CN113985912A - Path planning method and system for cooperative inspection of vehicle and unmanned aerial vehicle - Google Patents

Abstract

The invention provides a path planning method and system for cooperative inspection of a vehicle and an unmanned aerial vehicle, and relates to the technical field of cooperative operation. Firstly, acquiring relevant parameters in a vehicle-mounted device collaborative inspection process; then presetting a vehicle-machine cooperative inspection constraint condition, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on related parameters; and finally, solving the constructed vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path. The method considers the real situation in the scene of the vehicle-machine cooperative inspection, thereby being capable of rapidly and accurately solving the optimal scheme of the path planning of the vehicle-machine cooperative inspection and scientifically and effectively guiding the related work.

Description

Path planning method and system for cooperative inspection of vehicle and unmanned aerial vehicle

Technical Field

The invention relates to the technical field of cooperative operation, in particular to a path planning method and system for cooperative inspection of a vehicle and an unmanned aerial vehicle.

Background

Along with the development of sensor technology, the unmanned aerial vehicle carries various loads to play an increasingly important role in military and civil fields such as information reconnaissance, power inspection and the like. However, at present, due to the limited battery capacity, the unmanned aerial vehicle cannot perform the routing inspection task for a long time or a long distance, and the target execution efficiency is low. In order to solve the problem of endurance of the unmanned aerial vehicle when the unmanned aerial vehicle executes the polling task, the unmanned aerial vehicle and the vehicle can be used for task polling in a coordinated mode, the vehicle serves as a ground moving platform, the unmanned aerial vehicle can be carried, the battery of the unmanned aerial vehicle can be replaced to recycle the unmanned aerial vehicle, and therefore the efficiency of the unmanned aerial vehicle executing the target polling can be effectively improved.

In the prior art, when the vehicle-mounted cooperative inspection is implemented, an effective solution of the vehicle-mounted cooperative inspection path planning cannot be accurately or quickly solved, so that the related work of vehicle-mounted cooperative inspection cannot be scientifically and effectively guided.

Therefore, a new path planning technology for collaborative inspection of the vehicle and the unmanned aerial vehicle is urgently needed to be provided so as to effectively solve the problem that the path planning result of collaborative inspection of the vehicle and the unmanned aerial vehicle cannot be quickly and accurately solved in the prior art.

Disclosure of Invention

Technical problem to be solved

Aiming at the defects of the prior art, the invention provides a path planning method and a system for cooperative inspection of a vehicle and an unmanned aerial vehicle, which solve the problem that the path planning result of cooperative inspection of a vehicle and the unmanned aerial vehicle cannot be quickly and accurately solved in the prior art.

(II) technical scheme

In order to achieve the purpose, the invention is realized by the following technical scheme:

in a first aspect, the present invention first provides a path planning method for cooperative inspection of a vehicle and an unmanned aerial vehicle, where the method includes:

acquiring vehicle-machine cooperative inspection related parameters, wherein the vehicle-machine cooperative inspection related parameters comprise unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters;

acquiring preset vehicle-machine cooperative inspection constraint conditions, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal point constraint;

and solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

Preferably, the drone parameters include drone speed and maximum endurance time of the drone;

the ground vehicle parameters include ground vehicle speed;

the inspection task parameters comprise the time that each target point needs to be inspected by the unmanned aerial vehicle;

the road network parameters comprise a node coordinate set in the vehicle-machine cooperation inspection process, and the nodes comprise starting points, stop points, end points and target points.

Preferably, the objective function includes:

minz＝t_*

wherein, t_*And the total time consumption of the unmanned aerial vehicle from the starting point and reaching the end point after all the target points are inspected is shown.

Preferably, the patrol target point path inhibition loop constraint includes:

M×(1-y_ij)≥(Q_i+C_j-Q_j)，i,j∈V_ti ≠ j, which guarantees that if the drone flies from target point i to target point j, the drone takes off from a certain stop point until the drone has patrolled target point i_iPatrol time C with unmanned aerial vehicle at target point j_jThe sum of the time Q of the unmanned aerial vehicle from the takeoff of a certain stop point to the inspection completion of the target point j is less than or equal to_jPreventing the flying path of the unmanned aerial vehicle from being in a condition of i → j → i;

the unmanned aerial vehicle take-off point constraint includes:

this constraint ensures that if an unmanned aerial vehicle takes off from a certain stop point i, that stop point must be on the path of the ground vehicle;

the ground vehicle stop point time constraints include:

M×(3-y_ij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-Q_i|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which

Constraint guarantees that if the unmanned aerial vehicle takes off from the stop point k, the patrol target point i, lands to the stop point j, and the ground vehicle runs from the stop point k to the stop point j, the time t when the ground vehicle leaves the stop point j is_jEqual to the time t at which the ground vehicle leaves the stopping point k_kAnd the time Q from the takeoff of the unmanned aerial vehicle from the stop point k to the completion of the inspection of the target point i_iTime d for unmanned aerial vehicle to fly from target point i to stop point j_ij/v₁The sum of the three;

M×(1-x_ij)≥|t_j-t_i-d_ij/v₂-s_j|,i∈{0}∪V_s,j∈V_s{. j, i ≠ j, which guarantees the waiting time s of the ground vehicle at the docking point j if the ground vehicle travels from the docking point i to the docking point j_jEqual to the moment t at which the ground vehicle leaves the stop point j_jWith the time (t) at which the ground vehicle reaches the stop point j_i+d_ij/v₂) The difference between the two; wherein, t_iRepresents the time when the ground vehicle leaves the stopping point i;

M×(3-x_ij-S_ki-y_kq)≥t_q-t_j,i∈{0}∪V_s,j,q∈V_s∪{*},k∈V_tthe constraint ensures that if the ground vehicle travels from stop point i to stop point j, the unmanned aerial vehicle takes off from stop point i, inspects the target point k, and lands on stop point q, then the time t when the ground vehicle leaves stop point j_jAt a time t equal to or greater than the time when the ground vehicle leaves the stopping point q_qThat is, the unmanned aerial vehicle can continue to take off only after landing at a certain stop point;

the drone and ground vehicle access endpoint constraints include:

x_*i＝0，i∈{0}∪V_su {. the }, the constraint ensures that the ground vehicle terminates the driving state at the terminal point;

the constraint ensures that the drone does not take off from the endpoint;

the constraint ensures that the sum of the times of flying the unmanned aerial vehicle from the target point i to the terminal point is less than or equal to 1;

the preset vehicle-mounted machine collaborative inspection constraint condition further comprises:

x_ii＝0，i∈{0}∪V_su {. X }, which ensures that the ground vehicle cannot circularly run in situ at the docking point i;

Q_i＝0，i∈{0}∪V_su {. X }, the constraint ensures that Q is only when i is the target point_iIs valid, otherwise Q_iIs 0;

wherein M represents a positive integer approaching infinity;

v₁representing the speed of the drone;

v₂representing the speed of the ground vehicle;

d_ijrepresenting the Euclidean distance between the node i and the node j;

Q_ithe time from the takeoff of the unmanned aerial vehicle from a certain stop point to the completion of the inspection of the target point i is represented;

T_iindicating the order in which the stop points i are visited by the ground vehicle, as a continuous digital variable, T₀＝1；

s_iRepresenting the time for the ground vehicle to wait for the unmanned aerial vehicle at a stop point i;

t_iindicating the moment at which the ground vehicle leaves the stopping point i, t₀＝s₀；

{0} represents a starting point;

V_srepresenting all selectable sets of waypoints; { } denotes end point; v_tRepresenting a set of all target points;

C_irepresenting the time that the target point i needs to be inspected by the unmanned aerial vehicle, and i belongs to V_t；

x_ij、y_ij、S_ijAll represent decision variables, specifically:

preferably, the solving of the vehicle-machine cooperative inspection path planning model includes:

and solving the vehicle-machine cooperative inspection path planning model by using a CPLEX solver.

