Unmanned aerial vehicle signal sequential logic task motion planning method based on sequential decomposition

Abstract

The invention provides an unmanned aerial vehicle signal sequential logic task motion planning method based on sequence decomposition, and the method comprises the steps: firstly, proposing a sequence decomposition strategy, decomposing a task of an unmanned aerial vehicle arriving at a destination according to a time sequence, and solving a time sequence of the unmanned aerial vehicle; a sequence decomposition strategy enables the growth rate of a predicate function of each stage to be constrained by a control barrier function so as to ensure the feasibility of reaching a task, and a system with a relative order of 2 can be processed; and secondly, according to the time sequence characteristics of the signal time logic task, a dynamic constraint activation strategy is provided, the calculation amount of the convex optimization problem can be reduced, and the conservative property of the method is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle path planning, and in particular relates to a method for unmanned aerial vehicle signal timing logic task motion planning based on sequence decomposition. Background Art

[0002] With the continuous advancement of computing chips and sensor technology, drones are becoming more and more powerful in all aspects, and research on drones is of great significance. Drone motion planning is an important part of drone research. Traditional methods usually only require reaching a specified target point, and the overall task is relatively simple, and it is difficult to handle complex time-constrained signal sequential logic (STL). As a subfield of sequential logic, STL is very suitable for handling access and obstacle avoidance problems with specific spatiotemporal attributes of drones.

[0003] The most basic STL motion planning method is the mixed integer linear programming (MILP) method. In each time step, a binary variable is introduced for each predicate, the STL constraints are encoded in the form of MILP, the optimal control problem is converted into a mixed integer linear programming problem, and the optimal path of the UAV is iteratively obtained. The MILP method has exponential time complexity and is difficult to solve quickly when the mission time is long. In order to solve this problem, the control obstacle function (CBF) method was proposed. This method encodes each STL constraint into a one-to-one corresponding control obstacle function, converts the STL constraint into a constraint solving convex optimization problem of the control quantity, and thus obtains the UAV motion path.

[0004] However, the disadvantage of the MILP method is that when the time span of the task increases, the number of binary variables will increase exponentially, significantly increasing the computational burden and making it difficult to implement in real time in complex dynamic scenarios. The CBF method has difficulty handling systems with a relative order higher than 1. Summary of the invention

[0005] To solve the above problems, the present invention provides a UAV signal temporal logic task motion planning method based on sequence decomposition, which realizes motion planning under STL constraints for UAVs with relative orders higher than 1 while having low computational requirements.

[0006] A method for UAV signal temporal logic task motion planning based on sequence decomposition includes the following steps:

[0007] S1: Divide the UAV's path planning task into an obstacle avoidance task sequence and access task sequences Among them, K O is the number of obstacles, K O The obstacle avoidance subtask corresponding to each obstacle, K d is the number of destinations, K d The visit subtasks corresponding to the destinations;

[0008] S2: Based on the relaxed quadratic programming, the control amount u(t) of the drone when performing each obstacle avoidance subtask and each access subtask is obtained to complete the motion planning. The control amount u(t) is obtained as follows:

[0009] (u(t),δ(t))=min u(t),δ(t) u(t) 2 +pδ 2 (t)

[0010]

[0011] u min ≤u(t)≤u max

[0012] Among them, δ(t) is the slack variable, p is the set weight, and u min is the lower limit of the control quantity, u max is the upper limit of the control amount. is the indication value when the UAV performs the k1th obstacle avoidance subtask, where but like but a k1 is the start time of the k1th obstacle avoidance subtask, is the end time of the k1th obstacle avoidance subtask, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k1th obstacle avoidance subtask, express The second-order Lie derivative along f(x), and f(x) = [Vcosθ, Vsinθ, 0] T , θ is the direction of the UAV, V is the linear velocity of the UAV, express Lie derivative along g(x), g(x) = [0,0,1] T , For The relevant 0th-order barrier function is for The coefficient of for The index of is an odd number, For The first-order barrier function is for The coefficient of for The index of is an odd number, is the indication value when the UAV performs the k2th access subtask, where but like but is a predicate function used to characterize that the rate of increase of the distance between the current position of the UAV and the final target position is less than the set value when executing the k2th access subtask. express Lie derivative along f(x), express Lie derivative along g(x), For The relevant 0th-order barrier function is for The coefficient of for The index of is a predicate function used to characterize that the rate of increase of the distance between the current position of the UAV and the final target position is not less than the set value when executing the k2th access subtask. express Lie derivative along f(x), express Lie derivative along g(x), For The relevant 0th-order barrier function is for The coefficient of for The index of .

