Time-Energy Based UAV Autonomous Landing Path Planning Method and System

Abstract

The present invention proposes a method and system for autonomous landing path planning of an unmanned aerial vehicle based on time - energy. The method includes: constructing an autonomous landing model with optimal time - energy; designing an autonomous tracking mechanism based on the autonomous landing model: based on the target trajectory prediction sequence, generating local targets through rolling space and performing local replanning, taking the local targets as target positions, and obtaining the unmanned aerial vehicle trajectory search sequence through a search algorithm; generating an unmanned aerial vehicle target trajectory prediction sequence based on the unmanned aerial vehicle trajectory search sequence; designing an autonomous landing mechanism: designing time and dynamic constraints, and considering the terminal constraints in autonomous landing, optimizing the autonomous landing model; solving the autonomous landing model through NLOPT; finally, initializing the parameters and completing the landing through an autonomous landing finite - state machine including the autonomous tracking mechanism and the autonomous landing mechanism. The present invention can adapt to different sites and environments and adapt to different conditions by adjusting the planning parameters or algorithms.

Description

Technical Field

[0001] The present invention relates to the autonomous landing path planning technology of unmanned aerial vehicles, and specifically relates to a method and system for autonomous landing path planning of unmanned aerial vehicles based on time - energy. Background Art

[0002] The technical background of the autonomous landing of unmanned aerial vehicles on mobile platforms involves multiple fields such as high - precision positioning, dynamic environment perception, motion prediction, and control algorithms. With the continuous expansion of the application scenarios of unmanned aerial vehicles, especially the increasing demand for landing on dynamic platforms such as maritime ships and mobile vehicles, the autonomous landing technology has become a research hotspot. However, there are the following problems in the autonomous landing on mobile platforms: First, how to land smoothly and quickly on the dynamic platform. An unmanned aerial vehicle is a typical under - actuated system, and the state of the landing platform is unknown and time - varying, which introduces cross - dependent time and space conditions for the trajectory of the unmanned aerial vehicle. To cope with this situation, the unmanned aerial vehicle needs to flexibly adjust its flight time during the trajectory generation process. Second, how to cope with sudden changes in complex situations. A mobile platform with sudden motion changes and severe external disturbances will cause the original planned trajectory to be abandoned. In order to enable the unmanned aerial vehicle to land quickly in a complex environment, high - frequency replanning is essential. Third, how to ensure the safety of landing during high - frequency planning.

[0003] Limited by navigation technology and on - board computing capabilities, for autonomous landing, the vision - servo - based method is the easiest to implement and the most maturely applied method. This method directly plans the unmanned aerial vehicle at the control level, directly constructs the state deviation term based on the target observation, and then uses PID or a controller designed based on the second method of Lyapunov to complete the autonomous landing of the unmanned aerial vehicle. The vision - servo - based landing method can capture the landing marker in real time and execute the landing, with the characteristics of high accuracy and strong independence. However, this method often ignores environmental information and is short - sighted, making it difficult to introduce state constraints related to the landing platform, having high requirements for the environment, requiring sufficient light and clarity, and also needing to be able to identify a suitable landing area. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for autonomous landing path planning of unmanned aerial vehicles based on time - energy, which can meet the dynamic constraints of vertical take - off and landing unmanned aerial vehicles, can adapt to different sites and environments, and can adapt to different conditions by adjusting planning parameters or algorithms.

[0005] To achieve the above - mentioned purpose, the technical solution adopted by the present invention is as follows:

[0006] A method for autonomous landing path planning of unmanned aerial vehicles based on time - energy, comprising the steps of:

[0007] Construct an autonomous landing model with optimal time - energy;

[0008] Design an autonomous tracking mechanism based on an autonomous landing model to generate an autonomous tracking path for the UAV, including:

[0009] Generate local targets through a rolling space and perform local replanning, use the local targets as target positions, and obtain a UAV trajectory search sequence through a search algorithm;

[0010] Based on the UAV trajectory search sequence, generate a UAV target trajectory prediction sequence;

[0011] Design an autonomous landing mechanism to achieve UAV landing, including:

[0012] Design time and dynamic constraints, and consider terminal constraints in autonomous landing to optimize the autonomous landing model;

[0013] Solve the autonomous landing model through NLOPT;

[0014] Initialize the motion planning parameters, and complete the landing through an autonomous landing finite state machine including the autonomous tracking mechanism and the autonomous landing mechanism.