In a second aspect, the present invention further provides a path planning system for collaborative inspection of a vehicle and an unmanned aerial vehicle, where the system includes:

the system comprises a parameter acquisition module, a data processing module and a data processing module, wherein the parameter acquisition module is used for acquiring vehicle-machine cooperative inspection related parameters, and the vehicle-machine cooperative inspection related parameters comprise unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters;

the model construction module is used for acquiring preset vehicle-machine cooperative inspection constraint conditions and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal point constraint;

and the model solving module is used for solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

Preferably, the parameters of the unmanned aerial vehicle in the parameter acquisition module include the speed of the unmanned aerial vehicle and the longest endurance time of the unmanned aerial vehicle;

the ground vehicle parameters include ground vehicle speed;

the inspection task parameters comprise the time that each target point needs to be inspected by the unmanned aerial vehicle;

the road network parameters comprise a node coordinate set in the vehicle-machine cooperation inspection process, and the nodes comprise starting points, stop points, end points and target points.

Preferably, the objective function includes:

minz＝t_*

wherein, t_*And the total time consumption of the unmanned aerial vehicle from the starting point and reaching the end point after all the target points are inspected is shown.

Preferably, the patrol target point path inhibition loop constraint includes:

M×(1-y_ij)≥(Q_i+C_j-Q_j)，i,j∈V_ti ≠ j, which guarantees that if the drone flies from target point i to target point j, the drone takes off from a certain stop point until the drone has patrolled target point i_iPatrol time C with unmanned aerial vehicle at target point j_jThe sum of the time Q of the unmanned aerial vehicle from the takeoff of a certain stop point to the inspection completion of the target point j is less than or equal to_jPreventing the flying path of the unmanned aerial vehicle from being in a condition of i → j → i;

the unmanned aerial vehicle take-off point constraint includes:

this constraint ensures that if an unmanned aerial vehicle takes off from a certain stop point i, that stop point must be on the path of the ground vehicle;

the ground vehicle stop point time constraints include:

M×(3-y_ij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-Q_i|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which

Constraint guarantees that if the unmanned aerial vehicle takes off from the stop point k, the patrol target point i, lands to the stop point j, and the ground vehicle runs from the stop point k to the stop point j, the time t when the ground vehicle leaves the stop point j is_jEqual to the time t at which the ground vehicle leaves the stopping point k_kAnd the time Q from the takeoff of the unmanned aerial vehicle from the stop point k to the completion of the inspection of the target point i_iTime d for unmanned aerial vehicle to fly from target point i to stop point j_ij/v₁The sum of the three;

M×(1-x_ij)≥|t_j-t_i-d_ij/v₂-s_j|,i∈{0}∪V_s,j∈V_s{. j, i ≠ j, which guarantees the waiting time s of the ground vehicle at the docking point j if the ground vehicle travels from the docking point i to the docking point j_jEqual to the moment t at which the ground vehicle leaves the stop point j_jWith the time (t) at which the ground vehicle reaches the stop point j_i+d_ij/v₂) The difference between the two; wherein, t_iRepresents the time when the ground vehicle leaves the stopping point i;

M×(3-x_ij-S_ki-y_kq)≥t_q-t_j,i∈{0}∪V_s,j,q∈V_s∪{*},k∈V_tthe constraint ensures that if the ground vehicle travels from stop point i to stop point j, the unmanned aerial vehicle takes off from stop point i, inspects the target point k, and lands on stop point q, then the time t when the ground vehicle leaves stop point j_jAt a time t equal to or greater than the time when the ground vehicle leaves the stopping point q_qThat is, the unmanned aerial vehicle can continue to take off only after landing at a certain stop point;

the drone and ground vehicle access endpoint constraints include:

x_*i＝0，i∈{0}∪V_su {. the }, the constraint ensures that the ground vehicle terminates the driving state at the terminal point;

the constraint ensures that the drone does not take off from the endpoint;

the constraint ensures that the sum of the times of flying the unmanned aerial vehicle from the target point i to the terminal point is less than or equal to 1;

the preset vehicle-mounted machine collaborative inspection constraint condition further comprises:

x_ii＝0，i∈{0}∪V_su {. X }, which ensures that the ground vehicle cannot circularly run in situ at the docking point i;

Q_i＝0，i∈{0}∪V_su {. X }, the constraint ensures that Q is only when i is the target point_iIs valid, otherwise Q_iIs 0;

wherein M represents a positive integer approaching infinity;

v₁representing the speed of the drone;

v₂representing the speed of the ground vehicle;

d_ijrepresenting the Euclidean distance between the node i and the node j;

Q_ithe time from the takeoff of the unmanned aerial vehicle from a certain stop point to the completion of the inspection of the target point i is represented;

T_iindicating the order in which the stop points i are visited by the ground vehicle, as a continuous digital variable, T₀＝1；

s_iRepresenting the time for the ground vehicle to wait for the unmanned aerial vehicle at a stop point i;

t_iindicating the moment at which the ground vehicle leaves the stopping point i, t₀＝s₀；

{0} represents a starting point;

V_srepresenting all selectable sets of waypoints; { } denotes end point; v_tRepresenting a set of all target points;

C_irepresenting the time that the target point i needs to be inspected by the unmanned aerial vehicle, and i belongs to V_t；

x_ij、y_ij、S_ijAll represent decision variables, specifically:

preferably, the step of solving the vehicle-machine cooperative inspection path planning model by the model solving module includes:

and solving the vehicle-machine cooperative inspection path planning model by using a CPLEX solver.

(III) advantageous effects

The invention provides a path planning method and system for cooperative inspection of a vehicle and an unmanned aerial vehicle. Compared with the prior art, the method has the following beneficial effects:

1. the method comprises the steps of obtaining vehicle-mounted machine collaborative inspection related parameters; then presetting a vehicle-machine cooperative inspection constraint condition, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the related parameters; and finally, solving the constructed vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path. Compared with the prior art, the method can quickly and accurately obtain the feasible solution, and can obtain the optimal scheme of the vehicle-machine cooperative routing inspection path planning based on the feasible solution, so that the related work of vehicle-machine routing inspection can be scientifically and effectively guided.

