CN114740871B - Multi-target path re-planning method for unmanned crawler hybrid power platform - Google Patents
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Abstract
The invention relates to a multi-target path re-planning method for an unmanned crawler hybrid power platform, which belongs to the field of navigation path re-planning and comprises the following steps: when the unmanned crawler hybrid power platform is blocked on a road or the driving direction is changed, triggering the platform to replan a global path and a plurality of retreat paths; establishing a path selection model which comprises a path selection model based on the shortest time, a path selection model based on the optimal energy and a path selection model based on the minimum number of gear shifting times; simulating the condition that the platform runs on a plurality of backspacing paths based on the cross-country environment model and the dynamic model; constructing a multi-target path selection model based on an entropy weight method according to simulation data of the path selection model; selecting an optimal backspacing path according to the multi-target path selection model; and after the control platform retreats along the optimal retreating path, the control platform drives to the next task point along the global path again. The method can shorten the time and energy consumed by the platform in running and improve the stability of path re-planning.
Description
Technical Field
The invention relates to the technical field of path re-planning in a navigation process, in particular to a multi-target path re-planning method for an unmanned crawler hybrid power platform.
Background
With the rapid improvement of the artificial intelligence development level and the platform unmanned improvement technology, the onshore unmanned equipment has huge application prospect and research value in the future combat system. Unmanned crawler hybrid platforms are becoming the main warfare equipment and the support force for military combat as an important component of unmanned on land equipment. In the off-road environment, according to the task property requirements, the driving path of the unmanned crawler hybrid power platform generates a topological path which passes through each task point in sequence from a starting point to an end point through global planning, and the platform is guided to safely pass along a set track. However, due to the complexity of the off-road environment, the local planning may have a local minimum or no solution condition, and cannot continue to drive according to the original planned route, and the route needs to be re-planned to bypass the blocked road, and continue to complete the unexecuted task points. Therefore, it is necessary to avoid the impassable area and block the road by the re-planning.
Many relevant researches and theoretical achievements exist in the path re-planning technology of the unmanned crawler hybrid power platform, and currently, commonly used path re-planning methods include a grid method, a sampling search method, an artificial potential field method, an ant colony method, an artificial intelligent neural network method and the like. The prior art proposes a fast re-planning a x algorithm for path planning and re-planning in partially unknown environments. But the road passing time is not considered, and the platform is not researched in a targeted way. The prior art also researches the problem of re-planning of the unmanned aerial vehicle, considers the influence of threat sources of the unmanned aerial vehicle in a three-dimensional topological space, but does not find the unmanned aerial vehicle on the ground track hybrid power platform. Therefore, the problems of platform characteristics, time cost and cruising ability are not considered when a road is blocked or the driving direction is changed in the conventional path re-planning method. Therefore, there is a need in the art to develop a method for re-planning a path of an unmanned crawler hybrid platform, so as to combine the time, energy, and gears that should be selected when different turning radii and speeds are considered, and fully consider the timeliness and economy of the platform while ensuring the dynamic performance of longitudinal movement.
Disclosure of Invention
The invention aims to provide a multi-target path re-planning method for an unmanned crawler hybrid power platform, which aims to solve the problems that the characteristics, time cost and cruising ability of the platform are not considered when the road of the platform is blocked or the driving direction of the platform is changed, and achieve the purposes of shortening the time and energy consumed by the platform in driving and improving the path re-planning stability.
In order to achieve the purpose, the invention provides the following scheme:
a multi-target path re-planning method for an unmanned crawler hybrid power platform comprises the following steps:
establishing a cross-country environment model for driving the unmanned crawler belt hybrid power platform and a dynamic model for driving the unmanned crawler belt hybrid power platform;
when the unmanned crawler hybrid power platform is blocked on a road or the driving direction is changed, triggering the unmanned crawler hybrid power platform to re-plan a global path and a plurality of backspacing paths capable of going to a next task point along the global path;
establishing a path selection model; the route selection model comprises a shortest time-based route selection model, an optimal energy-based route selection model and a least shift-time-based route selection model;
simulating the driving condition of the unmanned crawler hybrid power platform on the plurality of backspacing paths based on the cross-country environment model and the dynamic model, and respectively obtaining simulation data of the path selection model;
constructing a multi-target path selection model based on an entropy weight method according to the simulation data of the path selection model;
selecting an optimal backspacing path from the plurality of backspacing paths according to the multi-target path selection model;
and controlling the unmanned crawler hybrid power platform to retreat along the optimal retreat path and then travel to the next task point along the global path again.
Optionally, the establishing a dynamic model of the unmanned crawler hybrid power platform includes:
establishing a driving dynamic model of the unmanned crawler belt hybrid power platform
(ii) a Wherein
And
the left motor output torque and the right motor output torque of the unmanned crawler belt hybrid power platform are respectively;
the transmission ratio of the gear is set;
the transmission ratio of the main speed reducers on two sides is adopted;
the radius of rotation of the driving wheel;
mass of the unmanned crawler hybrid power platform;
is a universal gravitation constant;
，
in the case of a slope of a ramp,
is the rolling resistance coefficient;
the longitudinal movement speed of the unmanned crawler hybrid power platform is obtained;
is a rotational mass conversion factor.
Optionally, when the unmanned crawler hybrid power platform is blocked on a road or the driving direction of the unmanned crawler hybrid power platform is changed, triggering the unmanned crawler hybrid power platform to re-plan a global path includes:
when the unmanned crawler hybrid power platform runs normally, the length of the front path is judged
Whether or not it is less than a passable threshold
Obtaining a first judgment result;
if the first judgment result is
<
And the unmanned crawler belt hybrid power platform backs a car
Rice waiting
Second later, the front path length is judged again
Whether or not less than
+
Obtaining a second judgment result;
if the second judgment result is
<
+
Determining that the road is blocked, and triggering the unmanned crawler hybrid power platform to re-plan a global path at the moment;
if the first judgment result is
≥
Or the second judgment result is
≥
+
Judging whether a specific target object exists in front or not to obtain a third judgment result;
if the third judgment result shows that a specific target object exists in the front, determining that the driving direction needs to be changed, and triggering the unmanned crawler hybrid power platform to re-plan a global path;
if the third judgment result shows that no specific target object exists in the front, returning to the unmanned crawler hybrid power platform to normally drive, and judging the length of a front path
Whether or not it is less than a passable threshold
The step (2).