[0013] Furthermore, the predicate function The calculation method is as follows:

[0014]

[0015] Among them, x p (t) is the position coordinate of the UAV at time t, The position coordinates of the obstacles when the drone performs k1 obstacle avoidance tasks. The radius of the obstacle when the UAV performs k1 obstacle avoidance subtasks;

[0016] and The 0th-order barrier function The calculation formula is as follows:

[0017]

[0018] and The first-order barrier function The calculation formula is as follows:

[0019]

[0020] in, for The derivative of .

[0021] Furthermore, the predicate function The calculation formula is:

[0022]

[0023] in, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k2th access subtask, and t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0, is the center coordinate of the k2th destination, is the radius of the k2th destination, for The derivative of , indicating the growth rate of the predicate function;

[0024] and The 0th-order barrier function The calculation formula is:

[0025]

[0026] Furthermore, the predicate function The calculation formula is:

[0027]

[0028] in, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k2th access subtask, and t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0, is the center coordinate of the k2th destination, is the radius of the k2th destination, for The derivative of , indicating the growth rate of the predicate function;

[0029] and The 0th-order barrier function The calculation formula is:

[0030]

[0031] Beneficial effects:

[0032] The present invention provides a method for motion planning of unmanned aerial vehicle signal timing logic tasks based on sequence decomposition. The present invention provides a method for motion planning of unmanned aerial vehicle signal timing logic tasks based on sequence decomposition. Firstly, a sequence decomposition strategy is proposed, and the task of the unmanned aerial vehicle reaching the destination is decomposed in chronological order. The sequence decomposition strategy makes the growth rate of the predicate function of each stage subject to the constraint of the control barrier function to ensure the feasibility of the arrival task, and can handle systems with a relative order of 2; secondly, the present invention also proposes a dynamic constraint activation strategy according to the timing characteristics of the signal time logic task, which can reduce the calculation amount of the convex optimization problem and reduce the conservatism of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flowchart of a method for UAV signal timing logic task motion planning based on sequence decomposition provided by the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0035] like Figure 1 As shown, a method for UAV signal timing logic task motion planning based on sequence decomposition includes the following steps:

[0036] S1: Divide the UAV's path planning task into an obstacle avoidance task sequence and access task sequences Among them, K O is the number of obstacles, K O The obstacle avoidance subtask corresponding to each obstacle, K d is the number of destinations, K d The visit subtasks corresponding to the destinations;

[0037] S2: Based on the relaxed quadratic programming, the control amount u(t) of the drone when performing each obstacle avoidance subtask and each access subtask is obtained to complete the motion planning. The control amount u(t) is obtained as follows:

[0038] (u(t),δ(t))=min u(t),δ(t) u(t) 2 +pδ 2 (t)

[0039]

[0040] u min ≤u(t)≤u max

[0041] Among them, δ(t) is the slack variable, p is the set weight, and u min is the lower limit of the control quantity, u max is the upper limit of the control amount. is the indication value when the UAV performs the k1th obstacle avoidance subtask, where but like but is the start time of the k1th obstacle avoidance subtask, b k1 is the end time of the k1th obstacle avoidance subtask, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k1th obstacle avoidance subtask, express The second-order Lie derivative along f(x), and f(x) = [Vcosθ, Vsinθ, 0] T , θ is the direction of the UAV, V is the linear velocity of the UAV, express Lie derivative along g(x), g(x) = [0,0,1] T , For The relevant 0th-order barrier function is for The coefficient of for The index of is an odd number, For The first-order barrier function is for The coefficient of for The index of is an odd number, is the indication value when the UAV performs the k2th access subtask, where but like but is a predicate function used to characterize that the rate of increase of the distance between the current position of the UAV and the final target position is less than the set value when executing the k2th access subtask. express Lie derivative along f(x), express Lie derivative along g(x), For The relevant 0th-order barrier function is for The coefficient of for The index of is a predicate function used to characterize that the rate of increase of the distance between the current position of the UAV and the final target position is not less than the set value when executing the k2th access subtask. express Lie derivative along f(x), express Lie derivative along g(x), For The relevant 0th-order barrier function is for The coefficient of for The index of .