2. The method constructs the vehicle-machine cooperative routing inspection path planning model by taking the shortest total time of the unmanned aerial vehicle when the unmanned aerial vehicle reaches the end point after finishing the routing inspection of all the target points as an objective function, and simultaneously presets constraint conditions such as routing inspection target point path forbidding loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint, unmanned aerial vehicle and ground vehicle access end point constraint and the like when constructing the model, so that the accuracy of solution and the rapidity of solution can be ensured.

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic view of a scene of the cooperative inspection of the vehicle-mounted inspection device;

fig. 2 is a flowchart of a path planning method for cooperative inspection of a vehicle and an unmanned aerial vehicle in the embodiment of the present invention;

FIG. 3 is a vehicle-machine cooperative inspection path diagram solved by the present application in scenario 1 of the present invention;

FIG. 4 is a vehicle-mounted cooperative inspection path diagram solved by using a 2E-GUCRP model in scenario 1 in the embodiment of the present invention;

FIG. 5 is a vehicle-machine cooperative inspection path diagram solved by the present application under scenario 2 in the embodiment of the present invention;

fig. 6 is a vehicle-machine cooperative inspection path diagram solved by using a 2E-GUCRP model in scenario 2 in the embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but 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 embodiment of the application provides a path planning method and system for cooperative inspection of a vehicle and an unmanned aerial vehicle, solves the problem that the path planning result of cooperative inspection of the vehicle and the unmanned aerial vehicle cannot be quickly and accurately solved in the prior art, and achieves the purpose of scientifically and effectively guiding the cooperative inspection work of the vehicle and the unmanned aerial vehicle.

In order to solve the technical problems, the general idea of the embodiment of the application is as follows:

in order to accurately and quickly solve the vehicle-machine cooperative inspection path planning result, the method comprises the steps of firstly obtaining relevant parameters including unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters, road network parameters and the like in the vehicle-machine cooperative inspection process; then presetting related constraint conditions including routing inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint, unmanned aerial vehicle and ground vehicle access terminal constraint and the like according to the real situation of the vehicle-machine cooperative routing inspection, and constructing a vehicle-machine cooperative routing inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches the terminal after finishing routing inspection of all target points as a target function based on related parameters; and finally, solving the constructed vehicle-machine cooperative inspection path planning model, and finally obtaining the optimal planning scheme of the vehicle-machine cooperative inspection path.

In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.

Referring to fig. 1, a scenario of the cooperative inspection by the vehicle-mounted device may be described as follows:

an unmanned aerial vehicle is carried to a vehicle and is started from the starting point, ground vehicle traveles along the road network, release unmanned aerial vehicle at suitable stop, unmanned aerial vehicle flies the target point outside the road network, patrol and examine it, patrol and examine a plurality of target points as far as under the prerequisite that unmanned aerial vehicle continued the journey allows, then converge with ground vehicle at suitable stop once more, change the battery, unmanned aerial vehicle takes off repeatedly, patrol and examine the target point, descend the process of changing the battery, until patrolling and examining all target points, final ground vehicle and unmanned aerial vehicle are in the set of final point.

In the technical solution of the present invention, the following assumptions are made:

1) the speed of both the vehicle and drone is constant;

2) the time for taking off and landing the unmanned aerial vehicle from the vehicle is ignored;

3) the time for replacing the battery of the unmanned aerial vehicle on the vehicle is ignored;

4) the vehicle may wait for the drone at the stop and not vice versa.

Example 1:

in a first aspect, the present invention first provides a path planning method for cooperative inspection of a vehicle and an unmanned aerial vehicle, referring to fig. 2, the method includes:

s1, acquiring vehicle-machine cooperative inspection related parameters, wherein the vehicle-machine cooperative inspection related parameters comprise: unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters;

s2, acquiring preset vehicle-machine cooperative inspection constraint conditions, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time of the unmanned aerial vehicle to reach a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal point constraint;

and S3, solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

Therefore, the vehicle-mounted cooperative inspection related parameters are obtained; then presetting a vehicle-machine cooperative inspection constraint condition, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the related parameters; and finally, solving the constructed vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path. Compared with the prior art, the method can quickly and accurately obtain the feasible solution, and can obtain the optimal scheme of the vehicle-machine cooperative routing inspection path planning based on the feasible solution, so that the related work of vehicle-machine routing inspection can be scientifically and effectively guided.

The following describes the implementation of one embodiment of the present invention in detail with reference to the explanation of specific steps S1-S3.

S1, acquiring vehicle-machine cooperative inspection related parameters, wherein the vehicle-machine cooperative inspection related parameters comprise: unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters, and road network parameters.

Setting relevant parameters in the cooperative inspection process of the vehicle machine, wherein the relevant parameters are as follows:

unmanned aerial vehicle parameter, it includes: velocity v of unmanned aerial vehicle₁And the longest endurance time theta of the unmanned aerial vehicle;

a ground vehicle parameter, comprising: ground vehicle speed v₂；

And the road network parameters comprise a coordinate set of nodes in the vehicle-machine cooperative inspection process. A path graph in the vehicle-machine cooperative inspection process is represented by G ═ (V, E), as shown in fig. 3-6, where E represents a set of all edges, which are connecting lines between nodes; v denotes a set of all nodes, V ═ 0 { [ u ] } V {_s∪{*}∪V_t(ii) a Wherein {0} denotes a starting point, V _s1, 2.. m } represents the set of all available waypoints, i.e., a total of m waypoints; { } denotes the end point, here denoted m + 1; v_tA { m +2, m + 3.., m + n +1} represents a set of all target points, i.e., there are n target points in total;

patrol and examine task parameter, it includes: patrol time C of each patrolled target point_i(ii) a Where i represents the number of the target point being patrolled and i ∈ V_t。

S2, acquiring preset vehicle-machine cooperative inspection constraint conditions, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time of the unmanned aerial vehicle to reach a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal constraint.

In order to obtain an optimal vehicle-machine cooperation inspection path planning scheme in a real scene, the vehicle-machine cooperation inspection path planning model is constructed by taking the shortest total time when the unmanned aerial vehicle starts from the starting point and inspects all target points to be inspected and reaches the end point as an objective function based on the related parameters in the vehicle-machine cooperation inspection process. Specifically, the objective function can be formulated as:

minz＝t_*

wherein, t_*And the total time consumption of the unmanned aerial vehicle from the starting point and reaching the end point after all the target points are inspected is shown.