[0015] A UAV autonomous landing path planning system based on time - energy, including:

[0016] An autonomous landing model construction unit for constructing an autonomous landing model with optimal time - energy;

[0017] An autonomous tracking mechanism design unit, which designs an autonomous tracking mechanism based on the autonomous landing model to generate an autonomous tracking path for the UAV, including: generating local targets through a rolling space and performing local replanning, using the local targets as target positions, and obtaining a UAV trajectory search sequence through a search algorithm; generating a UAV target trajectory prediction sequence based on the UAV trajectory search sequence

[0018] An autonomous landing mechanism design unit for designing an autonomous landing mechanism to achieve UAV landing, including: designing time and dynamic constraints, considering terminal constraints in autonomous landing, optimizing the autonomous landing model, and solving the autonomous landing model through NLOPT;

[0019] A landing unit that initializes the motion planning parameters and completes the landing through an autonomous landing finite state machine including the autonomous tracking mechanism and the autonomous landing mechanism.

[0020] Compared with the prior art, the remarkable features of the present invention are:

[0021] (1) The motion planning algorithm proposed in the present invention considers the time adjustability, can dynamically adjust the motion trajectory according to environmental changes or task requirements, and improves the system's ability to handle emergencies;

[0022] (2) The motion planning algorithm proposed in the present invention is based on the trajectory optimization method of MINCO, and can perform spatio-temporal joint optimization on path points and motion time;

[0023] (3) The motion planning algorithm proposed in the present invention can be flexibly embedded in the state machine. When adding new states or transitions, only the corresponding code needs to be added, without significantly modifying the existing logic, which improves the scalability of the system;

[0024] (4) The trajectory generation algorithm proposed in the present invention is designed for vertical takeoff and landing unmanned aerial vehicles, and can meet the autonomous landing of various vertical takeoff and landing unmanned aerial vehicles, including ordinary rotor unmanned aerial vehicles and compound configuration vertical takeoff and landing unmanned aerial vehicles;

[0025] (5) The motion planning algorithm proposed in the present invention simultaneously considers the initial state constraint and the terminal state constraint of the unmanned aerial vehicle, adopts a hierarchical framework, and iteratively generates a trajectory to improve the efficiency of the autonomous landing of the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the flow chart of the unmanned aerial vehicle landing path planning and optimization algorithm based on time-energy of the present invention.

[0027] Figure 2 is the flow chart of the finite state machine for autonomous landing of the mobile platform of the present invention.

[0028] Figure 3 is the schematic diagram of the local planning strategy in the present invention.

[0029] Figure 4 is the schematic diagram of two scenarios of replanning caused by collision and fixed-time replanning in the present invention.

[0030] Figure 5 is the comparison chart of the conventional A* algorithm and the A* algorithm extended by dynamics, Figure 5 in which (a) is the search schematic diagram of the conventional A* algorithm, Figure 5 in which (b) is the search schematic diagram of the A* algorithm extended by dynamics.

[0031] Figure 6 is the schematic diagram of three situations of the unmanned aerial vehicle landing in the present invention.

[0032] Figure 7 is different in the present invention when the landing degree of the curve schematic diagram. DETAILED DESCRIPTION OF THE INVENTION

[0033] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0034] This invention conducts research on the trajectory generation of the autonomous landing of unmanned aerial vehicles (UAVs) on mobile platforms, and proposes an autonomous landing path planning and optimization method considering time adjustability. The method uses the MINCO trajectory class to plan the path of the UAV for autonomous landing on the mobile platform. This method can meet the dynamic constraints of vertical takeoff and landing UAVs, combined with Figure 1 , and specifically includes the following steps:

[0035] Step1. Construct an autonomous landing finite state machine, receive the control signal for starting the landing task or set the waiting time for starting the landing task, and assist the UAV to execute the autonomous tracking task according to the UAV trajectory search sequence and the target trajectory prediction sequence of the autonomous tracking mechanism, and complete the autonomous landing according to the autonomous landing mechanism;

[0036] Step2. Construct a time - energy optimal autonomous landing model for smoothly transitioning the UAV from the normal flight state to the motion state consistent with the target;

[0037] Step3. Design an autonomous tracking mechanism based on the autonomous landing model to generate the UAV autonomous tracking path, including:

[0038] Step3 - 1. Generate the local target of the autonomous landing model based on the rolling space, execute local replanning, and obtain the UAV trajectory search sequence through the path search algorithm;

