CN113867341A - Patrol car path planning and tracking algorithm with high-precision tracking and control - Google Patents
Patrol car path planning and tracking algorithm with high-precision tracking and control Download PDFInfo
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Abstract
The invention provides a patrol car path planning and tracking algorithm with high-precision tracking and control. The method comprises the following steps: step 1, rasterizing the geography of the land where the patrol car is used; step 2, designing a patrol car path planning model; step 3, solving a course angle and a speed of the patrol car according to the kinematics model; step 4, carrying out closed-loop control on the speed, the course angle and the position of the patrol car; and 5, processing the abnormal condition on the patrol route and updating the optimizing path. The invention realizes the high-speed and high-precision patrol car path planning optimization algorithm, improves the patrol car path optimization efficiency, and provides a double-ring series PID closed-loop control system in the control problem of patrol cars, thereby effectively overcoming the non-linear factors and system friction during the movement of the patrol cars.
Description
Technical Field
The invention relates to the field of patrol car tracking, in particular to a patrol car path planning and tracking algorithm with high-precision tracking and control.
Background
With the continuous development of society and the continuous and deep technical research in various fields, various intelligent agent technologies are gradually integrated into the daily work and life of people, help people to release from boring repeated labor, improve the production efficiency and reduce the production cost.
The patrol car is used as the robot which is most widely applied in the building and security industry at present, and has great value in the aspects of safety monitoring, patrol monitoring and the like. The patrol car path planning aims at finding a collision-free optimal path from a starting point to a terminal point by using a proper path planning method, and in a controllable structured factory environment, the patrol car has certain autonomy, can sense the surrounding environment, detect obstacles and plan a collision-free path to navigate to a destination. However, in complex dynamic construction space environments such as mines, rock caves, construction sites and the like, the application of the patrol cars needs to be completely autonomous and is not fully solved, and planning the routes of the patrol cars on a specific floor plan requires information about starting points and end points (namely field channels and target working areas), static obstacles (such as walls and columns) and dynamic dangerous areas (such as materials, workers and equipment working areas). Therefore, it is necessary to implement global path planning and path tracking control of the robot system according to the construction activities, the distribution and the changes of the working space, which are performed at different positions of the site at different time nodes.
Disclosure of Invention
In order to solve the problems, the invention provides a patrol car path planning and tracking algorithm with high-precision tracking and control based on real-time acquired driving field data. A patrol car path planning model is designed for searching an optimal path of a patrol car, meanwhile, in order to improve the accuracy rate of the patrol car in operation, closed-loop control of the speed, the course angle and the position of the patrol car is provided, and the abnormal obstacles are processed by updating the optimal path in real time. The invention provides a patrol car path planning and tracking algorithm with high-precision tracking and control, which comprises the following specific steps:
step 1, rasterizing the geography of the land where the patrol car is used: collecting data of a driving field of the patrol car, rasterizing a field map according to the field data, setting grid blocks to be square, setting grid movement cost in the transverse direction and the longitudinal direction to be 10, setting grid movement cost in the diagonal line to be 14, setting each grid to correspond to a coordinate range in the geography, and simultaneously dividing the grids into an empty area, an area in an obstacle and a target area;
step 2, designing a patrol car path planning model: adding the starting point and the coordinates into the model, simultaneously traversing the grids to search the route of the patrol car, finding out the optimal route with the minimum evaluation function value, and taking the optimal route as the moving route of the patrol car;
step 3, solving the course angle and the speed of the patrol car according to the kinematics model: establishing a kinematics model of the patrol car through the angle, the speed and the acceleration detected by the gyroscope;
step 4, carrying out closed-loop control on the speed, the course angle and the position of the patrol car: designing a speed, a course angle and a position closed-loop control double-loop series PID of the patrol car, firstly designing the speed value and the PID of the course angle of the patrol car to realize the closed-loop control of the speed and the course angle, and simultaneously accessing the position closed loop of the patrol car to finally realize the position tracking of the patrol car;
step 5, abnormal conditions on the patrol route: and if the route has obstacles, the background monitoring software sends the updated grid map to the control software of the patrol car through communication, the control software receives the updated data through communication interruption, and the steps 2 to 4 are repeated according to the updated grid map.