Meanwhile, in order to ensure the scientificity of the vehicle-machine cooperative inspection path planning model and the effectiveness and the rationality of a solution result when the model is solved, the following constraint conditions are set for the vehicle-machine cooperative inspection path planning model objective function:

this constraint ensures that each selectable stop is visited at most once;

the constraint indicates that the in-degree of the end point is equal to the out-degree of the start point and is equal to 1;

the constraint indicates that the out-degree of the end point is equal to the in-degree of the start point and is equal to 0;

m-1≥T_i-T_j+m×x_ij，i∈{0}∪V_s,j∈{*}∪V_sthe constraint is a cancellation of the sub-loop constraint (MTZ constraint), a cancellation of the sub-loop of the route of the vehicle; t is_iRepresenting the visit sequence of vehicles of the intersection node i, and being a continuous digital variable;

this constraint ensures that all target points are visited only once;

M×(1-y_ij)≥|C_j+d_ij/v₁-Q_j|+S_ji-1|，i∈{0}∪V_s,j∈V_tthe constraint describes the takeoff process of the drone, S when the drone takes off from node i, flying to node j_ji1, the total consumption time of the unmanned aerial vehicle at the target point j is equal to the service time of the point j and the flight time between the nodes;

M×(2-y_ij-S_ik)≥|Q_i+C_j+d_ij/v₁-Q_j|+|S_ik-S_jk|，i,j∈V_t,k∈{0}∪V_sthe order of

The beam describes the flight of the drone between target points;

M×(1-y_ij)≥(Q_i+d_ij/v₁-θ)，i∈V_t,j∈{0}∪V_su {. the }, the constraint describes the landing process of the unmanned aerial vehicle from the target point i to the docking point j;

M×(2-y_ij-S_ik)≥|x_kj-1|，i∈V_t,j,k∈{0}∪V_sj ≠ k, which ensures that if an drone lands on a certain stop, that stop must be on the path of the ground vehicle;

M×(3-y_ij-x_kj-S_ik)≥(d_kj/v₂-d_ij/v₁-Q_i)，i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which

Constraints limit that ground vehicles must reach a designated landing junction before unmanned aerial vehicles;

y_ij＝0，i,j∈{0}∪V_su {. the }, this constraint ensures that the unmanned aerial vehicle will not fly on the road network;

this constraint ensures that the stop point reached by the ground vehicle must be the node of takeoff or landing of the drone;

M×(2-yij-S_ij)≥|s_j-Q_i-d_ij/v₁|，i∈V_t,j∈{0}∪V_sthe constraint represents the time for waiting for the return of the unmanned aerial vehicle in situ after the ground vehicle releases the unmanned aerial vehicle at a certain docking point; s_iRepresenting the time when the vehicle waits for the unmanned aerial vehicle at the intersection node i;

M×(3-yij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-s_j|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which is to calculate the waiting time of the vehicle at the stop point j;

M×(1-x_ij)≥t_i-t_j+d_ij/v₂，i∈{0}∪V_s,j∈{*}∪V_si ≠ j, which guarantees the consistency of the time of the ground vehicle leaving the junction node and the sequence of the junction node; t is t_iThe time when the vehicle departs from the intersection node i from the starting point is represented;

M×(1-x_ij)≥t_i+d_ij/v₂+s_j-t_j，i∈{0}∪V_s,j∈V_sthe constraint ensures that the ground vehicle runs from a docking point i to a docking point j, and the running time is less than or equal to the time when the vehicle reaches the docking point j;

the constraint indicates that the unselected anchor point t has a value of 0;

the constraint indicates that each target node of the drone is assigned to a specific flight segment;

this constraint ensures that S is only when i is the target point_ijIs effective;

0≤Q_i≤θ，i∈V_tthe constraint is the endurance constraint of the unmanned aerial vehicle;

T₀1, the constraint is the value of T of the initialization start node;

t₀＝s₀the constraint describes a special scenario, i.e. the drone takes off from the starting point, patrols the target point, and then returns to the starting point.

In particular, except for the constraint conditions, the vehicle-mounted device collaborative inspection constraint conditions preset in the application further include:

and (3) forbidding loop constraint of the routing inspection target point path:

M×(1-y_ij)≥(Q_i+C_j-Q_j)，i,j∈V_ti ≠ j, which guarantees that if the drone flies from target point i to target point j, the drone takes off from a certain stop point until the drone has patrolled target point i_iPatrol time C with unmanned aerial vehicle at target point j_jThe sum of the time Q of the unmanned aerial vehicle from the takeoff of a certain stop point to the inspection completion of the target point j is less than or equal to_jPreventing the flying path of the unmanned aerial vehicle from being in a condition of i → j → i;

unmanned aerial vehicle departure point restraint includes:

this constraint ensures that if an unmanned aerial vehicle takes off from a certain stop point i, that stop point must be on the path of the ground vehicle;

the ground vehicle stop point time constraints include:

M×(3-y_ij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-Q_i|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which

Constraint guarantees that if the unmanned aerial vehicle takes off from the stop point k, the patrol target point i, lands to the stop point j, and the ground vehicle runs from the stop point k to the stop point j, the time t when the ground vehicle leaves the stop point j is_jEqual to the time t at which the ground vehicle leaves the stopping point k_kAnd the time Q from the takeoff of the unmanned aerial vehicle from the stop point k to the completion of the inspection of the target point i_iTime d for unmanned aerial vehicle to fly from target point i to stop point j_ij/v₁The sum of the three;

M×(1-x_ij)≥|t_j-t_i-d_ij/v₂-s_j|,i∈{0}∪V_s,j∈V_s{. j, i ≠ j, which guarantees the waiting time s of the ground vehicle at the docking point j if the ground vehicle travels from the docking point i to the docking point j_jEqual to the moment t at which the ground vehicle leaves the stop point j_jWith the time (t) at which the ground vehicle reaches the stop point j_i+d_ij/v₂) The difference between the two; wherein, t_iRepresents the time when the ground vehicle leaves the stopping point i;

M×(3-x_ij-S_ki-y_kq)≥t_q-t_j,i∈{0}∪V_s,j,q∈V_s∪{*},k∈V_tthe constraint ensures that if the ground vehicle travels from stop point i to stop point j, the unmanned aerial vehicle takes off from stop point i, inspects the target point k, and lands on stop point q, then the time t when the ground vehicle leaves stop point j_jAt a time t equal to or greater than the time when the ground vehicle leaves the stopping point q_qThat is, the unmanned aerial vehicle can continue to take off only after landing at a certain stop point;

unmanned aerial vehicle and ground vehicle access terminal constraints include:

x_*i＝0，i∈{0}∪V_su {. the }, the constraint ensures that the ground vehicle terminates the driving state at the terminal point;

the constraint ensures that the drone does not take off from the endpoint;

the constraint ensures that the sum of the times of flying the unmanned aerial vehicle from the target point i to the terminal point is less than or equal to 1;

in addition, the constraint condition of the cooperative inspection of the preset vehicle machine further comprises:

x_ii＝0，i∈{0}∪V_su {. X }, which ensures that the ground vehicle cannot circularly run in situ at the docking point i;

Q_i＝0，i∈{0}∪V_su {. X }, the constraint ensures that Q is only when i is the target point_iIs valid, otherwise Q_iThe value of (d) is 0.