[0042] That is to say, the present invention divides the path planning tasks of the UAV into two categories: obstacle avoidance tasks and access tasks. The positions and sizes of obstacles and target areas are determined in the workspace (x, y) of the UAV, and converted into STL constraints; for the obstacle avoidance task, a high-order control obstacle function is designed to convert the STL constraints into control quantity constraints; for the access task, each STL constraint is converted into two corresponding sub-constraints by sequence decomposition, and then the control quantity is constrained by the control obstacle function method; in order to reduce the computational burden and reduce the conservatism of the method, a dynamic activation mechanism is proposed; when an STL constraint is no longer needed, the control quantity constraint corresponding to the STL constraint is deactivated in the solver; finally, all activated control quantity constraints are considered at the same time, and the control quantity is obtained by solving a relaxed QP problem; the control quantity is applied to the actuator of the UAV, and the UAV motion path that satisfies the STL constraint can be obtained.

[0043] It should be noted that signal sequential logic is a predicate logic. The predicate function will be evaluated as "true" or "false" at each moment. In the present invention, the predicate function is specified as a function that measures the distance between the drone and a specific point. This specific point may be an obstacle or the center point of the destination.

[0044] Furthermore, the predicate function The calculation method is as follows:

[0045]

[0046] Among them, x p (t) is the position coordinate of the UAV at time t, The position coordinates of the obstacles when the drone performs k1 obstacle avoidance tasks. The radius of the obstacle when the UAV performs k1 obstacle avoidance subtasks;

[0047] and The 0th-order barrier function The calculation formula is as follows:

[0048]

[0049] and The first-order barrier function The calculation formula is as follows:

[0050]

[0051] in, for The derivative of .

[0052] Furthermore, the predicate function The calculation formula is:

[0053]

[0054] in, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k2th access subtask, and t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0, is the center coordinate of the k2th destination, is the radius of the k2th destination, for The derivative of , indicating the growth rate of the predicate function;

[0055] and The 0th-order barrier function The calculation formula is:

[0056]

[0057] Furthermore, the predicate function The calculation formula is:

[0058]

[0059] in, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k2th access subtask, and t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0, is the center coordinate of the k2th destination, is the radius of the k2th destination, for The derivative of , indicating the growth rate of the predicate function;

[0060] and The 0th-order barrier function The calculation formula is:

[0061]

[0062] The process of obtaining the control quantity u(t) is deduced in detail below.

[0063] The dynamic model of the drone is constructed as follows:

[0064]

[0065] Among them, x p =[xy] T represents the position of the UAV, θ represents its direction, V>0 represents its linear velocity, and u represents the control amount, that is, the angular velocity of the UAV.

[0066] An STL constraint φ can be defined using the following STL syntax:

[0067]

[0068]

[0069] in, Indicates that the task specification φ is satisfied at some time steps between time t+a and time t+b. Similarly, It means that the task specification φ is satisfied at every time step between time t+a and time t+b.

[0070] In step S1, assume that there are K O obstacles and K d destination, the UAV path planning task φ is divided into the obstacle avoidance task φ obs and access task φ des Two categories, namely

[0071] φ=φ obs ∧φ des

[0072] in, is the STL constraint representing the obstacle avoidance task, is an STL constraint that represents an access task.

[0073] Considering the kth obstacle avoidance task, the drone must always avoid obstacles within the specified time, which is consistent with the definition of the time operator G. Assume xp (t) represents the position of the drone. The obstacles in the working plane are not circular obstacles. The kth obstacle is located at x o,k , whose radius is r o,k , need to be in [a k ,b k ] time period, then the obstacle avoidance STL constraint for this obstacle can be written as:

[0074]

[0075] The corresponding predicate function is

[0076]

[0077] The relative order of this predicate function to the system dynamics equations is 2.

