A method for optimizing urban traffic routes for electric vertical takeoff and landing aircraft

Abstract

This invention relates to a method for optimizing urban traffic routes using electric vertical takeoff and landing (EVTOL) aircraft. The steps are as follows: establishing constraints; initializing a population of individuals within a specified spatial range, with each individual's coordinates representing a route; calculating the fitness of each individual; calculating the inertial mass of each individual; calculating the sum of forces acting on each individual in different directions and calculating the acceleration; updating the velocity and position of each individual; selecting and replacing individuals based on the constraints; determining the termination condition; if the maximum number of iterations is met, terminating the search and outputting the optimal solution as the route optimization scheme; using the variable parameters in the obtained optimal design scheme as the final tiltrotor aircraft route. This invention comprehensively considers the economy, route length, and safety of tiltrotor aircraft routes, making urban traffic using tiltrotor aircraft more efficient and convenient.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) route optimization methods, and in particular to a method for optimizing urban traffic routes for electric vertical take-off and landing (eVTOL) aircraft using an improved gravity search algorithm. Background Technology

[0002] Urban air mobility (UAM), as a core component of the low-altitude economy, is receiving increasing attention from all sectors of society. UAM systems refer to a mode of transportation that uses electric vertical take-off and landing (eVTOL) aircraft to provide travel services to residents. Due to the numerous buildings in cities, eVTOL aircraft inevitably have to navigate between buildings. Therefore, ensuring the safe operation of eVTOL aircraft in urban areas requires particularly careful flight path planning.

[0003] Existing trajectory optimization methods are either complex, slow to converge, and difficult to implement, or they may get stuck in local optima to varying degrees, or they only calculate the cost of flight and cannot fully consider the difficulties of urban transportation using aircraft. These design flaws will greatly affect the efficiency of eVTOL aircraft and make it difficult to meet the economic and safety requirements that eVTOL aircraft are concerned with in urban transportation. Summary of the Invention

[0004] The technical problem solved by this invention is to overcome the shortcomings of existing technologies and provide a method for optimizing urban traffic routes for electric vertical takeoff and landing (EVTOL) aircraft. By introducing the calculation of safety margins, the planned flight paths are kept as far away from the edges of real buildings as possible to ensure safety. Simultaneously, a minimum energy consumption objective function is proposed, so that the algorithm considers not only the path length but also the economic efficiency of the path when searching for it. This method draws on the gravity search algorithm, fully leveraging its advantages of simple design, ease of implementation, fast convergence speed, and strong versatility. Furthermore, the algorithm is improved to address the problem of easily getting trapped in local optima during optimization, ultimately achieving a high probability of obtaining the globally optimal flight path.

[0005] This invention adopts the following technical solution: a method for optimizing urban traffic routes using electric vertical takeoff and landing aircraft, comprising the following steps:

[0006] (1) Using waypoint coordinates of the route as design variables, constraints are established based on urban traffic space characteristics, regulations and the performance of eVTOL itself;

[0007] (2) Randomly generate a population of N individuals within the variable space bounded by the upper and lower bounds of the constraints, and use the matrix composed of the design variables as the position X of the individuals. i Each column vector represents the spatial coordinates of a waypoint;

[0008] (3) Calculate the fitness function of the individual based on the constraints;

[0009] (4) Calculate the inertial mass of each individual in the population;

[0010] (5) Based on the inertial mass of each individual, calculate the sum of the forces acting on each individual in different directions and calculate the acceleration;

[0011] (6) Update the velocity and position of each individual based on its acceleration;

[0012] (7) Calculate whether the updated individual meets the constraints described in step (1); if the constraints are met, proceed to the next step; if there are individuals that do not meet the constraints, count the number e of individuals that do not meet the constraints, and extract the top e individuals with high fitness from the previous iteration in descending order of fitness to replace them.

[0013] (8) If the maximum number of iterations or the required accuracy is met, the search is terminated and the optimal solution is output as the route optimization scheme; otherwise, proceed to step (2).

[0014] (9) The variable parameters in the obtained global optimal design scheme are used as the final eVTOL waypoints.