Further, the process of designing the patrol car route planning model in the step 2 can be represented as follows:
step 2.1, adding the starting point of the patrol car into the open list as a current node;
step 2.2, searching nodes adjacent to the current node, putting the reachable nodes into an open list, and setting the current node as a father node of the nodes;
step 2.3, removing the current node from the open list and placing the current node into a closed list, searching and traversing the node with the minimum evaluation function value in the open list, simultaneously placing the node with the minimum evaluation function value into a closed list, repeating the step 2.2 and the step 2.3 until the terminal is placed into the closed list, and stopping optimizing;
the specific formula of the evaluation function f (n) is:
f(n)＝g(n)+h(n)
wherein g (n) is the optimal path value of the moving cost from the nth node to the last node; h (n) is a heuristic value of the optimal path from the starting point to the end point after the nth node is selected.
Further, the process of solving the heading angle and the speed of the patrol car according to the kinematic model in the step 3 can be represented as follows:
setting a kinematics model of the patrol car as follows:
in the formula (I), the compound is shown in the specification,
differential value of the speed of the patrol car,
Is a differential value of the course angle,
Is a differential value of a path transverse coordinate,
Is a differential value of a longitudinal coordinate of the path, a is an acceleration, R is a turning radius,
Is the course angle; and solving the constant integral value of the patrol car through a differential equation:
in the formula, vk+1For the patrol car speed value at k +1,
is the heading angle of the patrol car at k +1, Xk+1For the transverse coordinate of the patrol car on the path k +1, Yk+1For the longitudinal coordinate of the patrol car on the path k +1, vkTo speed the patrol car at k,
is the heading angle of the patrol car at k, XkFor the transverse coordinate of the patrol car on the path k, YkThe longitudinal coordinate of the patrol car on the path k is shown, and t is a time variable; the patrol car sets the driving distance to be delta d and the driving course angle to be delta d between the time k +1 and the time k
Then it can be obtained:
where Δ t is the interval between time k +1 and time k.
Further, the process of the closed-loop control of the speed, the heading angle and the position of the patrol car in the step 4 can be expressed as follows:
in order to improve the control precision of the patrol car and reduce the overshoot of the system, wherein the closed-loop control of the speed, the course angle and the position is realized by adopting integral separation PID control, a logic function is introduced into an algorithm, and the output sampling point value of a regulator is as follows:
wherein u (i) is the output of PID, e (i) is the i time system error, e (i-1) is the i-1 time system error, i is the discretized time variable, Kp，Kl1，KdProportional coefficient, integral coefficient and differential coefficient of PID algorithm, e (j) is system error at time j, A is PID control system threshold, when deviation is large, integral term of PID does not work, when deviation is within threshold, integral algorithm is introduced to control running of patrol car.
The patrol car path planning and tracking algorithm with high-precision tracking and control provided by the invention has the beneficial effects that: the invention has the technical effects that:
1. the method realizes the high-speed and high-precision patrol car path planning and optimizing algorithm, and improves the patrol car path optimizing efficiency;
2. the invention provides a three-closed-loop control system in the aspect of control of the patrol car, which effectively overcomes the non-linear factors and system friction when the patrol car moves;
3. the invention provides an important technical means for a path planning and tracking algorithm of the patrol car.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a dual loop series PID control diagram of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a patrol car path planning and tracking algorithm with high-precision tracking and control, which aims to improve the optimal path selection and tracking precision of a patrol car, solve the optimal path and the tracking stability and accuracy of the patrol car on a gridded map and solve the tracking model of the patrol car on the gridded map, and a flowchart of the invention is shown in figure 1. The steps of the present invention will be described in detail with reference to the flow chart.
