Vertical channel optimization

Abstract

Vertical course optimization. An embodiment provides for performing vertical course optimization by the following steps: generating a plurality of waypoints with allowed parameters for a course, at which the course of an aircraft including a heading element, an altitude element, and a speed element is adjustable; calculating a number of unique trajectories available based on the plurality of waypoints; when the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper limit of the set of evaluated trajectories; identifying mobile waypoints in the trajectories of the elite trajectory set; performing a global optimal path evaluation, wherein the positions of the mobile waypoints are adjusted within the associated trajectories to identify an optimal trajectory for the aircraft on the course; and providing the optimal trajectory to the aircraft to follow the course according to the optimal trajectory.

Description

Technical Field

[0001] The present disclosure relates to trajectory optimization and, more particularly, to optimizing the altitude and speed of an aircraft over multiple segments of a trajectory taking into account various parameters measuring the efficiency of the aircraft. Background Art

[0002] A course includes a track indicating the altitude and speed of an aircraft over the course of the course. As will be appreciated, the altitude and speed of the aircraft may vary over the course of a journey from takeoff to landing, but the aircraft typically climbs / descends to a specified altitude and maintains a specified speed on a specified heading for an extended period of time. Changes in altitude or speed may affect how quickly the aircraft reaches its destination, the amount of fuel consumed, the level of noise generated (in the cabin or in the environment), the weather patterns that interact with the aircraft, and the level of wear on the components of the aircraft, among other effects. Different operators may give preference to increasing or decreasing the values ​​of various parameters relative to each other when setting a course, however, generating a course that is optimized according to the operator's preferences from the available options can be an intensive operation of time and processor resources. Summary of the invention

[0003] Aspects of the present disclosure provide improved systems and methods for optimizing an aircraft trajectory based on various parameters.

[0004] In one aspect, the present disclosure provides a method for vertical course optimization, the method comprising the following steps: generating a plurality of waypoints with allowed parameters for a course, at which the routes of an aircraft including heading elements, altitude elements and speed elements are adjustable; calculating the number of unique trajectories available based on the plurality of waypoints; in response to determining that the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper limit range of the set of evaluated trajectories; identifying mobile waypoints in the trajectories of the elite trajectory set; performing a global optimal path evaluation, wherein the positions of the mobile waypoints are adjusted within the associated trajectories to identify an optimal trajectory for the aircraft on the course; and providing the optimal trajectory to the aircraft to follow the course according to the optimal trajectory.

[0005] On the other hand, in combination with any of the example methods above or below, the method further includes, in response to determining that the number of unique trajectories is less than a threshold number of trajectories: generating an exhaustive trajectory set corresponding to the unique trajectories; and performing an exhaustive evaluation on the exhaustive trajectory set to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper range of the exhaustive trajectory set.

[0006] On the other hand, in combination with any of the example methods above or below, the step of probability evaluation of the method further comprises the following steps: generating an initial parameter probability distribution based on the plurality of waypoints; generating a probabilistic trajectory set of a predefined number of trajectories based on the initial parameter probability distribution; and selecting the elite trajectory set from the probabilistic trajectory set.

[0007] On the other hand, in combination with any of the example methods above or below, the method of selecting the elite trajectory set from the probabilistic trajectory set also includes the following method, which also includes the following steps: generating a subsequent parameter probability distribution based on the waypoints included in the elite trajectory set; generating a subsequent probabilistic trajectory set of a predefined number of trajectories based on the subsequent parameter probability distribution; and selecting a new trajectory set from the subsequent probabilistic trajectory set as the elite trajectory set.

[0008] On the other hand, in combination with any of the example methods above or below, the moving waypoint is a waypoint at which at least one element of the route is adjusted in a specific trajectory in the elite trajectory set; and when performing the global optimal path evaluation, the following waypoints in the specific trajectory are removed from the specific trajectory: at which at least one element of the route is not adjusted.

[0009] In another aspect, in combination with any of the example methods above or below, the position of the moving waypoints is adjusted within the associated trajectory within a range defined by intermediate points between the moving waypoints located on the trajectory.