Wherein, in the above constraint, M represents a positive integer close to infinity; v. of₁Representing the speed of the drone; v. of₂Representing the speed of the ground vehicle; d_ijRepresenting the Euclidean distance between the node i and the node j; q_iThe time from the takeoff of the unmanned aerial vehicle from a certain stop point to the completion of the inspection of the target point i is represented; t is_iIndicating the order in which the stop points i are visited by the ground vehicle, as a continuous digital variable, T₀＝1；t_iThe time point of departure of the ground vehicle from the starting point 0 and leaving the stopping point i is represented; s_iRepresenting the time for the ground vehicle to wait for the unmanned aerial vehicle at a stop point i; {0} represents a starting point; v_sRepresenting all selectable sets of waypoints; { } denotes end point; v_tRepresenting a set of all target points; c_iRepresenting the time that the target point i needs to be inspected by the unmanned aerial vehicle, and i belongs to V_t；x_ij、y_ij、S_ijAll represent decision variables, specifically:

and S3, solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

Under the constraint conditions, the vehicle-machine cooperation inspection path planning model is solved by using methods such as a CPLEX solver and the like, the total time of the unmanned aerial vehicle reaching the end point after the unmanned aerial vehicle finishes inspecting all target points in the vehicle-machine cooperation process can be obtained according to the planned path corresponding to each solved solution and the flight speed of the unmanned aerial vehicle, and the total time corresponding to all solutions is compared, wherein the total time is shortest (namely t is t_*) The path planning scheme corresponding to the solution is an optimal planning scheme of the vehicle-machine cooperative routing inspection path, namely the optimal path planning scheme refers to the path planning scheme corresponding to the shortest total time when the unmanned aerial vehicle reaches the end point after finishing routing inspection of all target points. Specifically, the optimal path planning scheme includes the sequence of the target points for the unmanned aerial vehicle to patrol, the sequence numbers of the alternative stop points where the unmanned aerial vehicle stops, the sequence of the stop points, and the like. After the optimal path planning scheme is obtained, the inspection work of the vehicle-mounted equipment can be executed according to the scheme.

Therefore, the whole process of the path planning method for the cooperative inspection of the vehicle and the unmanned aerial vehicle is completed.

In order to verify the effectiveness and the high efficiency of the solution of the invention, the technical scheme is compared with the prior art in different scenes.

The method comprises the steps of setting unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters in the vehicle-machine cooperative inspection process, generating a small-scale example based on the road network parameters, wherein a simulation area of the small-scale example is 100X 100, then randomly generating coordinates of all required nodes in the area, expressing the coordinates of the nodes by (X, Y), the speed of the unmanned aerial vehicle is 2 units/second, the speed of the ground vehicle is 1 unit/second, the maximum endurance time of the unmanned aerial vehicle is 100 units, then solving the example by using a CPLEX solver based on preset constraint conditions, and verifying the effectiveness of the method based on the solving result.

1) And verifying the validity of the invention, namely verifying whether the invention can accurately solve the valid solution.

Scene 1: the number of the stop points is set to be 2, and the number of the target points is set to be 4.

TABLE 1 set of Start, stop, and end points in scene 1

	X coordinate	Y coordinate
			Starting point 0	30	77
Docking point 1	86	40
			Docking point 2	48	57
End point 3	96	41

Table 2 set of target points in scenario 1

Target point	X coordinate	Y coordinate	Inspection time (second)
				4	3	37	7.49
5	63	46	9.61
				6	82	76	10.63
7	38	47	7.16

Referring to FIG. 3, a square represents a set of stop points, wherein 0 is a starting point, 3 is an end point, and 1 and 2 are both stop points; circles 4, 5, 6 and 7 are target points for unmanned aerial vehicle inspection; the solid line is the ground vehicle route of traveling, and the dotted line is the route of unmanned aerial vehicle flight. The ground vehicle carries the unmanned aerial vehicle and starts from the starting point (0 point in the figure), and unmanned aerial vehicle releases from the starting point, crosses with ground vehicle at stop point 2 after patrolling and examining target point 4 and 7 in succession, changes the battery after, flies from 2 points again, patrols and examines target point 5 and 6 in succession, and the car goes to 2 points from the starting point in step, traveles to the terminal point again, and finally the car meets with unmanned aerial vehicle at the terminal point (3 points in the figure).

The technical scheme is utilized to solve the scene 1, and an optimal path planning scheme is obtained as shown in fig. 3: wherein, the square represents a set of stop points, wherein 0 is a starting point, 3 is an end point, and 1 and 2 are both stop points; circles 4, 5, 6 and 7 are target points for unmanned aerial vehicle inspection; the solid line is the ground vehicle route of traveling, and the dotted line is the route of unmanned aerial vehicle flight. The ground vehicle carries the unmanned aerial vehicle and starts from the starting point (0 point in the figure), and unmanned aerial vehicle releases from the starting point, crosses with ground vehicle at stop point 2 after patrolling and examining target point 4 and 7 in succession, changes the battery after, flies from 2 points again, patrols and examines target point 5 and 6 in succession, and the car goes to 2 points from the starting point in step, traveles to the terminal point again, and finally the car meets with unmanned aerial vehicle at the terminal point (3 points in the figure). Under the path planning scheme, the time spent by the unmanned aerial vehicle when all the routing inspection target points reach the destination is 130.195 seconds. Meanwhile, when the optimal path planning scheme is solved by using the vehicle-mounted machine collaborative path planning model of the technical scheme, the running time of a CPU (namely the time spent by a computer in solving the optimal solution) is 1.24 seconds.

In the same scenario (that is, using the node information in the same node list and the vehicle-mounted Cooperative Routing related parameters), when a 2E-GUCRP (Two-Echelon group and its mobile station Cooperative Routing protocol) model in the prior art is operated, an obtained path planning scheme is shown in fig. 4. Wherein, ground vehicle carries unmanned aerial vehicle to travel from starting point 0 to stop 1, and travel to terminal point 3 again, 3 unmanned aerial vehicle take off the back at terminal point, patrol and examine 5, 6 in succession, behind 7 number target points, descend to stop 1, target point 4 number, unmanned aerial vehicle carries out solitary patrol and examine to it, y promptly₄₄1. However, the above path planning scheme violates the constraint that the drone prohibits takeoff from the terminal point, and the constraint of a prohibited loop between the target points, and is therefore not feasible.

Scene 2: the number of the stop points is set to be 4, and the number of the target points is set to be 4.

TABLE 3 set of Start, stop, and end points in scene 2

	X coordinate	Y coordinate
			Starting point 0	17	5
Docking point 1	55	36
			Docking point 2	59	93
Docking point 3	55	6
			Docking point 4	38	42
End point 5	33	58

Table 4 set of target points in scenario 2

Target point	X coordinate	Y coordinate	Inspection time (second)
				6	91	10	7.64
7	74	19	8.69
				8	16	28	7.45
9	70	29	7.52

Referring to fig. 5, in the figure, squares are stop points, where 0 is a starting point, 5 is an end point, 1,2, 3, and 4 are stop points, and circles 6, 7, 8, and 9 are target points. In the figure, a solid line is a ground vehicle running path, and a dotted line is an unmanned aerial vehicle flight path. The ground vehicle carries the unmanned aerial vehicle and starts from the starting point, and unmanned aerial vehicle releases from the starting point simultaneously, and it intersects with ground vehicle at stop point 3 after patrolling and examining target point 8, changes the battery, and then takes off from 3 points, patrols and examines target point 6, 7, 9 in succession, and the car goes to 3 points from the starting point in step, and then goes to the terminal point, finally converges with unmanned aerial vehicle at the terminal point.