[0039] Step3 - 2. Use the time - energy optimal moving target trajectory prediction algorithm to determine the target trajectory prediction sequence ;

[0040] Step 4. Design an autonomous landing mechanism to achieve the UAV landing, including:

[0041] Step 4 - 1. Optimize the autonomous landing model through time and dynamic constraints;

[0042] Step 4 - 2. Solve the autonomous landing model equation through NLOPT; use NLOPT to solve the unconstrained nonlinear optimization problem. By the soft constraint method, convert the hard constraints into penalty functions and add them to the objective function to simplify the problem. Select the NLOPT_LD_LBFGS algorithm for solution, and convert the solved trajectory into the desired state acceptable to the UAV controller through the differential flatness tool to achieve precise control;

[0043] Step 5. Initialize the motion planning parameters for autonomous landing, execute the autonomous landing finite state machine in Step 1, and perform the autonomous landing mechanism and autonomous tracking mechanism during the execution process to achieve autonomous landing.

[0044] The finite state machine is a mathematical model that divides the control target into finite states to simplify algorithm design. Generally, a state machine consists of three parts: a finite state set, an input set, and a state transition set. The autonomous landing task of the drone involved in this patent is controlled by a state machine on the ROS system. After receiving a signal sent by the system or reaching the startup time, this state machine is responsible for starting the planner and controlling the drone.

[0045] After receiving the control signal to start the landing task or when the waiting time for starting the landing task is reached, the autonomous landing finite state machine starts the autonomous landing path planning and controls the drone; when the autonomous landing path planning is started, the drone enters the waiting state. In the waiting state, the drone can receive human commands; the end moment of the waiting state is taken as the start moment of landing, that is, the initial moment; after the drone enters the waiting state, it starts to receive target information. If the planar distance between the drone and the target is greater than the tracking distance , then the drone is controlled to execute the autonomous tracking task, and the drone autonomously tracks the target to shorten the distance between the drone and the target until the planar distance between the drone and the target is less than or equal to the landing distance ; when the planar distance between the drone and the target is less than or equal to the landing distance , then the drone is controlled to execute the autonomous landing task to complete the autonomous landing task of the drone; when the drone encounters unexpected situations (such as optimization failure, the drone approaching an obstacle) during the autonomous tracking task or the autonomous landing task, then the drone is controlled to enter the hover state, and the drone maintains hovering.

[0046] According to Figure 2 As shown, after the program starts, the drone enters the waiting state, which is the fixed initial state of the drone. In the waiting state, the drone can receive human commands. Once the task start command is received, the state machine checks whether the target information is received. If the planar distance between the drone and the mobile platform is greater than the tracking distance , it will enter the autonomous tracking state. The autonomous tracking state is responsible for completing the tracking task and does not need to return to the waiting state.

[0047] Furthermore, for the landing task of the mobile platform of a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV), the planning objective of the autonomous landing model is to smoothly transition the UAV from the normal flight state to a motion consistent with the target motion. In this process, it is necessary to satisfy the dynamic differential constraints, boundary constraints, and process constraints determined by the dynamic equations, and at the same time consider the control cost and landing duration to achieve time - energy optimality. The autonomous landing model is modeled as a constrained optimization problem, with the optimization objective being to minimize the control cost and landing duration. Utilizing the differential flatness property of the UAV, a chained integral linear system is used to simplify the dynamic differential constraints. Finally, under the conditions of satisfying the boundary constraints and process constraints, the optimal control cost is solved to ensure the efficiency and stability of the UAV landing process. Therefore, the landing motion planning of the UAV can be regarded as a constrained optimization problem. The objective function of the autonomous landing model is described as:

[0048] (1)

[0049] where: is a tuning parameter, represents the control cost of the UAV, represents the landing duration, represents the weight matrix, and represent equality constraints and inequality constraints respectively. The equality constraints include dynamic differential constraints and boundary constraints, and the inequality constraints include process constraints;

[0050] The first equality constraint is the dynamic differential constraint of the system. Utilizing the differential flatness property of the UAV, a chained integral linear system is used to simplify the dynamic differential constraint:

[0051] (2)

[0052] where: represents the state of the UAV at time represents the first derivative of , , represents a zero matrix of rows and columns, represents the position of the UAV at time , and represent in the direction, direction, and The position in the , and respectively represent the first, second, and third derivatives of represents the jump of the UAV, and the value is , which is used to simplify the control cost ; the system input in Equation (2) uses the third derivative of displacement , and the objective function of the autonomous landing model is simplified to:

[0053] (3)

[0054] The boundary constraints are expressed as:

[0055] (4)

[0056] where: represents the start time of landing, that is, the initial time; represents the state of the UAV at the start time of landing, that is, the initial state of the UAV; represents the completion time of landing, represents the state of the UAV at the completion time of landing;

[0057] The process constraints regarding speed and acceleration are expressed as:

[0058] (5)

[0059] where: represents the speed of the UAV at time, represents the maximum speed constraint of the UAV during the landing process; represents the acceleration of the UAV at time, represents the maximum acceleration constraint of the UAV during the landing process;

[0060] The process constraints regarding the time trajectory are expressed as:

[0061] (6)

[0062] where: represents the th time trajectory segment. The start time of landing to the completion time of landing is divided into time trajectory segments according to the MINCO curve. Denote the time trajectory sequence as , and the duration of each trajectory segment must be greater than 0.

[0063] In the UAV landing mission, since the UAV is close to the landing platform, the influence of environmental constraints on its motion planning is usually not considered; in addition, the objective function contains the high-order derivatives of the trajectory, which indirectly reflects the smoothness of the trajectory. Therefore, the smoothness constraint can be ignored in the optimization process. In summary, the optimization problem during landing is determined by the objective function in Equation (3), the equality constraints by Equations (4) and (2), and the inequality constraints by Equations (5) and (6).

[0064] Furthermore, optimize the autonomous landing model through time and dynamic constraints, specifically including:

[0065] Optimize the process constraint of the objective function with respect to velocity to , and optimize the process constraint of the objective function with respect to acceleration to . Only the maximum resultant velocity and resultant acceleration are penalized during the landing process, so the process constraint of the objective function with respect to velocity and acceleration is optimized to :

[0066] (7)

[0067] (8)

[0068] Where: and are the weights of velocity penalty and acceleration penalty respectively;

[0069] Divide the landing duration into a fixed time and a relaxation time , ; for the relaxation time , introduce an optimization duration , and optimize the relaxation time to ; for the fixed time , introduce a maximum duration , and optimize the fixed time to ; then optimize the landing duration to , and set the initial value of the MINCO curve time trajectory sequence to ;

[0070] Define the unconstrained virtual time series as , and perform a diffeomorphic mapping on the rd time trajectory segment to obtain the th unconstrained virtual time :

[0071] (9)

[0072] (10)

[0073] Optimize the process constraint of the objective function with respect to the time trajectory into , and use to represent the conversion of the time trajectory sequence into an unconstrained virtual time series , then we have:

[0074] (11)

[0075] where: represents the sequence of path points of the MINCO curve. Here, belongs to the prior art, and the derivation process will not be given in detail here.

[0076] Propose a time and dynamics constraint optimization method. By defining an unconstrained virtual time , convert the constrained time variable into an unconstrained optimization problem. Use the diffeomorphic mapping to achieve the conversion between and . After the optimization is completed, the actual time is obtained by backtracking. To adapt to different scenarios, the total landing trajectory time is divided into a fixed part and a relaxation part, and the maximum value of the fixed time is adjusted in real time during the optimization. At the same time, only the maximum resultant velocity and resultant acceleration are penalized for the velocity and acceleration constraints to ensure the flexibility and feasibility of the trajectory planning.

[0077] The trajectory planning takes the local target as the end point and the current position of the UAV as the starting point for searching. Considering the limited sensing range of the UAV, the planning outside the rolling space is inefficient. Therefore, the path search and trajectory optimization are restricted to be carried out within the rolling space.

[0078] Furthermore, the local targets for generating the autonomous landing model based on the rolling space specifically include: before the start of the path search, set a global target for the mobile platform that needs to land. When the global target is not within the rolling space ( Figure 3 the dashed circle in Figure 3 ), connect the global target with the current position of the UAV, and set the intersection point of the connection line and the rolling space as the local target, as shown in

[0079] The position and velocity of the local target can be obtained by solving the BVP and then using formula (12). The so-called rolling space is centered on the current position of the UAV andA circular projection area with a radius; taking the current position of the target as the global target, determine whether the global target is within the rolling space: if it is within the rolling space, then take the global target as the target position, obtain the position of the UAV at the next moment through a search algorithm, and then form a UAV trajectory search sequence ; if it is not within the rolling space, then first connect the current position of the UAV to the global target, take the intersection point of the connection line and the rolling space as the local target, take the local target as the target position, obtain the position of the UAV at the next moment through a search algorithm, and then form a UAV trajectory search sequence .