[0078] Define the following high-order barrier function, The class function is set to the power function.

[0079]

[0080] in, and is an odd number, Then, the following control quantity constraints can be obtained:

[0081]

[0082] in, Representation function Lie derivative along the vector field f(x). Similarly, express The second-order Lie derivative along f(x), express Along the Lie derivative of g(x), as long as the control quantity satisfies this constraint, it is ensured that the UAV can avoid the obstacle area; for multiple obstacle avoidance tasks, it is necessary to consider the control quantity constraints corresponding to them at the same time.

[0083] Consider the k1th access task , the drone should arrive at the specified location within the specified time, which is consistent with the definition of time operators F and U. It is worth noting that U can be expressed as a combination of G and F, and the processing method of G has been discussed, so the following will focus on the analysis of F.

[0084] Given an STL constraint for accessing a task:

[0085]

[0086] in, Respectively represent the start time and end time of the constraint. Represents the center and radius of the target area, and the corresponding predicate function is:

[0087]

[0088] t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0. The relative order of this predicate function to the system dynamics equation is 2.

[0089] At the initial moment, To satisfy , we propose a sequence decomposition strategy to control the growth rate of the function value so that At certain moments in the time interval,

[0090] Consider the following evaluation function to measure Growth rate:

[0091]

[0092] Then according to the time constraint Decomposed into two sub-constraints:

[0093]

[0094] in

[0095]

[0096] The predicate function corresponding to the sub-constraint is:

[0097]

[0098] It is worth noting that the relative order of the predicate function of the sub-constraint to the system dynamics equation is 1. Therefore, the present invention can define the barrier function as:

[0099]

[0100] in, and is an odd number, Then, the following control quantity constraints can be obtained:

[0101]

[0102] As long as the control quantity satisfies this constraint, it is guaranteed that the UAV can reach the target area. For multiple access tasks, the control quantity constraints corresponding to them need to be considered simultaneously.

[0103] In order to reduce the amount of calculation and conservatism, the present invention proposes a dynamic activation mechanism, which cancels the activation state of the constraint when the constraint is no longer needed (ie, when the target position has been reached or obstacle avoidance is no longer needed).

[0104] Consider the obstacle avoidance task in, And k1∈{1,…,K o}, based on the control quantity constraints mentioned above, a dynamic activation mechanism is added as follows:

[0105]

[0106] in,

[0107]

[0108] Each access task will be activated and processed in the order of time constraints, and only one task will be processed at any time. Consider the access task in, And k2∈{1,…,K d}. At t = 0, only φ is decomposed d,1 , ignoring other constraints. d,1 The corresponding control quantity constraint can be written as:

[0109]

[0110] If φ d,1 Not completed, i1 will always be equal to 1. d,1 When completed, the completion time t1 and the system state x(t1) are recorded and i1 is set to 0. Then, the present invention decomposes φ d,2 :

[0111]

[0112] The corresponding control quantity constraint is:

[0113]

[0114] If φ d,2 Not completed, i2 will always be equal to 1, φ d,2 When completed, record the completion time t2 and system state x(t2) and set i2 to 0. By repeating the above process, the drone can complete the visit tasks in sequence.

[0115] For the total task φ = φ obs ∧φ des , the present invention also considers φ obs and φ desA relaxed quadratic program is formulated to generate the controller u(t) to ensure the feasibility of the quadratic program and satisfy the STL constraints:

[0116] (u(t),δ(t))=min u(t),δ(t) u(t) 2 +pδ 2 (t)

[0117]

[0118] u min ≤u(t)≤u max

[0119] Among them, δ(t)>0 is a slack variable, and p>0 is its weight. The present invention only adds a relaxation factor to the control quantity constraint inequality corresponding to the access task. Since -δ(t)<0, this can relax the constraints on the control quantity of the access problem to a certain extent, so that the system state may slightly violate the STL constraint of the access task, but it can make it easier to find a feasible solution to the optimization problem. The present invention believes that ensuring the safety of the system itself has the highest priority, so no relaxation factor is added to the inequality corresponding to the obstacle avoidance task. The optimization problem is convex and depends on only two decision variables.