Similarly, the solution is performed for the scene 2, and the optimal path planning scheme is as follows: and the unmanned aerial vehicle executes a path planning scheme corresponding to 121.816 seconds of time spent when all the routing inspection target points reach the end point. Meanwhile, when the optimal path planning scheme is solved by using the vehicle-mounted machine collaborative path planning model in the technical scheme, the running time of the CPU is 3.22 seconds.

In the same scenario (that is, the node information in the same node list and the vehicle-mounted cooperative inspection related parameters are adopted), when the 2E-GUCRP model in the prior art is operated, the solved path planning scheme is shown in fig. 6. Wherein, ground vehicle carries unmanned aerial vehicle and traveles to berth point 4 from starting point 0, it arrives berth point 5 to travel again, after 5 unmanned aerial vehicles take off at berth point, 3 unmanned aerial vehicle's the route of patrolling and examining appear simultaneously, unmanned aerial vehicle's first flight path is that unmanned aerial vehicle patrols and examines target point 6, 9 in succession, descend to 0 number of starting point, unmanned aerial vehicle's second flight path is that unmanned aerial vehicle patrols and examines descending to 0 number of starting point behind target point 7, unmanned aerial vehicle's third route is that unmanned aerial vehicle patrols and examines target point 8 and descend to 4 number of berth points. The path planning scheme described above violates a number of constraints and is therefore not feasible.

Therefore, the method and the device can accurately calculate the vehicle-mounted machine cooperative inspection path planning scheme, and can solve the problems that effective solutions cannot be calculated or solution results are inaccurate in the prior art.

2) The high efficiency of the solution of the invention is verified, and the effective solution can be rapidly solved.

In order to further verify that the technical scheme has higher efficiency and higher speed when solving the optimal path plan compared with the prior art, the embodiment further increases the number of experimental groups.

TABLE 5 solving time table for different node numbers

In table 5, the CPU running times in six different scenarios (first to sixth groups) were solved using the model of the present invention, and the corresponding scenarios were consistent with those in the 2E-GUCRP model. The CPU running time represents the time spent by the computer to solve the optimal vehicle-mounted machine cooperative routing scheme under the condition of different stop points and target points. As can be seen from the above table, when the same problem is solved, compared with the 2E-GUCRP model in the prior art, the solution time spent by the computer is shorter, which proves that the path planning scheme for the cooperative inspection by the in-vehicle machine can be solved more efficiently and quickly.

In conclusion, compared with the prior art, the method can more quickly and accurately solve the effective solution of the vehicle-mounted device cooperative routing inspection path planning.

Example 2:

in a second aspect, the present invention further provides a path planning system for collaborative inspection of a vehicle and an unmanned aerial vehicle, the system includes:

the system comprises a parameter acquisition module, a data processing module and a data processing module, wherein the parameter acquisition module is used for acquiring vehicle-machine cooperative inspection related parameters, and the vehicle-machine cooperative inspection related parameters comprise unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters;

the model construction module is used for acquiring preset vehicle-machine cooperative inspection constraint conditions and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal point constraint;

and the model solving module is used for solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

Optionally, the parameters of the unmanned aerial vehicle in the parameter obtaining module include the speed of the unmanned aerial vehicle and the maximum duration of the unmanned aerial vehicle;

the ground vehicle parameters include ground vehicle speed;

the inspection task parameters comprise the time that each target point needs to be inspected by the unmanned aerial vehicle;

the road network parameters comprise a node coordinate set in the vehicle-machine cooperation inspection process, and the nodes comprise starting points, stop points, end points and target points.

Optionally, the objective function includes:

minz＝t_*

wherein, t_*And the total time consumption of the unmanned aerial vehicle from the starting point and reaching the end point after all the target points are inspected is shown.

Optionally, the routing inspection target point path prohibition loop constraint includes:

M×(1-y_ij)≥(Q_i+C_j-Q_j)，i,j∈V_ti ≠ j, which guarantees that if the drone flies from target point i to target point j, the drone takes off from a certain stop point until the drone has patrolled target point i_iPatrol time C with unmanned aerial vehicle at target point j_jThe sum of the time Q of the unmanned aerial vehicle from the takeoff of a certain stop point to the inspection completion of the target point j is less than or equal to_jPreventing the flying path of the unmanned aerial vehicle from being in a condition of i → j → i;

the unmanned aerial vehicle take-off point constraint includes:

this constraint ensures that if an unmanned aerial vehicle takes off from a certain stop point i, that stop point must be on the path of the ground vehicle;

the ground vehicle stop point time constraints include:

M×(3-y_ij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-Q_i|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which

Constraint guarantees if drone is fromTaking off from the stop point k, inspecting the target point i, landing to the stop point j, driving the ground vehicle from the stop point k to the stop point j, and leaving the stop point j at the time t_jEqual to the time t at which the ground vehicle leaves the stopping point k_kAnd the time Q from the takeoff of the unmanned aerial vehicle from the stop point k to the completion of the inspection of the target point i_iTime d for unmanned aerial vehicle to fly from target point i to stop point j_ij/v₁The sum of the three;

M×(1-x_ij)≥|t_j-t_i-d_ij/v₂-s_j|,i∈{0}∪V_s,j∈V_s{. j, i ≠ j, which guarantees the waiting time s of the ground vehicle at the docking point j if the ground vehicle travels from the docking point i to the docking point j_jEqual to the moment t at which the ground vehicle leaves the stop point j_jWith the time (t) at which the ground vehicle reaches the stop point j_i+d_ij/v₂) The difference between the two; wherein, t_iRepresents the time when the ground vehicle leaves the stopping point i;

M×(3-x_ij-S_ki-y_kq)≥t_q-t_j,i∈{0}∪V_s,j,q∈V_s∪{*},k∈V_tthe constraint ensures that if the ground vehicle travels from stop point i to stop point j, the unmanned aerial vehicle takes off from stop point i, inspects the target point k, and lands on stop point q, then the time t when the ground vehicle leaves stop point j_jAt a time t equal to or greater than the time when the ground vehicle leaves the stopping point q_qThat is, the unmanned aerial vehicle can continue to take off only after landing at a certain stop point;

the drone and ground vehicle access endpoint constraints include:

x_*i＝0，i∈{0}∪V_su {. the }, the constraint ensures that the ground vehicle terminates the driving state at the terminal point;

the constraint ensures that the drone does not take off from the endpoint;

the constraint ensures that the sum of the times of flying the unmanned aerial vehicle from the target point i to the terminal point is less than or equal to 1;

the preset vehicle-mounted machine collaborative inspection constraint condition further comprises:

x_ii＝0，i∈{0}∪V_su {. X }, which ensures that the ground vehicle cannot circularly run in situ at the docking point i;