Step 1, rasterizing the geography of the land where the patrol car is used: collecting data of a driving field of the patrol car, rasterizing a field map according to the field data, setting grid blocks to be square, setting grid movement cost in the transverse direction and the longitudinal direction to be 10, setting grid movement cost in the diagonal line to be 14, setting each grid to correspond to a coordinate range in the geography, and simultaneously dividing the grids into an empty area, an area in an obstacle and a target area;
step 2, designing a patrol car path planning model: adding the starting point and the coordinates into the model, simultaneously traversing the grids to search the route of the patrol car, finding out the optimal route with the minimum evaluation function value, and taking the optimal route as the moving route of the patrol car;
step 2.1, adding the starting point of the patrol car into the open list as a current node;
step 2.2, searching nodes adjacent to the current node, putting the reachable nodes into an open list, and setting the current node as a father node of the nodes;
step 2.3, removing the current node from the open list and placing the current node into a closed list, searching and traversing the node with the minimum evaluation function value in the open list, simultaneously placing the node with the minimum evaluation function value into a closed list, repeating the step 2.2 and the step 2.3 until the terminal is placed into the closed list, and stopping optimizing;
the specific formula of the evaluation function f (n) is:
f(n)＝g(n)+h(n)
wherein g (n) is the optimal path value of the moving cost from the nth node to the last node; h (n) is a heuristic value of the optimal path from the starting point to the end point after the nth node is selected.
Step 3, solving the course angle and the speed of the patrol car according to the kinematics model: establishing a kinematics model of the patrol car through the angle, the speed and the acceleration detected by the gyroscope;
the patrol car kinematics model can be set as:
in the formula (I), the compound is shown in the specification,
prowl carDifferential value of speed,
Is a differential value of the course angle,
Is a differential value of a path transverse coordinate,
Is a differential value of a longitudinal coordinate of the path, a is an acceleration, R is a turning radius,
Is the course angle; and solving the constant integral value of the patrol car through a differential equation:
in the formula, vk+1For the patrol car speed value at k +1,
is the heading angle of the patrol car at k +1, Xk+1For the transverse coordinate of the patrol car on the path k +1, Yk+1For the longitudinal coordinate of the patrol car on the path k +1, vkTo speed the patrol car at k,
is the heading angle of the patrol car at k, XkFor the transverse coordinate of the patrol car on the path k, YkThe longitudinal coordinate of the patrol car on the path k is shown, and t is a time variable; the patrol car sets the driving distance to be delta d and the driving course angle to be delta d between the time k +1 and the time k
Then it can be obtained:
where Δ t is the interval between time k +1 and time k.
Step 4, carrying out closed-loop control on the speed, the course angle and the position of the patrol car: designing a speed, a course angle and a position closed-loop control double-loop series PID of the patrol car, firstly designing the speed value and the PID of the course angle of the patrol car to realize the closed-loop control of the speed and the course angle, and simultaneously accessing the position closed loop of the patrol car to finally realize the position tracking of the patrol car;
in order to improve the control precision of the patrol car and reduce the overshoot of the system, wherein the closed-loop control of the speed, the course angle and the position is realized by adopting integral separation PID control, a logic function is introduced into an algorithm, and the output sampling point value of a regulator is as follows:
wherein u (i) is the output of PID, e (i) is the i time system error, e (i-1) is the i-1 time system error, i is the discretized time variable, Kp，Kl1，KdProportional coefficient, integral coefficient and differential coefficient of PID algorithm, e (j) is system error at time j, A is PID control system threshold, when deviation is large, integral term of PID does not work, when deviation is within threshold, integral algorithm is introduced to control running of patrol car.
Step 5, abnormal conditions on the patrol route: and if the route has obstacles, the background monitoring software sends the updated grid map to the control software of the patrol car through communication, the control software receives the updated data through communication interruption, and the steps 2 to 4 are repeated according to the updated grid map.
