robot and the reference line at time t + Δ t, v (t) denotes a velocity of the robot at time t, v (t + Δ t) denotes a velocity of the robot at time t, ω (t) denotes an angular velocity of the robot at time t + Δ t, ω (t + Δ t) denotes an angular velocity of the robot at time t, and α (t) denotes an acceleration of the robot at time t.
Further, the establishing of the speed search space in the step S3 includes the following steps:
c1: the track is mainly a circular arc, and is determined by sampling speeds (v, ω) which are required to be within a feasible range, and the speeds form an initial speed search space:
V s ＝{(v,ω)|v≤|v max |∩ω≤|ω max ||}
wherein, V s Searching space for initial speed, (v, omega) is sampling speed, v is robot speed, | v max L is the maximum speed of the robot, omega is the angular speed of the robot, and | omega max L is the maximum angular velocity of the robot;
c2: setting a real-time updated maximum speed limit to ensure that the vehicle can stop before the nearest obstacle under the maximum acceleration:
wherein, V α1 Search space for velocity under the limit of distance from obstacle, | α max | is the maximum acceleration, | ω max L is the maximum angular acceleration, dist (v, ω) is the distance between the robot trajectory and the nearest obstacle;
c3: meanwhile, the real-time updated maximum angular speed limit is set, and the vehicle can stop before the obstacle closest to the course angle under the maximum angular acceleration:
wherein, V α2 Searching a space for the speed under the angle limit with the obstacle, wherein the heading (v, omega) is the angle of the course angle deviation of the robot and the included angle of the obstacle;
c4: since there is a range limit to the acceleration, the speed that can be reached with maximum acceleration or maximum acceleration in the negative direction for a certain time is retained:
V d ＝{(v,ω)|v∈[v 0 -α max *Δt,v 0 +α max *Δt]∩ω∈[ω 0 -α max *Δt,ω 0 +α max *Δt]|}
wherein, V d Search space for velocity under acceleration limit, v 0 Is the current robot speed, omega 0 The current angular velocity of the robot;
c5: according to the steps C1-C4, the speed search space is as follows:
V δ ＝V s ∩V α1 ∩V α2 ∩V d
wherein, V δ The space is searched for the final velocity. In the obtained speed search space, a plurality of sampling tracks are preliminarily established. The robot will eventually follow a certain sampling trajectory.
Further, the step S4 specifically includes the following steps:
d1: adding a course angle and an angle deviation between an actual robot and an obstacle connecting line, correcting the course of the robot, and using a cosine function or an inverse trigonometric function during calculation;
d2: and setting a maximum distance value during calculation, such as: 0.25 m, when the distance maximum value is exceeded, dist (v, omega) is constantly equal to the maximum value, so that the distance between the robot and the obstacle is kept, and the situation that the robot is farther away from the obstacle is better is prevented;
d3: on the basis of preliminary establishment of a sampling track, establishing a cost function:
G(v,ω)＝σ(a*heading(v,ω)+β*dist(v,ω)+γ*vel(v,ω)+ε*acc(v,ω))
g (v, omega) is a cost function, a, beta, gamma and epsilon parameters represent weights, different weights are needed in different environments, dimension is eliminated by adopting weight normalization, sigma parameters enable the weights of the four parts to be smoother, a certain gap is kept between a track and an obstacle, vel (v, omega) is a speed function, and acc (v, omega) is an acceleration function.
Further, in the step S5, an optimal trajectory within the time interval t is selected, that is, a path that can bypass and retain a certain stability margin when the robot moves to the target point and meets an obstacle, and the robot is moved by (v, ω) corresponding to the optimal trajectory
The period n is a positive integer, such as 10 or 20, set by itself.
Has the advantages that: compared with the prior art, aiming at the abnormal phenomenon that the robot directly impacts an obstacle and the terminal outputs a report error when the robot avoids the obstacle due to inaccurate path planning of the original algorithm, the method establishes the optimal track during path planning by establishing more speed search spaces compared with the constraint condition of the original path planning algorithm, adding the angle of the course angle of the robot deviating from the actual connection direction of the robot and the obstacle and adding an acceleration function when establishing a cost function, and the optimal path keeps a stable margin, so that the robot can safely and quickly pass when encountering the obstacle, effectively solves the problem that the robot impacts the obstacle when the obstacle is avoided by the existing path planning algorithm, improves the accuracy of path planning and obstacle avoidance, promotes the development of a domestic chip in the field of mobile robots, and has better practical significance and practical value.