Q_i＝0，i∈{0}∪V_su {. X }, the constraint ensures that Q is only when i is the target point_iIs valid, otherwise Q_iIs 0;

wherein M represents a positive integer approaching infinity;

v₁representing the speed of the drone;

v₂representing the speed of the ground vehicle;

d_ijrepresenting the Euclidean distance between the node i and the node j;

Q_ithe time from the takeoff of the unmanned aerial vehicle from a certain stop point to the completion of the inspection of the target point i is represented;

T_iindicating the order in which the stop points i are visited by the ground vehicle, as a continuous digital variable, T₀＝1；

s_iRepresenting the time for the ground vehicle to wait for the unmanned aerial vehicle at a stop point i;

t_iindicating the moment at which the ground vehicle leaves the stopping point i, t₀＝s₀；

{0} represents a starting point;

V_srepresenting all selectable sets of waypoints; { } denotes end point; v_tRepresenting a set of all target points;

C_irepresenting the time that the target point i needs to be inspected by the unmanned aerial vehicle, and i belongs to V_t；

x_ij、y_ij、S_ijAll represent decision variables, specifically:

optionally, the solving of the vehicle-machine cooperative inspection path planning model by the model solving module includes:

and solving the vehicle-machine cooperative inspection path planning model by using a CPLEX solver.

It can be understood that the path planning system for collaborative inspection of the vehicle and the unmanned aerial vehicle provided by the embodiment of the invention corresponds to the path planning method for collaborative inspection of the vehicle and the unmanned aerial vehicle, and the explanation, the example, the beneficial effects and the like of the relevant contents can refer to the corresponding contents in the path planning method for collaborative inspection of the vehicle and the unmanned aerial vehicle, and the details are not repeated here.

In summary, compared with the prior art, the method has the following beneficial effects:

1. the method comprises the steps of obtaining vehicle-mounted machine collaborative inspection related parameters; then presetting a vehicle-machine cooperative inspection constraint condition, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the related parameters; and finally, solving the constructed vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path. Compared with the prior art, the method can quickly and accurately calculate the feasible solution, and can obtain the optimal scheme of the vehicle-machine cooperative routing inspection path planning based on the feasible solution, so that the related work of vehicle-machine routing inspection can be scientifically and effectively guided;

2. the method constructs the vehicle-machine cooperative routing inspection path planning model by taking the shortest total time of the unmanned aerial vehicle when the unmanned aerial vehicle reaches the end point after finishing the routing inspection of all the target points as an objective function, and simultaneously presets constraint conditions such as routing inspection target point path forbidding loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint, unmanned aerial vehicle and ground vehicle access end point constraint and the like when constructing the model, so that the accuracy of solution and the rapidity of solution can be ensured.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A path planning method for cooperative inspection of a vehicle and an unmanned aerial vehicle is characterized by comprising the following steps:

acquiring vehicle-machine cooperative inspection related parameters, wherein the vehicle-machine cooperative inspection related parameters comprise unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters;

acquiring preset vehicle-machine cooperative inspection constraint conditions, and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal point constraint;

and solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

2. The method of claim 1, wherein the drone parameters include drone speed and a maximum time of flight for the drone;

the ground vehicle parameters include ground vehicle speed;

the inspection task parameters comprise the time that each target point needs to be inspected by the unmanned aerial vehicle;

the road network parameters comprise a node coordinate set in the vehicle-machine cooperation inspection process, and the nodes comprise starting points, stop points, end points and target points.

3. The method of claim 1, wherein the objective function comprises:

minz＝t_*

wherein, t_*And the total time consumption of the unmanned aerial vehicle from the starting point and reaching the end point after all the target points are inspected is shown.

4. The method of claim 1, wherein the patrol target point path inhibit loop constraints comprise:

M×(1-y_ij)≥(Q_i+C_j-Q_j)，i,j∈V_ti ≠ j, which guarantees that if the drone flies from target point i to target point j, the drone takes off from a certain stop point until the drone has patrolled target point i_iPatrol time C with unmanned aerial vehicle at target point j_jThe sum of the time Q of the unmanned aerial vehicle from the takeoff of a certain stop point to the inspection completion of the target point j is less than or equal to_jPreventing the flying path of the unmanned aerial vehicle from being in a condition of i → j → i;

the unmanned aerial vehicle take-off point constraint includes:

this constraint ensures that if an unmanned aerial vehicle takes off from a certain stop point i, that stop point must be on the path of the ground vehicle;

the ground vehicle stop point time constraints include:

M×(3-y_ij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-Q_i|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which guarantees that if the drone takes off from a stop point k, patrols a target point i, lands to a stop point j, while the ground vehicle travels from the stop point k to the stop point j, then the time t at which the ground vehicle leaves the stop point j_jEqual to the time t at which the ground vehicle leaves the stopping point k_kAnd the time Q from the takeoff of the unmanned aerial vehicle from the stop point k to the completion of the inspection of the target point i_iTime d for unmanned aerial vehicle to fly from target point i to stop point j_ij/v₁The sum of the three;

M×(1-x_ij)≥|t_j-t_i-d_ij/v₂-s_j|,i∈{0}∪V_s,j∈V_s{. j, i ≠ j, which guarantees the waiting time s of the ground vehicle at the docking point j if the ground vehicle travels from the docking point i to the docking point j_jEqual to the moment t at which the ground vehicle leaves the stop point j_jWith the time (t) at which the ground vehicle reaches the stop point j_i+d_ij/v₂) The difference between the two; wherein, t_iRepresents the time when the ground vehicle leaves the stopping point i;

M×(3-x_ij-S_ki-y_kq)≥t_q-t_j,i∈{0}∪V_s,j,q∈V_s∪{*},k∈V_tthe constraint ensures that if the ground vehicle travels from stop point i to stop point j, the unmanned aerial vehicle takes off from stop point i, inspects the target point k, and lands on stop point q, then the time t when the ground vehicle leaves stop point j_jAt a time greater than or equal to the time when the ground vehicle leaves the stopping point qt_qThat is, the unmanned aerial vehicle can continue to take off only after landing at a certain stop point;

the drone and ground vehicle access endpoint constraints include:

x_*i＝0，i∈{0}∪V_su {. the }, the constraint ensures that the ground vehicle terminates the driving state at the terminal point;

the constraint ensures that the drone does not take off from the endpoint;

the constraint ensures that the sum of the times of flying the unmanned aerial vehicle from the target point i to the terminal point is less than or equal to 1;

the preset vehicle-mounted machine collaborative inspection constraint condition further comprises:

x_ii＝0，i∈{0}∪V_su {. X }, which ensures that the ground vehicle cannot circularly run in situ at the docking point i;

Q_i＝0，i∈{0}∪V_su {. X }, the constraint ensures that Q is only when i is the target point_iIs valid, otherwise Q_iIs 0;

wherein M represents a positive integer approaching infinity;

v₁representing the speed of the drone;

v₂representing the speed of the ground vehicle;

d_ijrepresenting the Euclidean distance between the node i and the node j;

Q_ithe time from the takeoff of the unmanned aerial vehicle from a certain stop point to the completion of the inspection of the target point i is represented;

T_iindicating the order in which the stop points i are visited by the ground vehicle, as a continuous digital variable, T₀＝1；

s_iRepresenting the time for the ground vehicle to wait for the unmanned aerial vehicle at a stop point i;

t_iindicating land vehiclesTime of departure from stop point i, t₀＝s₀；

{0} represents a starting point;

V_srepresenting all selectable sets of waypoints; { } denotes end point; v_tRepresenting a set of all target points;

C_irepresenting the time that the target point i needs to be inspected by the unmanned aerial vehicle, and i belongs to V_t；

x_ij、y_ij、S_ijAll represent decision variables, specifically:

5. the method of claim 1, wherein the solving the in-vehicle collaborative inspection path planning model comprises:

and solving the vehicle-machine cooperative inspection path planning model by using a CPLEX solver.