[0010] On the other hand, in combination with any of the above or below example methods, waypoints with allowed parameters specify at least one criterion selected from the group consisting of: longitude; latitude; cabin noise level; ambient noise level; available thrust of the aircraft; elapsed flight time; sea level altitude of the aircraft; ground altitude of the aircraft; weight of the aircraft; and operational events of the aircraft.

[0011] In one aspect, the present disclosure provides a system for vertical course optimization, the system comprising: a processor; a memory comprising instructions, the instructions enabling the system to perform the following operations when executed by the processor: generating a plurality of waypoints with allowed parameters for a course, at which the course of an aircraft including a heading element, an altitude element, and a speed element is adjustable; calculating the number of unique trajectories available based on the plurality of waypoints; in response to determining that the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper limit range of the set of evaluated trajectories; identifying moving waypoints in the trajectories of the elite trajectory set; performing a global optimal path evaluation, wherein the positions of the moving waypoints are adjusted within the associated trajectories to identify an optimal trajectory for the aircraft on the course; and providing the optimal trajectory to the aircraft to follow the course according to the optimal trajectory.

[0012] On the other hand, in combination with any of the example systems above or below, in response to determining that the number of unique trajectories is less than a threshold number of trajectories, the system is further enabled to: generate an exhaustive trajectory set corresponding to the unique trajectories; and perform an exhaustive evaluation on the exhaustive trajectory set to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper range of the exhaustive trajectory set.

[0013] On the other hand, in combination with any of the example systems above or below, when performing the probability evaluation, the system is also enabled to perform the following operations: generate an initial parameter probability distribution based on the multiple waypoints; generate a probabilistic trajectory set of a predefined number of trajectories based on the initial parameter probability distribution; and select the elite trajectory set from the probabilistic trajectory set.

[0014] In another aspect, in combination with any of the example systems above or below, when the elite trajectory set is selected from the probabilistic trajectory set, the system is further enabled to perform the following operations: generate a subsequent parameter probability distribution based on waypoints included in the elite trajectory set; generate a subsequent probabilistic trajectory set of a predefined number of trajectories based on the subsequent parameter probability distribution; and select a new trajectory set from the subsequent probabilistic trajectory set as the elite trajectory set.

[0015] On the other hand, in combination with any of the example systems above or below, the moving waypoint is a waypoint at which at least one element of the route is adjusted in a specific trajectory in the elite trajectory set; and when performing the global optimal path evaluation, the following waypoints in the specific trajectory are removed from the specific trajectory: at which at least one element of the route is not adjusted.

[0016] In another aspect, in combination with the example systems above or below, the position of the mobile waypoints is adjusted within the associated trajectory within a range defined by intermediate points between the mobile waypoints located on the trajectory.

[0017] On the other hand, in combination with any of the example systems above or below, waypoints with allowed parameters specify at least one criterion selected from the group consisting of: longitude; latitude; cabin noise level; ambient noise level; available thrust of the aircraft; elapsed flight time; sea level altitude of the aircraft; ground altitude of the aircraft; weight of the aircraft; and operational events of the aircraft.

[0018] In one aspect, the present disclosure provides a non-transitory computer-readable medium including instructions, which, when executed by a processor, enable the processor to perform operations for vertical course optimization, the operations including: generating a plurality of waypoints with allowed parameters for a course, at which the course of an aircraft including a heading element, an altitude element, and a speed element is adjustable; calculating a number of unique trajectories available based on the plurality of waypoints; in response to determining that the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper limit range of the set of evaluated trajectories; identifying moving waypoints in the trajectories of the elite trajectory set; performing a global optimal path evaluation, wherein the position of the moving waypoints is adjusted within the associated trajectory to identify an optimal trajectory for the aircraft on the course; and providing the optimal trajectory to the aircraft to follow the course according to the optimal trajectory.