[0120] In summary, the present invention provides a UAV signal timing logic task motion planning method based on sequence decomposition. First, a sequence decomposition strategy is proposed to decompose the task of the UAV to reach the destination in chronological order. The sequence decomposition strategy makes the growth rate of the predicate function of each stage subject to the constraint of the control barrier function to ensure the feasibility of the arrival task, and can handle systems with a relative order of 2; secondly, the present invention also proposes a dynamic constraint activation strategy based on the timing characteristics of the signal time logic task, which can reduce the computational complexity of the convex optimization problem and reduce the conservatism of the present invention.

[0121] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may certainly make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for UAV signal temporal logic task motion planning based on sequence decomposition, characterized in that: The following steps are involved: S1: Divide the UAV's path planning task into an obstacle avoidance task sequence and access task sequences Among them, K O is the number of obstacles, K O The obstacle avoidance subtask corresponding to each obstacle, K d is the number of destinations, K d The visit subtasks corresponding to the destinations; S2: Based on the relaxed quadratic programming, the control amount u(t) of the drone when performing each obstacle avoidance subtask and each access subtask is obtained to complete the motion planning. The control amount u(t) is obtained as follows: (u(t),δ(t))=min u(t),δ(t) u(t) 2 +pδ 2 (t) Among them, δ(t) is the slack variable, p is the set weight, and u min is the lower limit of the control quantity, u max is the upper limit of the control amount. is the indication value when the UAV performs the k1th obstacle avoidance subtask, where but like but is the start time of the k1th obstacle avoidance subtask, is the end time of the k1th obstacle avoidance subtask, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k1th obstacle avoidance subtask, express The second-order Lie derivative along f(x), and f(x) = [Vcosθ, Vsinθ, 0] T , θ is the direction of the UAV, V is the linear velocity of the UAV, express Lie derivative along g(x), g(x) = [0,0,1] T , For The relevant 0th-order barrier function is for The coefficient of for The index of is an odd number, For The first-order barrier function is for The coefficient of for The index of is an odd number, is the indication value when the UAV performs the k2th access subtask, where but like but is a predicate function used to characterize that the rate of increase of the distance between the current position of the UAV and the final target position is less than the set value when executing the k2th access subtask. express Lie derivative along f(x), express Lie derivative along g(x), For The relevant 0th-order barrier function is for The coefficient of for The index of is a predicate function used to characterize that the rate of increase of the distance between the current position of the UAV and the final target position is not less than the set value when executing the k2th access subtask. express Lie derivative along f(x), express Lie derivative along g(x), For The relevant 0th-order barrier function is for The coefficient of for The index of .

2. A method for UAV signal temporal logic task motion planning based on sequence decomposition as claimed in claim 1, characterized in that: Predicate Function The calculation method is as follows: Among them, x p (t) is the position coordinate of the UAV at time t, The position coordinates of the obstacles when the drone performs k1 obstacle avoidance tasks. The radius of the obstacle when the UAV performs k1 obstacle avoidance subtasks; and The 0th-order barrier function The calculation formula is as follows: and The first-order barrier function The calculation formula is as follows: in, for The derivative of .

3. The method for UAV signal temporal logic task motion planning based on sequence decomposition as claimed in claim 1, characterized in that: Predicate Function The calculation formula is: in, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k2th access subtask, and t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0, is the center coordinate of the k2th destination, is the radius of the k2th destination, for The derivative of , indicating the growth rate of the predicate function; and The 0th-order barrier function The calculation formula is:

4. The method for UAV signal temporal logic task motion planning based on sequence decomposition as claimed in claim 1, characterized in that: Predicate Function The calculation formula is: in, is the predicate function used to characterize the distance between the current position of the UAV and the final target position when executing the k2th access subtask, and t0 is the activation time of the k2th access subtask, x p (t0) is the position coordinate of the drone at the activation time t0, is the center coordinate of the k2th destination, is the radius of the k2th destination, for The derivative of , indicating the growth rate of the predicate function; and The 0th-order barrier function The calculation formula is:

Images