Optionally, the establishing a path selection model specifically includes:
establishing a time-based shortest path selection model
(ii) a Wherein the content of the first and second substances,
is a set of re-planning times;
is as follows
The path selection model corresponding to the secondary re-planning and based on the shortest time is planned;
、
the longitudinal average speeds of the unmanned crawler hybrid power platform in the forward and backward states are respectively;
is as follows
The normal distance of passage of the sub-planned platform,
is as follows
Normal passage time of the secondary re-planned platform;
is as follows
Parking waiting time when the secondary re-planning is confirmed;
is a first
The reverse distance of the platform which is re-planned again,
is a first
The time required for the platform to run back at the turnout junction during secondary re-planning;
establishing a path selection model based on energy optimization
(ii) a Wherein the content of the first and second substances,
is a first
The path selection model based on the optimal energy corresponding to the secondary re-planning;
to
Is that the platform goes from the previous task point to the first
The driving time before the secondary re-planning;
to
Is entered into
Planning the running time from the second re-planning to the end of the backward running;
to
Is the first
The secondary re-planning platform starts to drive to the next target point in the forward direction;
establishing a path selection model based on least number of shifts
(ii) a Wherein the content of the first and second substances,
is as follows
The path selection model which corresponds to the secondary re-planning and is based on the minimum number of gear shifting times is planned;
from the previous task point to the first for the platform
The gear shifting times in the driving process before the secondary rescheduling;
is entered into
Re-planning the gear shifting times in the running process of the platform from the second re-planning to the end of the running;
is the first
And (5) re-planning the gear shifting times of the platform in the process of forward driving to the next target point.
Optionally, the constructing a multi-objective path selection model based on an entropy weight method according to the simulation data of the path selection model specifically includes:
are respectively to the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the minimum number of shifts
To (1) a
Secondary analog data
By the formula
Carrying out standardization processing to obtain standardized data
；
According to the standardized data
By the formula
Calculate the first
Entropy weighting of secondary analog data
(ii) a Wherein
Re-planning times;
according to the entropy weight
By the formula
Determining importance ratio of adjacent sub-simulation data
；
According to the importance ratio
By the formula
Computing weights for a routing model
；
According to the weight
Constructing a multi-objective path selection model
(ii) a Wherein the content of the first and second substances,
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the minimum number of shifts
The weight of (c);
are respectively the first
The shortest time-based path selection during secondary re-planningModel selection
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
A calculated scalar value.
A multi-target path re-planning system for an unmanned crawler hybrid power platform comprises:
the physical model establishing module is used for establishing a cross-country environment model for driving the unmanned crawler hybrid power platform and a dynamic model for driving the unmanned crawler hybrid power platform;
the rescheduling triggering module is used for triggering the unmanned crawler hybrid power platform to reschedule a global path and a plurality of backspacing paths which can go to a next task point along the global path when the unmanned crawler hybrid power platform is blocked or the driving direction is changed on a road;
the path selection model establishing module is used for establishing a path selection model; the route selection model comprises a shortest time-based route selection model, an optimal energy-based route selection model and a least shift-time-based route selection model;
the platform running simulation module is used for simulating the running condition of the unmanned crawler hybrid power platform on the plurality of backspacing paths based on the off-road environment model and the dynamic model, and respectively obtaining simulation data of the path selection model;
the multi-target path selection model building module is used for building a multi-target path selection model based on an entropy weight method according to the simulation data of the path selection model;
the optimal backspacing path selection module is used for selecting an optimal backspacing path from the plurality of backspacing paths according to the multi-target path selection model;
and the platform running control module is used for controlling the unmanned crawler hybrid power platform to run to the next task point along the global path again after returning along the optimal returning path.
Optionally, the physical model building module specifically includes:
a dynamic model establishing unit for establishing a dynamic model of the unmanned crawler belt hybrid power platform in running
(ii) a Wherein
And
the left motor output torque and the right motor output torque of the unmanned crawler belt hybrid power platform are respectively;
the transmission ratio of the gear is set;
the transmission ratio of the main reducers on two sides is adopted;
the radius of the driving wheel is the rotation radius;
the mass of the unmanned crawler hybrid power platform;
is a universal gravitation constant;
，
in the case of a slope of a ramp,
is the rolling resistance coefficient;
the longitudinal movement speed of the unmanned crawler hybrid power platform is obtained;
is a rotating mass scaling factor.
Optionally, the replanning triggering module specifically includes:
a first judging unit for judging the front path length when the unmanned crawler hybrid power platform normally runs
Whether or not it is less than a passable threshold
Obtaining a first judgment result;
a second judgment unit for judging if the first judgment result is
<
And the unmanned crawler belt hybrid power platform backs a car
Rice waiting
Second later, the front path length is judged again
Whether or not less than
+
Obtaining a second judgment result;
a first re-planning triggering unit for judging if the second judgment result is
<
+
Determining that the road is blocked, and triggering the unmanned crawler hybrid power platform to re-plan a global path at the moment;
a third judging unit for judging if the first judgment result is
≥
Or the second judgment result is
≥
+
Judging whether a specific target object exists in front or not to obtain a third judgment result;
the second replanning triggering unit is used for determining that the driving direction needs to be changed if the third judgment result shows that a specific target object exists in the front, and triggering the unmanned crawler hybrid power platform to replan a global path at the moment;
and the circulation judging unit is used for returning to the first judging unit if the third judging result shows that no specific target object exists in the front.