[0015] In step (1), establishing constraints based on urban traffic space characteristics, regulations, and the performance of eVTOL itself includes:

[0016] a) Flight altitude constraints:

[0017] H min ≤z i,m ≤H max 1≤m≤M

[0018] Among them, H min =max(H min1 H min2 ), H max =min(H max1 H max2 ), H min1 H min2 These are the minimum flight altitudes permitted by the aircraft's own capabilities and by regulations, respectively, H. max1 H max2 These are the maximum flight altitudes allowed by the aircraft's own capabilities and those permitted by regulations;

[0019] b) Climb angle constraint:

[0020]

[0021] Where, θ m Let θ be the angle of ascent of the m-th segment of the track formed by the m-th waypoint and the (m-1)-th waypoint. max The maximum climb angle determined by the aircraft's performance; x i,m y i,m z i,m Let m be the spatial coordinates of the m-th waypoint of the i-th track;

[0022] c) Steering angle constraint:

[0023]

[0024] Where, β m Let β be the turning angle of the m-th segment of the trajectory. max This is the aircraft's maximum turning angle;

[0025] d) Safety margin constraints:

[0026]

[0027] Among them, D k N is the minimum distance that must be maintained between eVTOL and urban buildings. k The impact factor, S, represents the continuous forward distance traveled by eVTOL when it detects a building ahead and attempts to change course, and W... a This is the maximum size for eVTOL.

[0028] In step (2), the location X of the individual i The expression is as follows:

[0029]

[0030] Each column vector represents the spatial coordinates of a waypoint, with the origin being the takeoff point, the coordinate system being the NED coordinate system, and M representing the number of design variables, i.e., the number of waypoints for each route. i A unique identifier representing an individual.

[0031] In step (3), the fitness function of the individual is as follows:

[0032] fitness i (t)=mL1()+nL2()

[0033] L1() and L2() are the eVTOL track cost and energy cost, respectively, and m and n are the weights of the eVTOL track cost and energy cost, respectively.

[0034]

[0035]

[0036] Where c1, c2, c3, and c4 are all 0-1 variables, representing whether there are changes in speed, climb angle, turn angle, and temperature between the m-th and (m-1)-th flight segments, respectively; if any item changes, the corresponding variable is set to 1, otherwise it is set to 0, (x i,0 y i,0 , z i,0 ( ) represents the coordinates of the takeoff point;

[0037] p1(v m p2(θ) m p3(β) m p4(T) m These are the energy consumption coefficients for flight speed, climb angle, turn angle, and temperature, respectively. Ω represents the rotor speed when the aircraft is hovering, and R represents the rotor radius. v m For flight speed, T m This refers to the atmospheric temperature.

[0038] The inertial mass of the individuals in the population is obtained by the following formula:

[0039]

[0040] Where N is the number of individuals, m i (t) is an intermediate variable, fitness i k(t) represents the fitness of individual i at time t, best(t) and worst(t) are the best and worst fitness values ​​among all individuals at time t, respectively. i (t) is the inertial mass weighting coefficient, M i (t) represents the inertial mass.

[0041] The inertial mass weighting coefficient

[0042] The calculation of the sum of forces acting on each individual in different directions and the calculation of acceleration are obtained through the following formula:

[0043]

[0044] In the formula, Let M be the gravitational force between individual i and individual j, where a and b are the column and row numbers of the gravitational force, respectively. i (t) and M j (t) represents the inertial mass of individual i and individual j, respectively, R ij(t) represents the distance between individual i and individual j, and ε is a small quantity, taken as ε = 1 × 10. -6 This is used to prevent the denominator from being zero. G(t) is the gravitational constant at time t, T is the maximum number of iterations, and G0 is the adjustment coefficient. For the net force acting on individual i, rand j A random number between 0 and 1 Let f be the acceleration of individual i, and fbest means that only the first f individuals with the best fitness are considered when calculating the resultant force.

[0045] The adjustment coefficient is calculated as follows:

[0046]

[0047] Where γ is a constant, and These represent the maximum and minimum position values ​​of the first-generation population in d-dimensional space, respectively, x m Calculate as follows:

[0048]

[0049] x i,m y i,m z i,m Let be the spatial coordinates of the m-th waypoint of the i-th track.

[0050] In the method for calculating the resultant force, the f individuals with the highest fitness are selected for calculation to reduce the influence of differences. That is, half the number of individuals in the population.

[0051] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for optimizing urban traffic routes for electric vertical takeoff and landing aircraft.

[0052] Compared with the prior art, the present invention has the following beneficial effects and advantages:

[0053] This invention provides a new approach to route optimization design. By leveraging the advantages of the gravity search algorithm, the algorithm is improved to more easily obtain the globally optimal design and effectively improve the accuracy of the optimal solution. The optimization efficiency is higher, improving the economy and safety of the optimized route and making it more suitable for urban traffic scenarios. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0055] Figure 2 This is a diagram illustrating the principle of gravity search. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings.