The echo state network training sample data used by the invention is subjected to salt and pepper noise processing to simulate the interference of environmental noise, and simultaneously, the stability and robustness of the echo state network model are improved, so that overfitting of the model is avoided to a certain extent. In addition, when the echo state network output obtained by training has errors, the echo state network model is finely adjusted.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (4)
1. A patrol car path planning and tracking algorithm with high-precision tracking and control comprises the following specific steps:
step 1, rasterizing the geography of the land where the patrol car is used: collecting data of a driving field of the patrol car, rasterizing a field map according to the field data, setting grid blocks to be square, setting grid movement cost in the transverse direction and the longitudinal direction to be 10, setting grid movement cost in the diagonal line to be 14, setting each grid to correspond to a coordinate range in the geography, and simultaneously dividing the grids into an empty area, an area in an obstacle and a target area;
step 2, designing a patrol car path planning model: adding the starting point and the coordinates into the model, simultaneously traversing the grids to search the route of the patrol car, finding out the optimal route with the minimum evaluation function value, and taking the optimal route as the moving route of the patrol car;
step 3, solving the course angle and the speed of the patrol car according to the kinematics model: establishing a kinematics model of the patrol car through the angle, the speed and the acceleration detected by the gyroscope;
step 4, carrying out closed-loop control on the speed, the course angle and the position of the patrol car: designing a speed, a course angle and a position closed-loop control double-loop series PID of the patrol car, firstly designing the speed value and the PID of the course angle of the patrol car to realize the closed-loop control of the speed and the course angle, and simultaneously accessing the position closed loop of the patrol car to finally realize the position tracking of the patrol car;
step 5, abnormal conditions on the patrol route: and if the route has obstacles, the background monitoring software sends the updated grid map to the control software of the patrol car through communication, the control software receives the updated data through communication interruption, and the steps 2 to 4 are repeated according to the updated grid map.
2. A high accuracy tracking and control patrol car path planning and tracking algorithm as claimed in claim 1, wherein: the process of designing the patrol car path planning model in the step 2 can be represented as follows:
step 2.1, adding the starting point of the patrol car into the open list as a current node;
step 2.2, searching nodes adjacent to the current node, putting the reachable nodes into an open list, and setting the current node as a father node of the nodes;
step 2.3, removing the current node from the open list and placing the current node into a closed list, searching and traversing the node with the minimum evaluation function value in the open list, simultaneously placing the node with the minimum evaluation function value into a closed list, repeating the step 2.2 and the step 2.3 until the terminal is placed into the closed list, and stopping optimizing;
the specific formula of the evaluation function f (n) is:
f(n)＝g(n)+h(n)
wherein g (n) is the optimal path value of the moving cost from the nth node to the last node; h (n) is a heuristic value of the optimal path from the starting point to the end point after the nth node is selected.
3. A high accuracy tracking and control patrol car path planning and tracking algorithm as claimed in claim 1, wherein: the process of solving the heading angle and the speed of the patrol car according to the kinematic model in the step 3 can be represented as follows:
setting a kinematics model of the patrol car as follows:
in the formula (I), the compound is shown in the specification,
differential value of the speed of the patrol car,
Is a differential value of the course angle,
Is a differential value of a path transverse coordinate,
Is a differential value of a longitudinal coordinate of the path, a is an acceleration, R is a turning radius,
Is the course angle; and solving the constant integral value of the patrol car through a differential equation:
in the formula, vk+1For the patrol car speed value at k +1,
is the heading angle of the patrol car at k +1, Xk+1For the transverse coordinate of the patrol car on the path k +1, Yk+1For the longitudinal coordinate of the patrol car on the path k +1, vkTo speed the patrol car at k,
is the heading angle of the patrol car at k, XkFor the transverse coordinate of the patrol car on the path k, YkThe longitudinal coordinate of the patrol car on the path k is shown, and t is a time variable; the patrol car sets a line between the time k +1 and the time kThe distance of driving is delta d, and the course angle of driving is
Then it can be obtained:
where Δ t is the interval between time k +1 and time k.
4. A high accuracy tracking and control patrol car path planning and tracking algorithm as claimed in claim 1, wherein: the process of the closed-loop control of the speed, the course angle and the position of the patrol car in the step 4 can be represented as follows:
in order to improve the control precision of the patrol car and reduce the overshoot of the system, wherein the closed-loop control of the speed, the course angle and the position is realized by adopting integral separation PID control, a logic function is introduced into an algorithm, and the output sampling point value of a regulator is as follows:
wherein u (i) is the output of PID, e (i) is the i time system error, e (i-1) is the i-1 time system error, i is the discretized time variable, Kp，Kl1，KdProportional coefficient, integral coefficient and differential coefficient of PID algorithm, e (j) is system error at time j, A is PID control system threshold, when deviation is large, integral term of PID does not work, and deviation is atWhen the threshold is within the threshold, an integral algorithm is introduced to control the running of the patrol car.
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