[0080] During the execution of the trajectory, the local occupancy grid map is updated using the projection method, and the update frequency is set manually according to the hardware conditions. There are two scenarios that will trigger replanning. First, when the planning module obtains updated grid map data, it determines the safety of the currently executing trajectory based on the latest map. If it is found that the current trajectory intersects or collides with an obstacle, replanning will be triggered to ensure the safety of the UAV. Second, within a fixed time interval, the motion planning module is called to recalculate the tracking points according to the time-energy optimal moving target trajectory prediction algorithm to update the global target and the local target, so that the motion planning can adapt to changes in the moving target or task requirements. Figure 4 Shows these two scenarios of replanning.

[0081] When executing the path search algorithm to obtain the UAV trajectory search sequence the optimal control cost of the UAV is obtained by solving the boundary value :[[]]END]]

[0082] (12)

[0083] Where:[[]]END]] represents the optimal state of the UAV at time represents a 9-dimensional co-state vector, respectively represent the dimensional co-state variables; , , , , , , , and represent the unknown constants to be solved in the direction, direction and direction respectively, represents the Hamiltonian function, and the expression is:[[]]END]]

[0084] (13)

[0085] Wherein: is an adjustment parameter; 、 and respectively represent the third-order, second-order, and first-order derivatives of the displacement , that is, the snap, acceleration, and velocity of the UAV. The subscripts 、 and respectively represent direction, direction, and direction.

[0086] Furthermore, when obtaining the UAV trajectory search sequence , the search algorithm searches for the UAV's next moment position based on the UAV's current position, and then forms the UAV trajectory search sequence ; the search algorithm expands the third-order chained dynamics model and simultaneously considers the UAV's cost function based on the A* algorithm;

[0087] In the path search algorithm, uses motion primitives as graph edges, expands based on the third-order chained dynamics model, and then generates motion primitives. Due to the differential flatness characteristic of the UAV, only the flat output, that is , needs to be considered, while can also be derived from . Denote the UAV state at time as , which respectively represent the position, velocity, and acceleration in the x, y, and z directions. Take the UAV's snap as the control cost , set the expansion step size to , discretize into , discretize into , then the dynamic differential constraint is discretized as:

[0088] (14)

[0089] Wherein: , , represents the direct sum of the matrix on the skew diagonal; the subscript is an integer, representing times the minimum discrete time; discretize the control cost uniformly in three-dimensional space. When discretizing, limit the control input within a range, that is , then there is a uniform discrete control input , Represents the set value of the coefficient of variation, Represents The maximum value in three-dimensional space; the dynamic expansion represented by this state equation is as shown in (b) of Figure 5 , and by introducing a kinematic model, motion constraints are incorporated into the search process. As shown in Figure 5 The conventional A* algorithm and RRT algorithm can only consider obstacle constraints, while the path generated by the method proposed in this patent not only considers obstacle constraints but also satisfies the motion constraint conditions of the UAV.

[0090] Another key method in the path search algorithm , which calculates the cost function during the search process, and subsequently Selects the optimal node according to the calculated cost function.

[0091] Following A*, the cost function of the UAV at the current position is expressed as , Represents the actual cost from the initial position of the UAV to the current position of the UAV , and actually represents the cumulative cost from the starting point to the current node. Represents the heuristic cost from the current position of the UAV to the target position, that is, the estimated cost from the current node to the target node; the actual cost of the UAV Is expressed as the combination of the discrete control cost and the landing duration . In order to balance between the discrete control cost and the landing duration , the objective function of the search Regarding the constraint on the landing duration Is defined to minimize the energy-time cost:

[0092] (15)

[0093] Where: Is a tuning parameter;

[0094] In the path search algorithm, Calculate, for each node expansion, based on the discrete control cost and the expansion step size Define the motion primitive cost As:

[0095] (16)

[0096] Where: Is a tuning parameter;

[0097] Based on the cost of motion primitives It is defined that for an optimal search path composed of motion primitives, the actual cost of the UAV corresponding to this optimal search path is expressed as:

[0098] (17)

[0099] represents the cost of the th motion primitive, and respectively represent the corresponding discrete control cost and expansion step size, ; Another component of the cost function is the heuristic cost. When the heuristic cost is 0, the algorithm degenerates into the Dijkstra algorithm.