Optionally, the path selection model establishing module specifically includes:
a first path selection model establishing unit for establishing a path selection model based on the shortest time
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
is a set of rescheduled times;
is as follows
The path selection model corresponding to the secondary re-planning and based on the shortest time is planned;
、
the longitudinal average speeds of the unmanned crawler hybrid power platform in the forward and backward states are respectively;
is a first
The normal distance of passage of the sub-planned platform,
is as follows
Normal passage time of the secondary re-planned platform;
is a first
Parking waiting time when the secondary re-planning is confirmed;
is as follows
The reverse distance of the platform which is re-planned,
is as follows
The time required for the platform to run back at the turnout junction during secondary re-planning is shortened;
a second path selection model establishing unit for establishing a path selection model based on energy optimization
(ii) a Wherein the content of the first and second substances,
is as follows
The path selection model based on the optimal energy corresponding to the secondary re-planning;
to
Is that the platform goes from the previous task point to the first
The driving time before the secondary re-planning;
to is that
Is entered into
Planning the running time from the secondary re-planning to the end of the backward running;
to
Is the first
The secondary re-planning platform starts to drive to the next target point in the forward direction;
a third path selection model creation unit for creating a path selection model based on the fewest number of shifts
(ii) a Wherein the content of the first and second substances,
is a first
The path selection model which corresponds to the secondary re-planning and is based on the minimum number of gear shifting times is planned;
from the previous task point to the second for the platform
The gear shifting times in the driving process before the secondary rescheduling;
is to enter into
Shifting from secondary rescheduling to end of backward driving platform driving processThe number of times;
is the first
And (4) re-planning the gear shifting times of the forward driving process of the platform to the next target point.
Optionally, the multi-target path selection model building module specifically includes:
a normalization processing unit for respectively normalizing
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the least number of shifts
To (1) a
Secondary analog data
By the formula
Carrying out standardization processing to obtain standardized data
；
An entropy weight calculation unit for calculating the normalized data
By the formula
Calculate the first
Entropy weighting of secondary analog data
(ii) a Wherein
Re-planning times;
an importance ratio calculation unit for calculating an importance ratio based on the entropy weight
By the formula
Determining importance ratios of adjacent sub-simulation data
；
A weight calculation unit for calculating a weight based on the importance ratio
By the formula
Computing weights for a routing model
；
A multi-target path selection model construction unit for constructing a multi-target path selection model according to the weight
Building multi-objective path selection model
(ii) a Wherein the content of the first and second substances,
are respectively the first
The path selection model based on the shortest time in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
The weight of (c);
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
A calculated scalar value.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a multi-target path re-planning method for an unmanned crawler hybrid power platform, which comprises the following steps: establishing a cross-country environment model for driving the unmanned crawler belt hybrid power platform and a dynamic model for driving the unmanned crawler belt hybrid power platform; when the unmanned crawler hybrid power platform is blocked on a road or the driving direction is changed, triggering the unmanned crawler hybrid power platform to re-plan a global path and a plurality of backspacing paths capable of going to a next task point along the global path; establishing a path selection model; the route selection model comprises a shortest time-based route selection model, an optimal energy-based route selection model and a least shift-time-based route selection model; simulating the driving condition of the unmanned crawler belt hybrid power platform on the plurality of backspacing paths based on the cross-country environment model and the dynamic model, and respectively obtaining simulation data of the path selection model; constructing a multi-target path selection model based on an entropy weight method according to the simulation data of the path selection model; selecting an optimal backspacing path from the plurality of backspacing paths according to the multi-target path selection model; and controlling the unmanned crawler hybrid power platform to retreat along the optimal retreat path and then travel to the next task point along the global path again. The method can solve the problem that the platform characteristics, the time cost and the cruising ability are not considered when the platform is blocked or the driving direction is changed, and achieves the purposes of shortening the time and energy consumed by the platform in driving and improving the stability of path re-planning.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a multi-goal path re-planning method for an unmanned crawler hybrid power platform according to the invention;
fig. 2 is a schematic diagram of a re-planning scene during road blocking according to an embodiment of the present invention;
FIG. 3 is a flowchart of a re-planning trigger provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of the multi-goal path re-planning system for the unmanned crawler hybrid power platform according to the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a multi-target path re-planning method for an unmanned crawler hybrid power platform, which aims to solve the problems that the characteristics, time cost and cruising ability of the platform are not considered when the road of the platform is blocked or the driving direction of the platform is changed, and achieve the purposes of shortening the time and energy consumed by the platform in driving and improving the path re-planning stability.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flow chart of a multi-target path re-planning method for an unmanned crawler hybrid power platform according to the invention. Referring to fig. 1, the multi-target path re-planning method for the unmanned crawler hybrid power platform comprises the following steps:
step 101: and establishing a cross-country environment model for the unmanned crawler hybrid power platform to run and a dynamic model for the unmanned crawler hybrid power platform to run.
Firstly, an off-road environment model for driving an unmanned crawler hybrid power platform (platform for short) is established, so that the platform can drive in the environment. Wherein the cross-country environmental model comprises: the platform driving area has the length and the width
And with
(ii) a Establishing a fork (such as six forks) scene at the center of the platform driving area, and defining the passing time of the platform according to the flatness, the bending degree and the gradient of each road branch
Energy consumption value
And suitable gear parameters
And assigning the model attributes to the model attributes of the global road network.