[0019] In another aspect, in combination with any of the example computer-readable media above or below, in response to determining that the number of unique trajectories is less than a threshold number of trajectories: generating an exhaustive trajectory set corresponding to the unique trajectories; and performing an exhaustive evaluation on the exhaustive trajectory set to identify an elite trajectory set including those trajectories whose efficiency metrics are within an upper range of the exhaustive trajectory set.

[0020] In another aspect, in combination with any example computer-readable medium above or below, the probability evaluation further comprises the following operations: generating an initial parameter probability distribution based on the plurality of waypoints; generating a probabilistic trajectory set of a predefined number of trajectories based on the initial parameter probability distribution; and selecting the elite trajectory set from the probabilistic trajectory set.

[0021] In another aspect, in combination with any example computer-readable medium above or below, selecting the elite trajectory set from a probabilistic trajectory set further comprises the following operations: generating a subsequent parameter probability distribution based on waypoints included in the elite trajectory set; generating a subsequent probabilistic trajectory set of a predefined number of trajectories based on the subsequent parameter probability distribution; and selecting a new trajectory set from the subsequent probabilistic trajectory set as the elite trajectory set.

[0022] In another aspect, in combination with any of the example computer-readable media above or below, the moving waypoint is a waypoint at which at least one element of the route is adjusted in a particular trajectory in the elite trajectory set; and when performing the global optimal path evaluation, removing from the particular trajectory the waypoint at which at least one element of the route is not adjusted.

[0023] In another aspect, in combination with any of the example computer readable media above or below, positions of mobile waypoints are adjusted within the associated trajectory within a range defined by intermediate points between the mobile waypoints located on the trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order that the above-recited features of the present disclosure may be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to aspects some of which are illustrated in the appended drawings.

[0025] Figure 1 Components of a system architecture for providing vertical lane optimization according to aspects of the present disclosure are shown.

[0026] Figure 2 is a flow chart of a method for optimizing a vertical channel according to aspects of the present disclosure.

[0027] Figure 3 is a flow chart of a method for performing probability evaluation according to aspects of the present disclosure.

[0028] FIG. 4A to FIG. 4C An example course is shown for which a first element of a trajectory is being optimized in accordance with aspects of the present disclosure.

[0029] FIG. 5A to FIG. 5C An example course is shown where a second element of a trajectory is being optimized in accordance with aspects of the present disclosure. DETAILED DESCRIPTION

[0030] The present disclosure relates to an improved system and method for optimizing the flight path of an aircraft. A flight path typically defines the route taken by an aircraft from a first point to a second point, and the aircraft may vary the speed and altitude (called a trajectory) of travel on the flight path to affect various parameters associated with flight. An aircraft typically changes one or more of heading, altitude, and speed at points (called waypoints) in the flight path. The number of potential waypoints to which the aircraft can adjust heading / altitude / speed and the range of allowed waypoint parameters to which the aircraft can adjust presents a pool of possible trajectories that may be too large to effectively analyze the optimal trajectory that maximizes the efficiency merit of the flight. In the case where the pool is small enough, an exhaustive analysis (scoring and ranking all possibilities) may be performed, but a larger pool may be analyzed based on a probabilistic representative sample set. Several iterations of the probability set may be analyzed until a diminishing return on efficiency gain is noted, and then the set of the current iteration is scored and ranked. Once analyzed (either exhaustively or probabilistically), an "elite" trajectory set with the best efficiency merit is identified. To further streamline the process, any waypoints in the elite trajectory that are not associated with a change in heading / altitude / speed may be ignored in further analysis. The remaining waypoints are "moving," and their positions in the elite trajectory are adjusted during the maximization analysis to find a trajectory that generates the "best" course for the operator's needs. This trajectory may then be assigned to the autopilot to follow when guiding the aircraft on course.

[0031] As will be appreciated, the present disclosure provides improvements in computing devices involving the generation and use of optimized courses via a novel process that differs significantly from the generation process used in prior systems by allowing probabilistic searches to be performed with enhanced accuracy and precision rather than relying on brute force analysis of a data set or a sampled subset thereof. The improvements provided include, but are not limited to, using less processing resources and memory storage space to handle analysis of equivalently large data sets, faster and more flexible course generation (allowing for in-flight updates to courses), and other benefits.