[0100] This patent adopts a cost function considering the actual flight situation of the UAV. Considering it as a two-point boundary value problem, to reduce the computational complexity, the acceleration is relaxed, and then there is:

[0101]

[0102] (18)

[0103]

[0104] Then the heuristic cost of the UAV search is expressed as:

[0105] (19)

[0106] The optimal discrete control cost of the UAV is obtained as:

[0107] (20)

[0108] The cost function of the UAV at the current position is optimized to:

[0109] (21)

[0110] where: represents the corresponding optimal discrete control cost.

[0111] The pseudocode of the path search algorithm is shown in Table 1.

[0112] Table 1 Pseudocode of the path search algorithm

[0113]

[0114] Furthermore, the time - energy optimal moving target trajectory prediction algorithm is adopted to determine the target trajectory prediction sequence. Specifically, for the sequence of UAV autonomous landing path points , it is divided into a predicted trajectory sequence and a search trajectory sequence of the UAV to the current moving platform . Based on the UAV trajectory search sequence , the target trajectory prediction sequence is determined. Since environmental constraints are not considered, the next - moment position of the UAV is directly searched based on the current position of the UAV and the current position of the target, thereby forming the UAV trajectory search sequence ;

[0115] For the moving platform in the landing mission, this patent believes that its kinematic model is known and the movement is fast and energy - saving. The current position and the current speed of the target can be obtained by using on - board sensors or communication. The goal of the prediction algorithm is to estimate the position of the moving platform after a future time, where is a fixed known time. The time - energy optimal moving target trajectory prediction algorithm gives the time - energy optimal moving target trajectory prediction algorithm. Before introducing the time - energy optimal moving target trajectory prediction algorithm, this patent first gives the kinematic modeling of the moving platform. To reduce the computational load, this patent adopts a second - order chained - integral model, that is, the acceleration is used as the control input.

[0116] During the UAV landing process, the predicted duration is calculated through the target initial position and the target initial speed to obtain the target estimated position after ; The target is dynamically modeled using a second - order chained - integral model, and the target acceleration is used as the system input, expressed as:

[0117] (22)

[0118] Where: represents the state variable of the target at time, and represents the first - order derivative of ; ; , , represents a zero matrix of rows and columns, denote the identity matrix of dimension denote the position of the target at moment, 、 and respectively denote in the direction, direction and direction, and respectively denote the first and second derivatives of denote the system input of the target, taking values in ;

[0119] In the time - energy optimal moving target trajectory prediction algorithm, the function (the second row in Table 2) calculates a rough end position through the motion model of the target , and calculates the prediction duration through the dynamic model of the target, and then the estimated position of the target:

[0120] (23)

[0121] is only an estimate of the final position, and its purpose is to obtain the heuristic cost to reach the final position;

[0122] The system input of the target

[0123] (24)

[0124] where: denotes the maximum acceleration constraint of the target, denotes the set value of the discrete coefficient, usually taking an integer value between 3 and 5;

[0125] is obtained by the function in the sixth row of Table 2 to get the target state , based on the state variables of the target at moment, select the system input of the target at

[0126] (25)

[0127] where: denotes the cost function of the target, Represents the actual cost of the target, represents the heuristic cost of the target:

[0128] (26)

[0129] Where: represents the adjustment parameter, and the Euclidean distance between and can be indirectly substituted; the state variable corresponding to the optimal system input is added to the target trajectory prediction sequence , and this step is completed by the function in the 7th row of Table 2.

[0130] After the described algorithm loops are completed, the desired is obtained, and the pseudocode is shown in Table 2.

[0131] Table 2 Pseudocode of the target trajectory prediction algorithm

[0132]

[0133] In trajectory prediction, an ideal situation is that the total time of the UAV landing trajectory is equal to the prediction time ( ), so that the UAV just lands on the mobile platform. However, in the autonomous landing of the UAV, a fixed landing time may lead to the inability to achieve landing. Especially when the speed of the mobile platform is greater than the speed of the UAV, fixing to and forcing the UAV to reach the target state is infeasible in some cases. For example, when the speed of the mobile platform is greater than the speed of the UAV, the UAV can never plan a trajectory that can achieve landing. Therefore, a flexible terminal constraint method is proposed to adapt to different situations by relaxing the total trajectory time, as shown in Figure 6 . This method introduces a landing intention coefficient , and dynamically adjusts the landing strategy according to the planar distance between the UAV and the mobile platform: give priority to tracking when the distance is far, and give priority to landing when the distance is close. By adjusting the predicted target position, it is ensured that the UAV can land smoothly when approaching the mobile platform.