A dynamic model of the unmanned track hybrid power platform in running is established to meet the requirements of the platform on speed and maximum curvature limitation under different working conditions, and the platform can be ensured to track a preset track as accurately as possible. Wherein the kinetic model is:
wherein
And
respectively outputting torque of a left motor and torque of a right motor of the unmanned crawler belt hybrid power platform;
the transmission ratio of the gear is set;
the transmission ratio of the main speed reducers on two sides is adopted;
the radius of the driving wheel is the rotation radius;
the mass of the unmanned crawler hybrid power platform;
is a universal gravitation constant;
，
in the case of a slope of a ramp,
is the rolling resistance coefficient;
for the longitudinal movement speed of the unmanned crawler hybrid power platform,
is the first derivative of the velocity;
is a rotational mass conversion factor.
Step 102: when the unmanned crawler hybrid power platform is blocked or the driving direction is changed on a road, triggering the unmanned crawler hybrid power platform to re-plan a global path and a plurality of backspacing paths capable of going to a next task point along the global path.
And determining the concept of path re-planning and a guiding strategy. During driving, the length of the road in front of the platform is less than
When the road is in the rice, the road blocking is considered to possibly occur;
according to platform dynamics and the sensor's limitations on environmental perception. Considering that the phenomenon of false detection is sensed, the platform needs to be driven for a certain distance for secondary judgment, when the detected number of the road blocking information frames reaches a set threshold value, the road blocking is determined to be the road blocking in front of the platform, then a path bypassing the blocked area needs to be re-planned so as to be capable of continuing to reach the target point of the subsequent task, and the process is called re-planning. FIG. 2 illustrates a typical blocked road ahead scenario for a platform at the point of completing a task
After the target, the road section 1 is guided to the next task point by referring to the global path
However, when the platform is detected to be unable to pass through the road section after driving into the road section 1 for a certain distance, the platform needs to re-plan a path from the current position to the terminal point, the unmanned control system updates the guidance strategy, and the guidance platform bypasses to the task point through the road section 2
And continues to travel. According to the motion state of the platform, the invention divides the re-planning into a re-planning triggering stage and a re-planning executing stage.
The main task of the replanning triggering stage is to judge whether the front road section can pass or not and plan a new global path according to the judgment result. In the method of the present invention, the minimum threshold length of the known local planning path is
In the local planning of the continuation
Perceptual data detection of frames to path lengths ahead of the platform
Are all less than the threshold value
It may be preliminarily assumed that the road section ahead is not passable. However, considering that the sensing system has false detection, which causes temporary shortening of local planned route, so that the fault is judged as a front non-passable area, in order to eliminate the fault and cause a re-planning function, when detecting that the front road section is not passable, backing is needed
Waiting for rice
And confirming again, and if the front road section is still detected to be impassable after backing the vehicle, confirming to trigger replanning. The specific numerical definitions of the relevant parameters are shown in table 1, based on platform geometry and dynamics.
TABLE 1 Replanning trigger procedure-related parameters
In addition, through detection of the platform image recognition system, when the specific target object influencing the safety of the unmanned crawler hybrid power platform is confirmed and recognized, secondary judgment is not carried out any more, and the unmanned crawler hybrid power platform directly enters a re-planning process, so that the unmanned crawler hybrid power platform is withdrawn from a dangerous area in time, and the safety of the platform is ensured.
The triggered re-planning process is shown in fig. 3. Referring to fig. 3, when the unmanned crawler hybrid platform is blocked or the driving direction of the unmanned crawler hybrid platform is changed on a road, triggering the unmanned crawler hybrid platform to re-plan a global path specifically includes:
when the unmanned crawler hybrid power platform runs normally, the length of the front path is judged
Whether or not it is less than a passable threshold
Obtaining a first judgment result;
if the first judgment result is
<
And the unmanned crawler belt hybrid power platform backs a car
Rice waiting
Second later, the front path length is judged again
Whether or not less than
+
Obtaining a second judgment result;
if the second judgment result is
<
+
Determining that the road is blocked, and triggering the unmanned crawler hybrid power platform to re-plan a global path at the moment;
if the first judgment result isIs composed of
≥
Or the second judgment result is
≥
+
Judging whether a specific target object exists in front or not to obtain a third judgment result;
if the third judgment result shows that a specific target object exists in the front, determining that the driving direction needs to be changed, and triggering the unmanned crawler hybrid power platform to re-plan a global path;
if the third judgment result shows that no specific target object exists in the front, returning to the unmanned crawler hybrid power platform to judge the front path length when the unmanned crawler hybrid power platform normally runs
Whether or not it is less than a passable threshold
The step (2).
After the fact that the re-planning is needed is confirmed through judgment of a re-planning triggering stage, the ground unmanned crawler hybrid power platform with non-integrity constraint enters a re-planning execution stage, and the platform firstly re-plans a global path through an A-x algorithm. The new road is started from the rear of the platform, the platform needs to change the driving direction, so the planning result path is divided into two sections of execution of a backward driving stage and a forward driving stage, firstly, the platform planning system sends a backward driving command to the execution system, the platform is backward driven to the nearest fork at the current position to adjust the course, and after the backward driving process is finished, the control is carried outAnd the system sends a forward command to the platform actuator, and the platform continues to run along the global path. Because a plurality of backward movement backward sections exist at the turnout, after a global path is re-planned by the unmanned crawler hybrid power platform, a plurality of backward paths which can go to the next task point along the global path are planned. It should be noted that, after the front road segment is blocked, in order to prevent the path from traveling along the original blocked road segment again during the path search, the topological relationship between two paths of points at the blocking position needs to be interrupted before planning, for example, the second point corresponding to the projection point of the platform on the road segment 1 shown in fig. 2
A waypoint and
and (4) breaking the topological relation among the road points, taking the current position of the platform as a new starting point, and re-planning a global path which does not contain a road blocking area according to the requirements of the remaining tasks.
Step 103: establishing a path selection model; the path selection model comprises a path selection model based on the shortest time, a path selection model based on the optimal energy and a path selection model based on the least number of shifts.