[0032] As used herein, an operator may refer to a pilot or crew member of an aircraft, an operator of an aircraft (e.g., an airline), an air traffic controller (ATC), or a group of coordinating ATCs. An operator may use one or more computing devices to perform one or more tasks including the methods discussed herein. The computing device includes a processor and a memory storage device including instructions contained therein, which instructions can be executed by the processor to enable the computing device to perform various actions. Examples of computing devices include servers, personal computing devices (e.g., towers, laptops, smart phones, tablets), aircraft control computing devices (e.g., computing systems that provide control and sensor interfaces and autopilot functions for aircraft), flight control computing devices (e.g., computing systems that provide command and control interfaces for airspace), etc.

[0033] As used herein, a waypoint refers to a set of defined conditions experienced within a flight plan, at which the aircraft may initiate a change in heading, speed, and / or altitude. Waypoint parameters describe various physical representations of the aircraft, airspace, atmosphere, or flight state at some points in the route for which the conditions are measured. In some aspects, the conditions may be satisfied by one or more position coordinates (e.g., latitude, longitude, height relative to sea level, height relative to the ground). In other aspects, the conditions may be satisfied by various characteristics of the aircraft (e.g., weight, available thrust, pitch). In other aspects, the conditions may be satisfied by various occurrences of events (e.g., landing gear deployment, flaps release, time since takeoff). A waypoint may include one or more waypoint parameters (e.g., latitude and longitude, latitude and weight, available thrust, and time since takeoff), and each waypoint in a flight plan may specify the same or different waypoint parameters. Although waypoints may be referred to in relation to a "position" or "point" in a course and relative to each other, it will be understood that waypoints are not limited to position criteria, but are multi-dimensional descriptions of aircraft, airspace, atmosphere, or course characteristics, and that a "position" or "point" in a course may refer to a percentage of completion of the associated course that is expected to satisfy a given set of waypoint parameters within the course's route.

[0034] As used herein, efficiency advantage refers to a combined weighted score of various waypoint parameters associated with a flight plan to describe quantitative and qualitative measures of flight efficiency related to operational, scheduling, regulatory, and reputation constraints. For example, when considering the efficiency advantage of a particular trajectory, the fuel usage rate, the impact of scheduling changes (e.g., the number of passengers who will miss a connection if delayed, whether there is a positive impact of arriving early), the crew's duty time, the noise level (e.g., internal cabin dB or ground dB), wear and tear metrics, etc. may be included separately. Waypoint parameters can address issues related to aircraft performance, operational management, passenger comfort, and environmental impact, as well as other issues. At various points in the flight plan, one or more waypoint parameters may oppose each other in optimizing efficiency advantages. For example, lower altitudes and higher speeds may increase the noise level detected on the ground, and the operator of the aircraft may wish to minimize the noise level to reduce the impact of flight operations on people living below the route. However, flying at higher altitudes may cause the aircraft to experience turbulence (which operators wish to reduce to improve passenger comfort), and flying at lower speeds may result in longer flights (which operators also wish to reduce to improve operational management). Optimizing opposing parameters according to operator preferences may weight different parameters differently at various points in the airway. For example, a parameter for exterior noise level may be weighted least at 0 to X miles from an airport and most at X to Y miles from an airport.

[0035] Now refer to Figure 1 , illustrates an architecture 100 that illustrates components of a system for providing vertical channel optimization. The architecture 100 may include one or more computing devices provided by one or more operators to provide the features and actions described herein. In various aspects, the architecture 100 represents a single computer system, while in other aspects, the architecture 100 is provided by several computer systems operating in a networked or connected environment (e.g., via the Internet, a local area network, an ad hoc wired network, etc.).