[0134] Furthermore, the constraint also includes a terminal constraint.

[0135] Set the end point of the UAV landing as the last position of the target trajectory prediction sequence , that is, , and only process during the end point constraint;

[0136] Use the landing degree ​Evaluate whether the drone performs a landing action: If , then maintain the state; if , then perform the landing action; if Figure 7 , the degree of landing is:

[0137] (27)

[0138] Among them: represents the maximum amplitude of the landing relative to the tracking, , represents the at the initial moment, a constant representing the landing speed range. The faster the landing speed, the larger;

[0139] Construct the relationship between the degree of landing and , and let:

[0140] (28)

[0141] Construct the end constraint for the drone to land as:

[0142]

[0143] (29)

[0144]

[0145] The initialized motion planning parameters include:

[0146] Parameters related to the initialization of the state machine: tracking distance d1 and landing distance d2;

[0147] Initialization parameters of the path search algorithm and trajectory prediction algorithm: target initial position , target initial velocity , drone initial position , prediction duration of the target future trajectory , target initial state , drone initial state , drone maximum speed constraint , drone maximum acceleration constraint .

[0148] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the above embodiments do not limit the present invention in any form. Any technical solutions obtained by using equivalent replacements or equivalent transformations fall within the protection scope of the present invention.

[0149] This embodiment also provides a time - energy - based autonomous landing path planning system for unmanned aerial vehicles (UAVs), including:

[0150] An autonomous landing model construction unit, which is used to construct an autonomous landing model with optimal time - energy;

[0151] An autonomous tracking mechanism design unit, which designs an autonomous tracking mechanism based on the autonomous landing model and is used to generate an autonomous tracking path for the UAV, including: generating local targets through a rolling space and performing local replanning, taking the local targets as target positions, and obtaining a UAV trajectory search sequence through a search algorithm; generating a UAV target trajectory prediction sequence based on the UAV trajectory search sequence

[0152] An autonomous landing mechanism design unit, which is used to design an autonomous landing mechanism to achieve the landing of the UAV, including: designing time and dynamic constraints, considering the terminal constraints in autonomous landing, optimizing the autonomous landing model, and solving the autonomous landing model through NLOPT;

[0153] A landing unit, which initializes the motion planning parameters and completes the landing through an autonomous landing finite - state machine including the above - mentioned autonomous tracking mechanism and autonomous landing mechanism.

[0154] A time - energy - based autonomous landing path planning method provided by this embodiment is an autonomous landing path planning technology specifically designed for vertical take - off and landing UAVs, which meets the dynamic constraints and real - time requirements.