The invention provides a decision method for selecting a fallback road section by an unmanned track hybrid power platform in a re-planning execution process, namely a multi-target path re-planning algorithm based on an entropy weight method, so that when the platform faces a plurality of fallback road sections at a turnout, an optimal fallback path with consideration of time, energy, different turning radii and speeds and a gear to be selected can be selected.
Under the condition of unknown target tasks, actual traffic conditions are not considered for the unmanned crawler hybrid power platform, and in order to meet different requirements of the platform on different tasks, the invention provides an entropy weight method for multi-target path selection. A multi-target path selection model based on the 3 sub-targets with the shortest time, the lowest energy and the least gear shifting times is mainly established. Compared with the existing iterative algorithm, the multi-target path re-planning based on the entropy weight method is a re-planned optimal path generated by considering the traffic time, the economy and the platform dynamics characteristics.
(3.1): the shortest time. In a transient and variable combat environment, accurate and rapid path planning is the basis for avoiding all risks as much as possible and for autonomously completing set tasks. When the task executed by the unmanned crawler hybrid power platform is urgent, the completion time needs to be shortened as much as possible, and therefore, a path selection model based on the shortest time is provided
：
Wherein the content of the first and second substances,
is a set of re-planning times;
is a first
The path selection model corresponding to the secondary re-planning and based on the shortest time is planned;
、
the longitudinal average speeds of the unmanned crawler hybrid power platform in the forward and backward states are respectively;
is as follows
The normal passage distance of the secondary re-planned platform,
is a first
Normal passage time of the secondary re-planned platform;
is as follows
Parking waiting time when the secondary re-planning is confirmed;
is as follows
The reverse distance of the platform which is re-planned,
is a first
The time required for the platform to drive back at the turnout during the secondary re-planning is saved.
(3.2): the energy is optimal. On the premise of meeting the dynamic property, the platform reduces energy consumption, and the key for executing unmanned driving tasks and realizing the performance of the platform is to save energy cost. Therefore, a driving route needs to be reasonably arranged, the phenomenon of 'supply short demand' of energy in the driving process is avoided, and because the energy consumption of transverse movement is small, the invention only considers the energy consumption of longitudinal movement and provides a path selection model based on energy optimization
：
Wherein, the first and the second end of the pipe are connected with each other,
is as follows
The path selection model based on the optimal energy corresponding to the secondary re-planning;
to
Is that the platform goes from the previous task point to the first
The driving time before secondary re-planning;
to
Is entered into
Planning the running time from the second re-planning to the end of the backward running;
to
Is the first
And the secondary re-planning platform starts to drive to the next target point in the forward direction.
Step (3.3): the number of shifts is minimal. Because the platform is provided with the multi-gear automatic speed change mechanism, the platform can be matched with different gears according to the planned speed, the length of the front path and the curvature of the path, the platform is matched with different gears according to the dynamic characteristics of the platform, in order to improve the stability of the platform in the driving process,the loss of the transmission is reduced, the running safety of the platform is ensured, and a path selection model based on the minimum number of gear shifting times is provided
：
Wherein the content of the first and second substances,
is as follows
A path selection model which corresponds to the secondary re-planning and is based on the minimum number of gear shifting times is planned;
from the previous task point to the second for the platform
Gear shifting times in the driving process before secondary re-planning;
is entered into
Re-planning the gear shifting times in the running process of the platform from the second re-planning to the end of the running;
is the first
And (4) re-planning the gear shifting times of the forward driving process of the platform to the next target point.
Step 104: and simulating the driving condition of the unmanned crawler hybrid power platform on the plurality of backspacing paths based on the cross-country environment model and the dynamic model, and respectively obtaining the simulation data of the path selection model.
Simulating the running condition of the unmanned crawler hybrid power platform on the plurality of backspacing paths based on the cross-country environment model and the dynamic model to respectively obtain a first backspacing path
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the minimum number of shifts
To (1) a
Secondary simulation data, note as
。
Step 105: and constructing a multi-target path selection model based on an entropy weight method according to the simulation data of the path selection model.
Are respectively to the first
The path selection model based on the shortest time in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the least number of shifts
To (1) a
Secondary analog data
Performing standardization processing to obtain standardized data
(ii) a The normalization process formula is as follows:
according to the standardized data
Calculate the first
Entropy weighting of secondary analog data
：
Wherein
The number of re-scribes.
According to the entropy weight
Determining importance ratios of adjacent sub-simulation data
：
According to the importance ratio
Calculating weights of the path selection models
：
According to the weight
Constructing a multi-target path selection model:
wherein the content of the first and second substances,
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
Weight of (2)；
Are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the minimum number of shifts
Calculating a scalar value;
is the first
And (5) the multi-target path selection model corresponding to the secondary re-planning.
According to the invention, different weight coefficients are divided according to the importance to the three parameters of time, energy and the number of gear shifting times, and a multi-target path selection model is established. The method solves the problems that when the road is blocked or the driving direction of the platform is changed, the characteristics of the platform, the time cost and the cruising ability are not considered, the time, the energy and the gears which should be selected when different turning radii and speeds are considered, the dynamic property of longitudinal movement is ensured, and meanwhile the timeliness and the economy are also considered fully.
Step 106: and selecting an optimal fallback path from the plurality of fallback paths according to the multi-target path selection model.
Each time a different back-off path is selected for re-planning, the first
The second re-planning being in accordance with
A strip fallback path. Will be first
Sub-rescheduling time path selection model
Calculated scalar value
Substituting into formula (9) to calculate out different
The value is obtained. Selecting
The smallest value of the second
And the strip rollback path is used as an optimal rollback path. Wherein the scalar value
Is that
Dimensionless values.
Step 107: and controlling the unmanned crawler hybrid power platform to retreat along the optimal retreat path and then travel to the next task point along the global path again.