Drawings
FIG. 1 is a schematic workflow of the process of the present invention;
FIG. 2 is a diagram of a robot according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a coordinate system of a robot provided by an embodiment of the invention;
FIG. 4 is an exemplary diagram of a heading angle of a robot according to an embodiment of the invention;
FIG. 5 is a diagram of a planned path of a robot when not optimized according to an embodiment of the present invention;
fig. 6 is a diagram of a robot planning path after optimization according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following detailed description in conjunction with the accompanying drawings, it is to be understood that such embodiments are merely illustrative of and not restrictive on the broad invention, and that various equivalent modifications of the invention may occur to those skilled in the art upon reading the appended claims.
The invention provides a robot path planning method for deploying ROS based on a Loongson embedded system, which comprises the following steps as shown in figure 1:
s1: the method comprises the following steps of A1-A8:
a1: installation of ROS dependence;
a2: configuring the rossdep by using the rossdep update;
a3: creating a catkin working space;
a4: downloading an ROS-Comm source code;
a5: using rossdep to install the dependency needed in the process of compiling the source code;
a6: compiling the source code using/src/cat/bin/cat _ make _ isolated-install-DCMAKE _ BUILD _ TYPE = Release;
a7: writing the path into a terminal start script so as to use an ROS interface in the terminal;
a8: and the ROS is mounted on the Loongson chip.
S2: referring to fig. 3, according to the non-omnidirectional movement characteristic of the robot, a linear trajectory model is established:
the establishment of the straight-line track model comprises the following steps B1 and B2:
b1: in the very short time of the robot motion, the track is regarded as uniform linear motion;
b2: as shown in fig. 2, since the robot can only advance and rotate, the advancing speed direction is consistent with the heading angle of the robot, meanwhile, the control variable is the acceleration for controlling the advance and the angular acceleration for controlling the steering, a linear track model is established, and the motion difference equation is as follows:
where Δ t is a time variation, x (t) denotes a displacement of the robot in the forward direction at time t, x (t + Δ t) denotes a displacement of the robot in the forward direction at time t + Δ t, y (t) denotes a displacement of the robot in the direction perpendicular to x (t) at time t, y (t + Δ t) denotes a displacement of the robot in the direction perpendicular to x (t + Δ t) at time t + Δ t, θ (t) denotes an angle between the robot and a reference line at time t, θ (t + Δ t) denotes an angle between the robot and the reference line at time t + Δ t, v (t) denotes a velocity of the robot at time t, v (t + Δ t) denotes a velocity of the robot at time t + Δ t, ω (t) denotes an angular velocity of the robot at time t + Δ t, ω (t + Δ t) denotes an angular velocity of the robot at time t, and α (t) denotes an acceleration of the robot at time t.
S3: establishing a speed search space according to four constraints of a planned track, a distance and an angle with an obstacle and an acceleration range, preliminarily establishing a sampling track in the speed search space, and newly adding a constraint relation to enable the establishment of the speed search space to be more perfect, wherein the method specifically comprises the following steps of C1-C5:
c1: the trajectory is mainly a circular arc, and is determined by sampling speeds (v, ω) which are required to be within a feasible range, and the speeds form an initial speed search space:
V s ＝{(v,ω)|v≤|v max |∩ω≤|ω max ||}
wherein, V s Searching space for initial speed, (v, omega) is sampling speed, v is robot speed, | v max L is the maximum speed of the robot, omega is the angular speed of the robot, and | omega max L is the maximum angular velocity of the robot;
c2: setting a real-time updated maximum speed limit to ensure that the vehicle can stop before the nearest obstacle under the maximum acceleration:
wherein, V α1 Search space for velocity under the limit of distance from obstacle, | α max L is the maximum acceleration, | ω max L is the maximum angular acceleration, dist (v, ω) is the distance between the robot trajectory and the nearest obstacle;
c3: meanwhile, the real-time updated maximum angular speed limit is set, and the vehicle can stop before the obstacle closest to the course angle under the maximum angular acceleration:
wherein, V α2 Searching a space for the speed under the angle limit with the obstacle, wherein the heading (v, omega) is the angle of the course angle deviation of the robot and the included angle of the obstacle, and the course angle of the robot in the embodiment is shown in FIG. 4;
c4: since there is a range limit to the acceleration, the speed that can be reached with a maximum acceleration or a maximum acceleration in the negative direction for a certain time is retained:
V d ＝{(v,ω)|v∈[v 0 -α max *Δt,v 0 +α max *Δt]∩ω∈[ω 0 -α max *Δt,ω 0 +α max *Δt]|}
wherein, V d Search space for velocity under acceleration limit, v 0 Is the current robot speed, ω 0 Is the current angular velocity of the robot;
c5: according to the steps C1-C4, the speed search space is as follows:
V δ ＝V s ∩V α1 ∩V α2 ∩V d
wherein, V δ The space is searched for the final velocity. In the obtained speed search space, a plurality of sampling tracks are preliminarily established. The robot will eventually follow a certain sampling trajectory.