[0036] Environmental prediction generator 105 receives weather and air traffic forecast information from various external data sources to map the route for analysis. External data sources may include ATC flight charts, flight plan databases, weather services, and other entities that provide information related to the environment in which the aircraft will fly. In some aspects, environmental prediction generator 105 generates a four-dimensional grid of weather features in the route, including turbulence, wind speed, wind direction, and temperature. In various aspects, environmental prediction generator 150 generates scheduling difference probabilities for various sectors of airspace and airport along the route, and generates a timing impact table, which is associated with the impact of the efficiency advantage of the destination or waypoint on a given route at various times other than the scheduled arrival time. For example, arriving at the destination airport X minutes in advance may have a smaller positive impact on the efficiency advantage, arriving on time or at most late Y minutes may have no impact or negligible impact on the efficiency advantage, but arriving late Z minutes or later may have a larger negative impact on the efficiency advantage, to encourage the generation of a route that causes on-time flight.

[0037] The flight objective generator 110 receives flight plans generated by the operator in various formats and atmospheric information generated by the environmental forecast generator 105. When generating a flight plan with an optimized trajectory for a given aircraft, the flight objective generator 110 generates a flight objective that specifies initial aircraft conditions (e.g., aircraft type, fuel load, weight, weight distribution), required waypoints, a subset of weather and forecast charts that the route passes through (e.g., within 50 miles of a specified passage), and a timing impact table.

[0038] The performance model database 115 stores data related to the performance standards of various aircraft, including but not limited to: aircraft type, number of passengers, number of crew, flight schedule, fuel consumption rate, altitude envelope, speed profile, acceleration / deceleration curve, allowed climb / descent rate, cabin contour, engine type and number, fuel load, etc.

[0039] The weather interpolator 120 receives weather data from the flight target generator 110 and calculates the most likely weather conditions at a given point within and around the weather pattern. For example, weather cells (e.g., cloud banks, low / high pressure areas) may be indicated in the weather data as covering certain coordinates, and the weather interpolator 120 calculates: wind speed; wind direction; likelihood and intensity of updrafts and downdrafts; regionalized likelihood of rain, snow, hail, ice, and / or lightning; etc. at various coordinates corresponding to the weather cells.

[0040] The trajectory space generator 125 divides the horizontal elements of the airway into segments along the passage with various waypoints spaced along the route of the airway at which the aircraft can adjust speed and / or altitude. The waypoints may include operator-specified waypoints as well as waypoints created by the trajectory space generator 125. At each waypoint, the trajectory space generator 125 determines the range of allowable altitudes and speeds for the aircraft based on flight control restrictions (e.g., restricted airspace, short-haul scheduled round-trip flights are restricted to one altitude range and long-haul flights are restricted to another altitude range), environmental factors (e.g., buildings or mountains may set a safety floor for the lower altitude limit), and performance factors (e.g., an aircraft may be certified to operate at various altitudes and speeds, and not operate at other altitudes and speeds due to mechanical or operating efficiency data).

[0041] Trajectory optimizer 130 searches within the range of allowed altitudes and speeds for each available waypoint to identify the "best" trajectory to satisfy the operator's preferences. Trajectory optimizer 130 receives the interpolated weather map from weather interpolator 120, receives the waypoints and ranges of values ​​for those waypoints from trajectory space generator 125, and communicates with trajectory score calculator 135 to determine which trajectories best satisfy the operator's preferences.

[0042] The trajectory score calculator 135 receives the trajectory optimized by the trajectory optimizer 130 and the initial aircraft conditions from the flight objective generator 110 and calculates the efficiency advantage for the trajectory. The operator sets the trajectory score calculator 135 to weight various parameters according to the operator's preferences. For example, one operator may set the trajectory score calculator 135 to prioritize passenger comfort (e.g., keep cabin noise below a threshold, avoid turbulence, keep ascent / descent below a threshold pitch), while another operator may set the trajectory score calculator 135 to prioritize fuel efficiency. The trajectory score calculator 135 provides the scores of the efficiency advantages of the various trajectories to the trajectory optimizer 130 so that the trajectory optimizer 130 can select an elite set of trajectories, perform a recursive round of probability analysis, and output the optimized trajectories to the pilot device 140.