On the basis of an A-algorithm based on a topological road network, the method of the invention provides that an optimal backspacing path is selected by adopting a multi-target path selection model based on an entropy weight method, the optimization problem that a backspacing road intersection or a backspacing road section is selected in the rescheduling process of a platform is solved, the problem of subjective randomness caused by directly determining index weight is avoided, the comprehensiveness of evaluation indexes is considered, and the aims of shortening the time and energy consumed by the platform in driving and improving the stability of a path rescheduling module are finally achieved.
Based on the method provided by the invention, the invention also provides a multi-target path re-planning system for the unmanned crawler hybrid power platform, which is characterized by comprising the following steps:
the physical model establishing module 401 is used for establishing an off-road environment model for driving the unmanned crawler hybrid power platform and a dynamic model for driving the unmanned crawler hybrid power platform;
a re-planning triggering module 402, configured to trigger the unmanned crawler hybrid platform to re-plan a global path and a plurality of fallback paths that can go to a next task point along the global path when the unmanned crawler hybrid platform is blocked on a road or changes a driving direction;
a path selection model establishing module 403, configured to establish a path selection model; the route selection model comprises a shortest time-based route selection model, an optimal energy-based route selection model and a least shift-time-based route selection model;
a platform driving simulation module 404, configured to simulate, based on the off-road environment model and the dynamic model, a driving condition of the unmanned crawler hybrid power platform on the multiple fallback paths, and obtain simulation data of the path selection model respectively;
a multi-objective path selection model construction module 405, configured to construct a multi-objective path selection model based on an entropy weight method according to simulation data of the path selection model;
an optimal fallback path selection module 406, configured to select an optimal fallback path from the multiple fallback paths according to the multi-target path selection model;
and the platform running control module 407 is configured to control the unmanned crawler hybrid platform to run to a next task point along the global path again after returning along the optimal return path.
The physical model building module 401 specifically includes:
a dynamic model building unit for building the dynamic modelDynamic model for driving unmanned crawler belt hybrid power platform
(ii) a Wherein
And
the left motor output torque and the right motor output torque of the unmanned crawler belt hybrid power platform are respectively;
the gear is the transmission ratio of the gear;
the transmission ratio of the main speed reducers on two sides is adopted;
the radius of the driving wheel is the rotation radius;
the mass of the unmanned crawler hybrid power platform;
is a universal gravitation constant;
，
in the case of a slope of a ramp,
is the rolling resistance coefficient;
the longitudinal movement speed of the unmanned crawler hybrid power platform is obtained;
is a rotating mass scaling factor.
The replanning triggering module 402 specifically includes:
a first judging unit for judging the front path length when the unmanned crawler hybrid power platform normally runs
Whether or not it is less than a passable threshold
Obtaining a first judgment result;
a second judgment unit for judging if the first judgment result is
<
And the unmanned crawler belt hybrid power platform backs a car
Rice waiting
Second later, the front path length is judged again
Whether or not less than
+
Obtaining a second judgment result;
a first re-planning triggering unit for judging if the second judgment result is
<
+
Determining that the road is blocked, and triggering the unmanned crawler hybrid power platform to re-plan a global path at the moment;
a third judging unit for judging if the first judgment result is
≥
Or the second judgment result is
≥
+
Judging whether a specific target object exists in front or not to obtain a third judgment result;
the second replanning triggering unit is used for determining that the driving direction needs to be changed if the third judgment result shows that a specific target object exists in the front, and triggering the unmanned crawler hybrid power platform to replan a global path at the moment;
and the circulation judging unit is used for returning to the first judging unit if the third judging result shows that no specific target object exists in the front.
The path selection model building module 403 specifically includes:
a first path selection model establishing unit for establishing a path selection model based on the shortest time
(ii) a Wherein the content of the first and second substances,
is a set of re-planning times;
is as follows
The path selection model corresponding to the secondary re-planning and based on the shortest time is planned;
、
the longitudinal average speeds of the unmanned crawler hybrid power platform in the forward and backward states are respectively;
is as follows
The normal distance of passage of the sub-planned platform,
is a first
Normal passage time of the secondary re-planned platform;
is as follows
Parking waiting time when the secondary re-planning is confirmed;
is as follows
The reverse distance of the platform which is re-planned,
is as follows
The time required for the platform to run back at the turnout junction during secondary re-planning;
a second path selection model establishing unit for establishing a path selection model based on energy optimization
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
is as follows
The path selection model based on the optimal energy corresponding to the secondary re-planning;
to
Is that the platform goes from the previous task point to the first
The driving time before the secondary re-planning;
to
Is entered into
Planning the running time from the secondary re-planning to the end of the backward running;
to
Is the first
The secondary re-planning platform starts to drive to the next target point in the forward direction;
a third path selection model creation unit for creating a path selection model based on the fewest number of shifts
(ii) a Wherein the content of the first and second substances,
is as follows
The path selection model which corresponds to the secondary re-planning and is based on the minimum number of gear shifting times is planned;
from the previous task point to the first for the platform
The gear shifting times in the driving process before the secondary rescheduling;
is to enter into
Re-planning the gear shifting times in the running process of the platform from the second re-planning to the end of the running;
is the first
And (4) re-planning the gear shifting times of the forward driving process of the platform to the next target point.