6. The utility model provides a path planning system that vehicle and unmanned aerial vehicle were patrolled and examined in coordination, its characterized in that, the system includes:

the system comprises a parameter acquisition module, a data processing module and a data processing module, wherein the parameter acquisition module is used for acquiring vehicle-machine cooperative inspection related parameters, and the vehicle-machine cooperative inspection related parameters comprise unmanned aerial vehicle parameters, ground vehicle parameters, inspection task parameters and road network parameters;

the model construction module is used for acquiring preset vehicle-machine cooperative inspection constraint conditions and constructing a vehicle-machine cooperative inspection path planning model by taking the shortest total time when the unmanned aerial vehicle reaches a destination after finishing the inspection of all target points as a target function based on the vehicle-machine cooperative inspection related parameters; the preset vehicle-machine cooperative inspection constraint conditions comprise inspection target point path forbidden loop constraint, unmanned aerial vehicle flying point constraint, ground vehicle stop point time constraint and unmanned aerial vehicle and ground vehicle access terminal point constraint;

and the model solving module is used for solving the vehicle-machine cooperative inspection path planning model to obtain an optimal planning scheme of the vehicle-machine cooperative inspection path.

7. The system of claim 6, wherein the drone parameters in the parameter acquisition module include drone speed and maximum endurance time of the drone;

the ground vehicle parameters include ground vehicle speed;

the inspection task parameters comprise the time that each target point needs to be inspected by the unmanned aerial vehicle;

the road network parameters comprise a node coordinate set in the vehicle-machine cooperation inspection process, and the nodes comprise starting points, stop points, end points and target points.

8. The system of claim 6, wherein the objective function comprises:

minz＝t_*

wherein, t_*And the total time consumption of the unmanned aerial vehicle from the starting point and reaching the end point after all the target points are inspected is shown.

9. The system of claim 6, wherein the patrol target point path inhibit loop constraints comprise:

M×(1-y_ij)≥(Q_i+C_j-Q_j)，i,j∈V_ti ≠ j, which guarantees that if the drone flies from target point i to target point j, the drone takes off from a certain stop point until the drone has patrolled target point i_iPatrol time C with unmanned aerial vehicle at target point j_jThe sum of the time Q of the unmanned aerial vehicle from the takeoff of a certain stop point to the inspection completion of the target point j is less than or equal to_jTo prevent fromThe flight path of the unmanned aerial vehicle is in a condition of i → j → i;

the unmanned aerial vehicle take-off point constraint includes:

this constraint ensures that if an unmanned aerial vehicle takes off from a certain stop point i, that stop point must be on the path of the ground vehicle;

the ground vehicle stop point time constraints include:

M×(3-y_ij-x_kj-S_ik)≥|t_j-t_k-d_ij/v₁-Q_i|,i∈V_t,j∈{*}∪V_s,k∈{0}∪V_sj ≠ k, which guarantees that if the drone takes off from a stop point k, patrols a target point i, lands to a stop point j, while the ground vehicle travels from the stop point k to the stop point j, then the time t at which the ground vehicle leaves the stop point j_jEqual to the time t at which the ground vehicle leaves the stopping point k_kAnd the time Q from the takeoff of the unmanned aerial vehicle from the stop point k to the completion of the inspection of the target point i_iTime d for unmanned aerial vehicle to fly from target point i to stop point j_ij/v₁The sum of the three;

M×(1-x_ij)≥|t_j-t_i-d_ij/v₂-s_j|,i∈{0}∪V_s,j∈V_s{. j, i ≠ j, which guarantees the waiting time s of the ground vehicle at the docking point j if the ground vehicle travels from the docking point i to the docking point j_jEqual to the moment t at which the ground vehicle leaves the stop point j_jWith the time (t) at which the ground vehicle reaches the stop point j_i+d_ij/v₂) The difference between the two; wherein, t_iRepresents the time when the ground vehicle leaves the stopping point i;

M×(3-x_ij-S_ki-y_kq)≥t_q-t_j,i∈{0}∪V_s,j,q∈V_s∪{*},k∈V_tthe constraint ensures that if the ground vehicle travels from stop point i to stop point j, the unmanned aerial vehicle takes off from stop point i, inspects the target point k, and lands on stop point q, then the ground vehicle leaves the stopTime t of point j_jAt a time t equal to or greater than the time when the ground vehicle leaves the stopping point q_qThat is, the unmanned aerial vehicle can continue to take off only after landing at a certain stop point;

the drone and ground vehicle access endpoint constraints include:

x_*i＝0，i∈{0}∪V_su {. the }, the constraint ensures that the ground vehicle terminates the driving state at the terminal point;

the constraint ensures that the drone does not take off from the endpoint;

the constraint ensures that the sum of the times of flying the unmanned aerial vehicle from the target point i to the terminal point is less than or equal to 1;

the preset vehicle-mounted machine collaborative inspection constraint condition further comprises:

x_ii＝0，i∈{0}∪V_su {. X }, which ensures that the ground vehicle cannot circularly run in situ at the docking point i;

Q_i＝0，i∈{0}∪V_su {. X }, the constraint ensures that Q is only when i is the target point_iIs valid, otherwise Q_iIs 0;

wherein M represents a positive integer approaching infinity;

v₁representing the speed of the drone;

v₂representing the speed of the ground vehicle;

d_ijrepresenting the Euclidean distance between the node i and the node j;

Q_ithe time from the takeoff of the unmanned aerial vehicle from a certain stop point to the completion of the inspection of the target point i is represented;

T_iindicating the order in which the stop points i are visited by the ground vehicle, as a continuous digital variable, T₀＝1；

s_iIndicating that the ground vehicle is waiting for nobody at stop point iThe time of the machine;

t_iindicating the moment at which the ground vehicle leaves the stopping point i, t₀＝s₀；

{0} represents a starting point;

V_srepresenting all selectable sets of waypoints; { } denotes end point; v_tRepresenting a set of all target points;

C_irepresenting the time that the target point i needs to be inspected by the unmanned aerial vehicle, and i belongs to V_t；

x_ij、y_ij、S_ijAll represent decision variables, specifically:

10. the system of claim 6, wherein the model solution module to solve the in-vehicle collaborative inspection path planning model comprises:

and solving the vehicle-machine cooperative inspection path planning model by using a CPLEX solver.

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