[0057] like Figure 1 As shown, this invention proposes a method for optimizing urban traffic routes for electric vertical takeoff and landing (EVTOL) aircraft, comprising the following steps:

[0058] (1) Using waypoint coordinates of the route as design variables, constraints are established based on urban traffic space characteristics, relevant regulations and the performance of eVTOL itself;

[0059] a) Flight altitude constraints

[0060] During trajectory planning, the aircraft's altitude may change, necessitating altitude restrictions. Therefore, the altitude constraint can be set as follows:

[0061] H min ≤z i,m ≤h max 1≤m≤M

[0062] Among them, H min =max(H min1 H min2 ), H max =min(H max1 H max2 ), H min1 H min2 These are the minimum flight altitudes permitted by the aircraft's own capabilities and by regulations, respectively, H. max1 H max2 These represent the maximum flight altitudes allowed by the aircraft's own capabilities and those permitted by regulations, respectively, with H as the reference. min =100m, H max =300m.

[0063] b) Climbing angle constraint

[0064] During flight, the eVTOL aircraft will climb, and its climb angle should be within the maximum allowable range. The climb angle constraint is set as follows:

[0065]

[0066] Where, θ max The maximum climb angle is determined by the eVTOL performance.

[0067] c) Steering angle constraint

[0068] During flight, the horizontal direction of an eVTOL will change, and its horizontal steering angle should be within the maximum allowable range of the aircraft. The constraint condition for the steering angle is set as follows:

[0069]

[0070] Where, β max For the maximum steering angle of eVTOL, take

[0071] d) Safety margin constraints

[0072] Safety margin refers to the minimum distance that must be maintained between the eVTOL and urban buildings to ensure flight safety. Its calculation formula is as follows:

[0073]

[0074] Where, N k The influencing factor is adjusted according to the type of building. When k=1, it represents N at that time. k It is the influence factor of residential buildings. When k=2, it represents N at this time. k This is the influence factor for public buildings; when k=3, it represents N at this time. k The influence factors for government buildings are set as k1 = 0.3, k2 = 0.36, and k1 = 0.4. S represents the continuous forward distance traveled by the eVTOL when it detects a building ahead and attempts to change course, and is set to S = 100m. W a For the maximum size of eVTOL (usually wingspan), W a =5.7m.

[0075] (2) Randomly generate a population of N individuals within the variable space bounded by the upper and lower bounds of the design variable constraints. i The individual's ID number, N=50; the individual's position. It is a matrix composed of design variables, where each column vector is the spatial coordinate of a waypoint, the origin is the takeoff point, the coordinate system is the NED coordinate system, and M is the number of design variables, that is, the number of waypoints for each route, which is taken as M=30;

[0076] (3) Calculate the individual's fitness i (t)=mL1()+nL2(), where L1() and L2() are the eVTOL track cost and energy cost, respectively, and m and n are the weights of the eVTOL track cost and energy cost, respectively.

[0077]

[0078]

[0079] Where c1, c2, c3, and c4 are all 0-1 variables, representing whether there are changes in speed, climb angle, turn angle, and temperature between the m-th and (m-1)-th flight segments, respectively; if any item changes, the corresponding variable is set to 1, otherwise it is set to 0, (x i,0 y i,0 , z i,0 p1(v) represents the coordinates of the takeoff point; m p2(θ) m p3(β) m p4(T) m These are the energy consumption coefficients for flight speed, climb angle, turn angle, and temperature, respectively. Ω represents the rotor speed when the aircraft is hovering, and R represents the rotor radius. p4(T m ) = 0.2; v m For flight speed, T m (4) Calculate the inertial mass of an individual in the population:

[0080]

[0081] Where N is the number of individuals, m i (t) is an intermediate variable, fitness i k(t) represents the fitness of individual i at time t, best(t) and worst(t) are the best and worst fitness values ​​among all individuals at time t, respectively. i (t) is the inertial mass weighting coefficient, M i (t) represents the inertial mass;

[0082] (5) Calculate the sum of the forces acting on each individual in different directions and calculate the acceleration:

[0083]