The multi-target path selection model building module 405 specifically includes:
a standardization processing unit for respectively comparing
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the least number of shifts
To (1) a
Secondary analog data
By the formula
Carrying out standardization processing to obtain standardized data
；
An entropy weight calculation unit for calculating the normalized data
By the formula
Calculate the first
Entropy weighting of secondary analog data
(ii) a Wherein
Re-planning times;
an importance ratio calculation unit for calculating an importance ratio based on the entropy weight
By the formula
Determining importance ratios of adjacent sub-simulation data
；
A weight calculation unit for calculating a weight based on the importance ratio
By the formula
Computing weights for a routing model
；
A multi-target path selection model construction unit for constructing a multi-target path selection model according to the weight
Constructing a multi-objective path selection model
(ii) a Wherein the content of the first and second substances,
are respectively the first
Plan for next timeTime-based shortest path selection model
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
The weight of (c);
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
A calculated scalar value.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (6)
1. A multi-target path re-planning method for an unmanned crawler hybrid power platform is characterized by comprising the following steps:
establishing a cross-country environment model for driving the unmanned crawler belt hybrid power platform and a dynamic model for driving the unmanned crawler belt hybrid power platform;
the establishing of the driving dynamic model of the unmanned crawler belt hybrid power platform specifically comprises the following steps:
establishing a driving dynamic model of the unmanned crawler belt hybrid power platform
(ii) a Wherein
And
the left motor output torque and the right motor output torque of the unmanned crawler belt hybrid power platform are respectively;
the transmission ratio of the gear is set;
the transmission ratio of the main speed reducers on two sides is adopted;
the radius of the driving wheel is the rotation radius;
the mass of the unmanned crawler hybrid power platform;
is a universal gravitation constant;
，
in the case of a slope of a ramp,
is the rolling resistance coefficient;
the longitudinal movement speed of the unmanned crawler hybrid power platform is obtained;
the conversion coefficient of the rotating mass;
when the unmanned crawler hybrid power platform is blocked on a road or the driving direction is changed, triggering the unmanned crawler hybrid power platform to re-plan a global path and a plurality of backspacing paths capable of going to a next task point along the global path;
establishing a path selection model; the route selection model comprises a shortest time-based route selection model, an optimal energy-based route selection model and a least shift-time-based route selection model;
the establishing of the path selection model specifically comprises the following steps:
establishing a time-based shortest path selection model
(ii) a Wherein the content of the first and second substances,
is a set of re-planning times;
is a first
The path selection model corresponding to the secondary re-planning and based on the shortest time is planned;
、
the longitudinal average speeds of the unmanned crawler hybrid power platform in the forward and backward states are respectively;
is as follows
The normal distance of passage of the sub-planned platform,
is a first
Normal passage time of the secondary re-planned platform;
is as follows
Parking waiting time when the secondary re-planning is confirmed;
is as follows
The reverse distance of the platform which is re-planned,
is as follows
The time required for the platform to run back at the turnout junction during secondary re-planning;
establishing a path selection model based on energy optimization
(ii) a Wherein the content of the first and second substances,
is as follows
The path selection model based on the optimal energy corresponding to the secondary re-planning;
to
Is that the platform goes from the previous task point to the first
The driving time before the secondary re-planning;
to is that
Is entered into
Planning the running time from the second re-planning to the end of the backward running;
to
Is the first
The secondary re-planning platform starts to drive to the next target point in the forward direction;
establishing a path selection model based on least number of shifts
(ii) a Wherein the content of the first and second substances,
is as follows
The path selection model which corresponds to the secondary re-planning and is based on the minimum number of gear shifting times is planned;
from the previous task point to the second for the platform
The gear shifting times in the driving process before the secondary rescheduling;
is entered into
Re-planning the gear shifting times in the running process of the platform from the secondary re-planning to the end of the backward running;
is the first
The gear shifting times of the forward driving process of the secondary re-planning platform to the next target point are planned;
simulating the situation that the unmanned crawler hybrid power platform runs on the plurality of backspacing paths based on the off-road environment model and the dynamic model to obtain simulation data of the path selection model;
constructing a multi-target path selection model based on an entropy weight method according to the simulation data of the path selection model;
selecting an optimal backspacing path from the plurality of backspacing paths according to the multi-target path selection model;
and controlling the unmanned crawler hybrid power platform to retreat along the optimal retreat path and then travel to the next task point along the global path again.
2. The method according to claim 1, wherein triggering the unmanned aerial vehicle hybrid platform to re-plan a global path when the unmanned aerial vehicle hybrid platform is blocked or changes driving direction on a road comprises:
when the unmanned crawler hybrid power platform runs normally, the length of the front path is judged
Whether or not it is less than a passable threshold
Obtaining a first judgment result;
if the first judgment result is
<
And the unmanned crawler belt hybrid power platform backs a car
Rice waiting
Second later, the front path length is judged again
Whether or not less than
+
Obtaining a second judgment result;
if the second judgment result is
<
+
Determining that the road is blocked, and triggering the unmanned crawler hybrid power platform to re-plan a global path at the moment;
if the first judgment result is
≥
Or the second judgment result is
≥
+
Judging whether a specific target object exists in front or not to obtain a third judgment result;
if the third judgment result shows that a specific target object exists in the front, determining that the driving direction needs to be changed, and triggering the unmanned crawler hybrid power platform to re-plan a global path;
if the third judgment result shows that no specific target object exists in the front, returning to the unmanned crawler hybrid power platform to judge the front path length when the unmanned crawler hybrid power platform normally runs
Whether or not it is less than a passable threshold
The step (2).
3. The method according to claim 1, wherein the constructing the multi-objective path selection model based on the entropy weight method according to the simulation data of the path selection model specifically comprises:
are respectively to the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the least number of shifts
To (1) a
Secondary analog data
By the formula
Carrying out standardization processing to obtain standardized data
；
According to the standardized data
By the formula
Calculate the first
Entropy weighting of secondary analog data
(ii) a Wherein
Re-planning times;
according to the entropy weight
By the formula
Determining importance ratio of adjacent sub-simulation data
；
According to the importance ratio
By the formula
Computing weights for a routing model
；
According to the weight
Constructing a multi-objective path selection model
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
are respectively the first
The path selection model based on the shortest time in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
The weight of (c);
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
A calculated scalar value.