S4: on the basis of the preliminarily established sampling track, calculating a cost function of robot path planning by sampling speed and adding an acceleration function, so that the cost function is restricted by the acceleration of the robot, and can better bypass an obstacle when avoiding the obstacle, and the method specifically comprises the following steps of D1-D3:
d1: adding a course angle and an angle deviation between an actual robot and an obstacle connecting line, correcting the course of the robot, and using a cosine function or an inverse trigonometric function during calculation;
d2: and setting a maximum distance value during calculation, such as: 0.25 m, when the distance maximum value is exceeded, let dist (v, omega) be constantly equal to the maximum value, so that the distance between the robot and the obstacle is kept, and the situation that the robot is farther away from the obstacle is better is prevented;
d3: on the basis of preliminary establishment of a sampling track, establishing a cost function:
G(v,ω)＝σ(a*heading(v,ω)+β*dist(v,ω)+γ*vel(v,ω)+ε*acc(v,ω))
g (v, omega) is a cost function, a, beta, gamma and epsilon parameters represent weights, different weights are needed in different environments, dimension is eliminated by adopting weight normalization, sigma parameters enable the weights of the four parts to be smoother, a certain gap is kept between a track and an obstacle, vel (v, omega) is a speed function, and acc (v, omega) is an acceleration function.
S5: selecting an optimal track within the time interval t, namely a path which can bypass and reserve a certain stability margin when the robot meets an obstacle when moving to a target point, and moving the robot by the optimal track corresponding to the (v, omega)
The period n is a positive integer set by itself, such as 10 or 20.
S6: and (5) repeating the steps S3-S5 until the distance between the robot and the target point is less than 0.2 m, stopping searching and obtaining the final planned path.
Based on the above scheme, in order to verify the effect of the method of the present invention, the optimization method of the present invention and the existing unoptimized path planning method are applied simultaneously, specifically as follows:
fig. 5 is an existing route planning method which is not optimized, and it can be found from the diagram that a yellow route planned by the robot has diverged to an obstacle and intersected with the obstacle, so that the robot may collide with the obstacle in an actual obstacle avoidance process, resulting in a wrong route planning.
Fig. 6 is a planned path of the robot optimized by the method of the present invention, and it can be seen from the figure that the optimized path does not intersect with the obstacle and does not collide with the obstacle any more.
As can be seen from fig. 5 and 6, the method of the invention effectively solves the problem that the robot collides with the obstacle when the obstacle is avoided by using the existing path planning algorithm, and improves the accuracy of path planning and obstacle avoidance.
Claims (6)
1. The robot path planning method for deploying the ROS based on the Loongson embedded system is characterized by comprising the following steps:
s1: performing ROS source code compiling and mounting on the Loongson chip;
s2: establishing a linear track model according to the non-omnidirectional motion characteristic of the robot;
s3: establishing a speed search space according to four constraints of a planned track, a distance and an angle between the planned track and an obstacle and an acceleration range, and primarily establishing a sampling track in the speed search space;
s4: on the basis of the preliminarily established sampling track, calculating a cost function of the robot path planning through sampling speed and adding an acceleration function;
s5: based on the cost function of the robot path planning, the optimal track in the time interval t is selected, and the robot is moved at the sampling speed corresponding to the optimal track
A period of time;
s6: and repeating the steps S3-S5 until the distance between the robot and the target point is less than a set value, stopping searching and obtaining the optimal track.
2. The method for planning a robot path based on ROS deployed by a Loongson embedded system according to claim 1, wherein the step S1 specifically comprises:
a1: installation of ROS dependence;
a2: configuring the rossdep by using the rossdep update;
a3: creating a catkin working space;
a4: downloading an ROS-Comm source code;
a5: using rossdep to install the dependency needed in the process of compiling the source code;
a6: compiling the source code;
a7: writing the path into a terminal start script so as to use an ROS interface in the terminal;
a8: and the ROS is mounted on the Loongson chip.