[0084] In the formula Let M be the gravitational force between individual i and individual j, where a and b are the column and row numbers of the gravitational force, respectively. i (t) and M j (t) represents the inertial mass of individual i and individual j, respectively, R ij (t) represents the distance between individual i and individual j, and ε is a small quantity, taken as ε = 1 × 10. -6 The significance is to prevent the denominator from being zero. `fbest` indicates that only the first `f` individuals with the best fitness are considered when calculating the resultant force, reducing the influence of differences. That is, half the number of individuals in the population. G(t) is the gravitational constant at time t, T is the maximum number of iterations, and G0 is the adjustment coefficient. γ = 2, and These represent the maximum and minimum position values ​​of the first-generation population in m-dimensional space, respectively, x. m Calculate as follows:

[0085]

[0086] like Figure 2 As shown, M1, M2, M3, and M4 represent the masses of the 1st to 4th individuals, respectively. The larger the circle, the greater its mass. F 12 F 13 F 14 These represent the gravitational forces exerted by the 2nd, 3rd, and 4th individuals on the 1st individual, respectively.

[0087] (6) Update the speed and position of each individual:

[0088]

[0089] In the formula and Let be the velocity and position of individual i in the d-dimensional space at time t+1, respectively. and Let r represent the velocity, acceleration, and position of individual i at time t, and rand. i The value is a random number between 0 and 1, and s is the position adjustment coefficient. T is the maximum number of iterations, set to T=500.

[0090] (7) Calculate whether the updated individual meets the constraints described in step (1). If the constraints are met, proceed to the next step. If there are individuals that do not meet the constraints, extract the same number of individuals with the highest fitness from the previous generation and replace them.

[0091] (8) Termination condition judgment. If the maximum number of iterations is met, the search is terminated and the optimal solution is output as the route optimization scheme; otherwise, proceed to step (2).

[0092] (9) The variable parameters in the obtained global optimal design scheme are used as the final eVTOL waypoints.

[0093] In summary, this invention proposes a novel method for optimizing urban traffic routes using electric vertical takeoff and landing (eVTOL) aircraft. First, constraints are established based on urban traffic spatial characteristics, relevant regulations, and the performance of the eVTOL itself, increasing the safety of the routes. Furthermore, an energy consumption evaluation component is added to the fitness function, thus ensuring that the route optimization process balances range and economy.

[0094] Secondly, in the gravity algorithm, a weighting coefficient is added when calculating inertial mass, further increasing the mass difference between different individuals and accelerating the convergence speed. By improving the gravitational constant, a linear function is used instead of the exponential function in the standard algorithm, and the function is dynamically adjusted based on the position of individuals in the population. This increases the algorithm's global search capability, allowing it to explore the optimal solution region faster and more accurately. Finally, an adjustment parameter is introduced in the position update: a larger s parameter in the early stages of the algorithm's operation is beneficial for global exploration, while a smaller s parameter in the later stages is beneficial for local exploration. The improved algorithm significantly improves search accuracy and convergence speed.

[0095] The above are merely specific steps of the present invention and do not constitute any limitation on the scope of protection of the present invention; it can be extended to the field of UAV route optimization design, and all technical solutions formed by equivalent transformation or equivalent substitution fall within the scope of protection of the present invention.

Claims

1. An electric vertical take-off and landing aircraft urban traffic route optimization method, characterized in that, The steps include the following: (1) Using waypoint coordinates of the route as design variables, constraints are established based on urban traffic space characteristics, regulations and the performance of eVTOL itself; (2) Randomly generate N individuals in the variable space between the upper and lower bounds of the constraints, and the matrix composed of design variables as the position X of the individual i Each column vector is the spatial coordinates of the waypoint; (3) Calculate the fitness function of the individual based on the constraints; (4) Calculate the inertial mass of each individual in the population; (5) Based on the inertial mass of each individual, calculate the sum of the forces acting on each individual in different directions and calculate the acceleration; (6) Update the velocity and position of each individual based on its acceleration; (7) Calculate whether the updated individual meets the constraints described in step (1); if the constraints are met, proceed to the next step; if there are individuals that do not meet the constraints, count the number e of individuals that do not meet the constraints, and extract the top e individuals with high fitness from the previous iteration in descending order of fitness to replace them. (8) If the maximum number of iterations or the required accuracy is met, the search is terminated and the optimal solution is output as the route optimization scheme; otherwise, proceed to step (2). (9) The variable parameters in the obtained global optimal design scheme are used as the final eVTOL waypoints.