[0155] The above has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form. Any technical solutions obtained by using equivalent replacement or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A time-energy based UAV autonomous landing path planning method, characterized by: Includes steps: Construct a time-energy optimal autonomous landing model; An autonomous tracking mechanism is designed based on the autonomous landing model to generate an autonomous tracking path for the drone, including: Generate local targets through rolling space and perform local replanning, use the local targets as target positions, and obtain the UAV trajectory search sequence through the search algorithm; Based on the UAV trajectory search sequence, generate the UAV target trajectory prediction sequence; Design an autonomous landing mechanism to land the drone, including: Design time and dynamic constraints, and consider the terminal constraints in autonomous landing to optimize the autonomous landing model; Solve the autonomous landing model through NLOPT; Initializing motion planning parameters, and completing landing through an autonomous landing finite state machine including the autonomous tracking mechanism and the autonomous landing mechanism; The objective function of the autonomous landing model is designed as: ； in, Indicates the landing time. To adjust the parameters, Respectively The third-order derivative of Indicates that the drone is The location at the moment; The constraints of the objective function include: The dynamic differential constraint is: ； ； in, express The status of the drone at the moment. express The first derivative of ; parameter matrix , express OK The zero matrix of columns, express dimensional identity matrix; , and Respectively exist direction, Direction and Position in direction, , and Respectively The first, second, and third derivatives of ; Indicates the jump of the drone, the value is ; The boundary constraints are: ； in, Indicates the landing start time, that is, the initial time; Indicates the state of the drone when it starts landing, that is, the initial state of the drone; Indicates the time when landing is completed. Indicates the status of the drone when it completes landing; The process constraints on velocity and acceleration are: ； ； in, Indicates that the drone is The speed of time, The maximum speed constraint of the UAV during the landing process; Indicates that the drone is The acceleration of time, Represents the maximum acceleration constraint of the drone during the landing process; Process constraints on time trajectory: the landing start time Until landing completion time According to the MINCO curve, it is divided into time trajectory segments, denoted as , the duration of each time trajectory segment must be greater than 0: ; Designing time and dynamic constraints to optimize the autonomous landing model includes: Regarding the process constraints of speed and acceleration, the objective function Optimized to: ； ； ； in, and are the weights of speed penalty and acceleration penalty respectively, For the optimization objective of the process constraint on acceleration, Optimizing objectives for process constraints regarding speed; Regarding the process constraints of the time trajectory, the objective function Optimized to: ； ； ； in, , is an unconstrained virtual time series, Represents the path point sequence of the MINCO curve; Based on the UAV trajectory search sequence, a UAV target trajectory prediction sequence is generated, which includes: The second-order chain integral model is used to model the target dynamics, and the target acceleration is used as the system input, which is: ； ； ； in, express The state variable of the target at time instant, express The first derivative of ; Indicates the target The location at the moment, , and Respectively exist direction, Direction and Position in direction, and Respectively The first and second derivatives of ; Represents the target system input, and its value is ; Calculate the predicted duration through the target's dynamic model The estimated target position : ； The initial position of the target , target initial speed ; Enter the target system Using discrete values, it is expressed as: ； in, represents the target maximum acceleration constraint, Indicates the set value of the discrete coefficient; based on The state variable of the target at the moment ,choose System input of the target at the moment : ； in, Indicates the actual cost, Represents the heuristic cost: ； ； in, represents the adjustment parameter; Input the optimal system The corresponding state variable Add to target trajectory prediction sequence .

2. The method for autonomous landing path planning of a UAV based on time-energy according to claim 1, characterized in that: Generating local targets through rolling space specifically includes: The current position of the target is taken as the global target, and it is determined whether the global target is in the rolling space. If it is in the rolling space, the global target is taken as the target position, and the next moment position of the drone is obtained through the search algorithm, thereby forming a drone trajectory search sequence. If it is not in the rolling space, first connect the current position of the drone with the global target, take the intersection of the line and the rolling space as the local target, take the local target as the target position, and obtain the next moment position of the drone through the search algorithm, thus forming the drone trajectory search sequence ; When executing the search algorithm, the optimal control cost of the UAV is obtained by solving the boundary value method .

3. The method for autonomous landing path planning of a UAV based on time-energy according to claim 2, characterized in that: Optimal control cost for: ； in, express The optimal state of the drone at all times; represents a 9-dimensional co-state vector, Respectively represent dimensional covariates; , , , , , , , and Indicates that direction, Direction and The unknown constant to be solved in the direction, Represents the Hamiltonian function, which is: ； in, To adjust the parameters; , and Respectively represent displacement The third, second and first order derivatives of the UAV, i.e. the jump, acceleration and speed of the UAV, subscript , and Respectively direction, Direction and direction.

4. The method for autonomous landing path planning of a UAV based on time-energy according to claim 2, characterized in that: The search algorithm adopts the A-star algorithm.

5. The method for autonomous landing path planning of a UAV based on time-energy according to claim 4, characterized in that: The cost function of the A-star algorithm is: ； ； in, , To adjust the parameters, and Respectively The corresponding discrete control cost and expansion step size, express The corresponding discrete control cost is for The corresponding optimal discrete control cost is, Search objective function for landing time constraints.

6. A UAV autonomous landing path planning system for implementing any of the methods described in claims 1-5, characterized in that: include: An autonomous landing model building unit, used to build a time-energy optimal autonomous landing model; The autonomous tracking mechanism design unit designs an autonomous tracking mechanism based on the autonomous landing model to generate the autonomous tracking path of the UAV, including: generating local targets through rolling space and performing local replanning, taking the local targets as the target positions, obtaining the UAV trajectory search sequence through the search algorithm; based on the UAV trajectory search sequence, generating the UAV target trajectory prediction sequence Autonomous landing mechanism design unit, used to design autonomous landing mechanism and realize UAV landing, including: designing time and dynamic constraints, considering terminal constraints in autonomous landing, optimizing autonomous landing model, and solving autonomous landing model through NLOPT; The landing unit initializes motion planning parameters and completes the landing through an autonomous landing finite state machine including the autonomous tracking mechanism and the autonomous landing mechanism.

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