3. The method for planning the route of the robot deploying the ROS based on the Loongson embedded system according to claim 1, wherein the building of the straight track model in the step S2 comprises the following steps:
b1: in the very short time of the robot motion, the track is regarded as uniform linear motion;
b2: because the robot can only advance and rotate, the advancing speed direction is consistent with the course angle of the robot, meanwhile, the control variable is the acceleration for controlling the advancing and the angular acceleration for controlling the steering, a linear track model is established, and the motion difference equation is as follows:
where Δ t is a time variation, x (t) denotes a displacement of the robot in the forward direction at time t, x (t + Δ t) denotes a displacement of the robot in the forward direction at time t + Δ t, y (t) denotes a displacement of the robot in the direction perpendicular to x (t) at time t, y (t + Δ t) denotes a displacement of the robot in the direction perpendicular to x (t + Δ t) at time t + Δ t, θ (t) denotes an angle between the robot and a reference line at time t, θ (t + Δ t) denotes an angle between the robot and the reference line at time t + Δ t, v (t) denotes a velocity of the robot at time t, v (t + Δ t) denotes a velocity of the robot at time t + Δ t, ω (t) denotes an angular velocity of the robot at time t + Δ t, ω (t + Δ t) denotes an angular velocity of the robot at time t, and α (t) denotes an acceleration of the robot at time t.
4. The method for robot path planning based on ROS deployment by a Loongson embedded system in claim 3, wherein the establishment of the speed search space in the step S3 comprises the following steps:
c1: the trajectory is mainly a circular arc, and is determined by sampling speeds (v, ω) which are required to be within a feasible range, and the speeds form an initial speed search space:
V s ＝{(v,ω)|v≤|v max |∩ω≤|ω max ||}
wherein, V s Searching space for initial speed, (v, omega) is sampling speed, v is robot speed, | v max L is the maximum speed of the robot, omega is the angular speed of the robot, and | omega max L is the maximum angular velocity of the robot;
c2: setting a real-time updated maximum speed limit to ensure that the vehicle can stop before the nearest obstacle under the maximum acceleration:
wherein, V α1 Search space for velocity under the limit of distance from obstacle, | α max | is the maximum acceleration, | ω max L is the maximum angular acceleration, dist (v, ω) is the distance between the robot trajectory and the nearest obstacle;
c3: meanwhile, the maximum angular speed limit updated in real time is set, and the vehicle can stop in front of the obstacle closest to the course angle under the maximum angular acceleration:
wherein, V α2 Searching a space for the speed under the angle limit with the obstacle, wherein heading (v, omega) is the angle of the course angle deviation of the robot and the included angle of the obstacle;
c4: since there is a range limit to the acceleration, the speed that can be reached with a maximum acceleration or a maximum acceleration in the negative direction for a certain time is retained:
V d ＝{(v,ω)|v∈[v 0 -α max *Δt,v 0 +α max *Δt]∩ω∈[ω 0 -α max *Δt,ω 0 +α max *Δt]|}
wherein, V d Search space for velocity under acceleration limit, v 0 Is the current robot speed, omega 0 Is the current angular velocity of the robot;
c5: according to the steps C1-C4, the speed search space is as follows:
V δ ＝V s ∩V α1 ∩V α2 ∩V d
wherein, V δ The space is searched for the final velocity.
5. The method for robot path planning based on loongson embedded system deployment ROS of claim 1, wherein the step S4 specifically comprises the following steps:
d1: adding a course angle and an angle deviation between an actual robot and an obstacle connecting line, correcting the course of the robot, and using a cosine function or an inverse trigonometric function during calculation;
d2: setting a maximum distance value during calculation, and making dist (v, omega) be equal to the maximum distance value when the maximum distance value is exceeded;
d3: on the basis of preliminary establishment of a sampling track, establishing a cost function:
G(v,ω)＝σ(a*heading(v,ω)+β*dist(v,ω)+γ*vel(v,ω)+ε*acc(v,ω))
g (v, omega) is a cost function, a, beta, gamma and epsilon parameters represent weights, the sigma parameter enables the weights of the four parts to be smoother, a certain gap is kept between a track and an obstacle, vel (v, omega) is a speed function, and acc (v, omega) is an acceleration function.
6. The method for planning the path of the robot deploying the ROS based on the Loongson embedded system of claim 1, wherein an optimal trajectory within the time interval t, that is, a path that the robot can bypass and reserve a certain stability margin when encountering an obstacle when moving to a target point is selected in step S5, and the robot is moved (v, ω) corresponding to the optimal trajectory
And n is a set positive integer.
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