2. The method of claim 1, wherein, In step (1), establishing constraints based on urban traffic space characteristics, regulations, and the performance of eVTOL itself includes: a) Flight altitude constraints: H min ≤z i,m ≤H max 1≤m≤M where H min = max(H min1 , H min2 ), H max = min(H max1 , H max2 ), H min1 , H min2 are the minimum flight heights allowed by the aircraft's own capabilities and by regulations, respectively, and H max1 , H max2 are the maximum flight heights allowed by the aircraft's own capabilities and by regulations, respectively. b) Climb angle constraint: wherein θ m is the climb angle of the mth segment of the flight path formed by the mth waypoint and the (m-1)th waypoint, θ max is the maximum climb angle determined by the aircraft performance; x i,m , y i,m , z i，m are the spatial coordinates of the mth waypoint of the ith flight path; c) Steering angle constraint: ≤β max where β m is the turning angle of the mth segment of the flight path, β max is the maximum turning angle of the aircraft; d) Safety margin constraints: where D k is the minimum distance that must be maintained between the eVTOL and the city buildings, N k is the impact factor, S is the sustained forward distance experienced by the eVTOL when it identifies the presence of a building ahead and attempts to change course, W a is the maximum size of the eVTOL.

3. The method of claim 1, wherein, In step (2), the position X of the individual i is expressed as follows: Each column vector represents the spatial coordinates of a waypoint, with the origin being the takeoff point and the coordinate system being the NED coordinate system. M represents the number of design variables, i.e., the number of waypoints for each route; i represents the individual's ID.

4. The method of claim 1, wherein, In step (3), the fitness function of the individual is as follows: fitness i (t) = mL1() + nL2() L1() and L2() are the eVTOL track cost and energy cost, respectively, and m and n are the weights of the eVTOL track cost and energy cost, respectively. wherein c1, c2, c3, c4 are 0-1 variables, respectively representing whether the speed, the climbing angle, the steering angle, the temperature between the mth track and the (m-1)th track have changed; if a certain item has changed, the corresponding variable is set to 1, otherwise 0, (x i，0 , y i，0 , z i，0 ) are the coordinates of the take-off point; p1(v m ), p2(θ m ), p3(β m ), p4(T m ) are the flight speed energy consumption coefficient, the climbing angle energy consumption coefficient, the steering angle energy consumption coefficient and the temperature energy consumption coefficient, respectively, Ω is the rotor speed when the aircraft is hovering, R is the rotor radius, p4(T m ) = 0.2; v m is the flight speed, T m is the atmospheric temperature.

5. The method of claim 1, wherein, The inertial mass of the individuals in the population is obtained by the following formula: where N is the number of individuals, m i (t) is an intermediate variable, fitness i (t) is the fitness of individual i at time t, best(t) and worst(t) are the best and worst fitness values of all individuals at time t, k i (t) is the inertia mass weighting factor, M i (t) is the inertia mass.

6. The method for optimizing urban traffic routes using an electric vertical takeoff and landing aircraft according to claim 5, characterized in that, the inertial mass weighting factor 7. The method of claim 1, wherein, The calculation of the sum of forces acting on each individual in different directions and the calculation of acceleration are obtained through the following formula: where, G (t) is the gravitational constant at time t, T is the maximum iteration number, G0 is the adjustment coefficient, i (t) is the distance between individual i and individual j, ε is a small quantity, and ε = 1 x 10 j (t) is the inertial mass of individual i and individual j, respectively, R ij (t) is the distance between individual i and individual j, ε is a small quantity, and ε = 1 x 10 -6 , for preventing the denominator from being zero, G(t) is the gravitational constant at time t, T is the maximum iteration number, G0 is the adjustment coefficient, is the resultant force on individual i, rand j is a random number between 0 and 1, is the acceleration of individual i, and fbest indicates that only the first f individuals with the best fitness are considered when calculating the resultant force.

8. The method for optimizing urban traffic routes using an electric vertical takeoff and landing aircraft according to claim 7, characterized in that: The adjustment coefficient is calculated as follows: Where γ is a constant, and These represent the maximum and minimum position values ​​of the first-generation population in d-dimensional space, respectively, x m Calculate as follows: x i,m , y i,m , z i，m is the spatial coordinate of the mth waypoint of the ith track.

9. The method for optimizing urban traffic routes for an electric vertical takeoff and landing aircraft according to claim 7, characterized in that: In the method for calculating the resultant force, the f individuals with the highest fitness are selected for calculation to reduce the influence of differences. That is, half the number of individuals in the population.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements a method for optimizing urban traffic routes for electric vertical takeoff and landing aircraft as described in any one of claims 1-9.

Images