4. The utility model provides a multi-target path re-planning system towards unmanned track hybrid platform which characterized in that includes:
the physical model establishing module is used for establishing a cross-country environment model for the unmanned crawler hybrid power platform to run and a dynamic model for the unmanned crawler hybrid power platform to run;
the physical model building module specifically comprises:
a dynamic model establishing unit for establishing a dynamic model of the unmanned crawler belt hybrid power platform in running
(ii) a Wherein
And
the left motor output torque and the right motor output torque of the unmanned crawler belt hybrid power platform are respectively;
the transmission ratio of the gear is set;
the transmission ratio of the main reducers on two sides is adopted;
the radius of the driving wheel is the rotation radius;
the mass of the unmanned crawler hybrid power platform;
is a universal gravitation constant;
，
in the case of a slope of a ramp,
is the rolling resistance coefficient;
the longitudinal movement speed of the unmanned crawler hybrid power platform is obtained;
the conversion coefficient of the rotating mass;
the rescheduling triggering module is used for triggering the unmanned crawler hybrid power platform to reschedule a global path and a plurality of backspacing paths which can go to a next task point along the global path when the unmanned crawler hybrid power platform is blocked or the driving direction is changed on a road;
the path selection model establishing module is used for establishing a path selection model; the route selection model comprises a shortest time-based route selection model, an optimal energy-based route selection model and a least shift-time-based route selection model;
the path selection model establishing module specifically comprises:
first path selection model establishment sheetElement for establishing a shortest time-based path selection model
(ii) a Wherein the content of the first and second substances,
is a set of re-planning times;
is as follows
The path selection model corresponding to the secondary re-planning and based on the shortest time is planned;
、
the longitudinal average speeds of the unmanned crawler hybrid power platform in the forward and backward states are respectively;
is as follows
The normal distance of passage of the sub-planned platform,
is a first
Normal passage time of the secondary re-planned platform;
is a first
Parking waiting time when the secondary re-planning is confirmed;
is as follows
The reverse distance of the platform which is re-planned,
is as follows
The time required for the platform to run back at the turnout junction during secondary re-planning;
a second path selection model establishing unit for establishing a path selection model based on energy optimization
(ii) a Wherein the content of the first and second substances,
is as follows
The path selection model corresponding to the secondary re-planning and based on the optimal energy is planned;
to
Is that the platform goes from the previous task point to the first
The driving time before the secondary re-planning;
to is that
Is entered into
Planning the running time from the second re-planning to the end of the backward running;
to
Is the first
The secondary re-planning platform starts to drive to the next target point in the forward direction;
a third path selection model establishing unit for establishing a path selection model based on the least number of shifts
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
is as follows
The path selection model which corresponds to the secondary re-planning and is based on the minimum number of gear shifting times is planned;
from the previous task point to the second for the platform
The gear shifting times in the driving process before the secondary rescheduling;
is entered into
Re-planning the gear shifting times in the running process of the platform from the second re-planning to the end of the running;
is the first
The gear shifting times of the forward driving process of the secondary re-planning platform to the next target point are planned;
the platform running simulation module is used for simulating the running condition of the unmanned crawler hybrid power platform on the plurality of backspacing paths based on the off-road environment model and the dynamic model, and respectively obtaining simulation data of the path selection model;
the multi-target path selection model building module is used for building a multi-target path selection model based on an entropy weight method according to the simulation data of the path selection model;
the optimal backspacing path selection module is used for selecting an optimal backspacing path from the plurality of backspacing paths according to the multi-target path selection model;
and the platform running control module is used for controlling the unmanned crawler hybrid power platform to run to the next task point along the global path again after returning along the optimal returning path.
5. The system according to claim 4, wherein the replanning trigger module specifically comprises:
a first judging unit for judging the front path length when the unmanned crawler hybrid power platform normally runs
Whether or not it is less than a passable threshold
Obtaining a first judgment result;
a second judgment unit for judging if the first judgment result is
<
And the unmanned crawler hybrid power platform backs a car
Rice waiting
Second later, the front path length is judged again
Whether or not less than
+
Obtaining a second judgment result;
a first re-planning triggering unit for judging if the second judgment result is
<
+
Determining that the road is blocked, and triggering the unmanned crawler hybrid power platform to re-plan a global path at the moment;
third judgment sheetElement for judging if the first judgment result is
≥
Or the second judgment result is
≥
+
Judging whether a specific target object exists in front or not to obtain a third judgment result;
the second replanning triggering unit is used for determining that the driving direction needs to be changed if the third judgment result shows that a specific target object exists in the front, and triggering the unmanned crawler hybrid power platform to replan a global path at the moment;
and the circulation judging unit is used for returning to the first judging unit if the third judging result shows that no specific target object exists in the front.
6. The system of claim 4, wherein the multi-goal path selection model building module specifically comprises:
a normalization processing unit for respectively normalizing
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
Or the path selection model based on the least number of shifts
To (1)
Secondary analog data
By the formula
Carrying out standardization processing to obtain standardized data
；
An entropy weight calculation unit for calculating the normalized data
By the formula
Calculate the first
Entropy weighting of secondary analog data
(ii) a Wherein
Re-planning times;
an importance ratio calculation unit for calculating an importance ratio based on the entropy weight
By the formula
Determining importance ratio of adjacent sub-simulation data
；
A weight calculation unit for calculating a weight based on the importance ratio
By the formula
Computing weights for a routing model
；
A multi-target path selection model construction unit for constructing a multi-target path selection model according to the weight
Constructing a multi-objective path selection model
(ii) a Wherein the content of the first and second substances,
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the least number of shifts
The weight of (c);
are respectively the first
The shortest time-based path selection model in secondary re-planning
The optimal energy-based path selection model
And the path selection model based on the minimum number of shifts
A calculated scalar value.
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