[0043] One or more navigation devices 140 may communicate with trajectory optimizer 130 to request and / or receive optimized courses and trajectories from trajectory optimizer 130. Navigation devices 140 may include handheld or personal devices (e.g., a pilot's tablet, a dispatcher's workstation, or onboard avionics) used by aircraft crew members to request and view various courses, flight management computers (including autopilots) in aircraft used to interface with the aircraft's flight controls and automate the operation of the aircraft to follow the provided courses, airline and ATC operating devices, etc. In various aspects, more than one navigation device 140 may receive an optimized course requested by one navigation device 140.

[0044] Figure 2 2 is a flow chart of a method 200 for optimizing a vertical course. The method 200 begins at block 210, where a flight objective generator 110 generates a flight objective for a given course. The flight objective generator 110 receives a flight plan generated by an operator, collects weather forecast data associated with the flight plan, and analyzes distances, weather, and traffic between waypoints to generate a set of waypoints at which elements of the route in flight (i.e., heading, altitude, and / or speed) may be changed.

[0045] At block 220, the trajectory space generator 125 generates a plurality of waypoints along the flight path having values ​​within a set of permitted performance parameters for the aircraft. The set of waypoints lists potential combinations of headings, speeds, and altitudes at which the aircraft is capable of operating. For example, the performance envelope for a given aircraft may specify maximum climb / descent / turn rates, stall speeds, cruise speeds, maximum speeds, etc. at various altitudes at which the aircraft is certified / permitted to operate. In some aspects, an operator may set a minimum altitude and / or speed and / or a maximum altitude and / or speed based on efficiency considerations.

[0046] At block 230 , the trajectory optimizer 130 determines the total number of unique trajectories (N ) that can be constructed based on the allowed parameter sets. T ) is greater or less than the threshold number of trajectories (N max ). In response to determining N T Less than (or equal to) N max , method 200 proceeds to block 240. In response to determining N T Greater than N max , method 200 proceeds to box 260.

[0047] At block 240, the trajectory optimizer 130 generates a unique trajectory set based on the allowed waypoint parameter values, and at block 250, an exhaustive evaluation is performed on the unique trajectory set. In the exhaustive evaluation, the trajectory optimizer 130 and the trajectory score calculator 135 analyze the efficiency merits of the individual trajectories in the unique trajectory set using the weights and parameters specified by the operator to return an elite trajectory set. The elite trajectories are a predefined number (e.g., x) of trajectories in the analyzed set that have the top x highest efficiency merits as determined by the trajectory score calculator 135. After block 250, the method 200 proceeds to block 270.

[0048] At block 260, the trajectory optimizer 130 and trajectory score calculator 135 perform a probability evaluation of the trajectories within the allowed waypoint parameter values. Figure 3 In more detail, the probability evaluation enables the computing device providing the trajectory optimizer 130 to save computing resources; instead of the operator performing the operation above the N max The trajectory optimizer 130 analyzes a statistically representative set of trajectories over a series of iterations to refine what trajectories belong to the elite set of trajectories. By reducing the number of trajectories analyzed, the trajectory optimizer 130 can return results faster and allow for additional functionality of the operator's device, as the course can be adjusted prior to flight and / or while the aircraft is in flight as new data is generated (e.g., weather patterns change as other aircraft adjust their courses).

[0049] At block 270, the trajectory optimizer 130 identifies moving waypoints in the elite trajectory set. The trajectory optimizer 130 removes waypoints from the elite trajectory where the heading, speed, and altitude remain constant, or where the waypoints are subject to operator requirements (i.e., a specified heading / speed / altitude must occur at the associated waypoint). In other words, those waypoints that are "static" in value for the trajectory are removed, and the remaining waypoints in the elite trajectory where the trajectory optimizer 130 can control and specify course changes in one or more of heading, altitude, and speed are retained for optimization by the trajectory optimizer 130.

[0050] The trajectory optimizer 130 identifies the range within which these mobile waypoints can move within the course. The trajectory optimizer 130 can use a preset distance, the presence of a static waypoint, and / or a radial distance from another mobile waypoint to identify the range of a given mobile waypoint. For example, the trajectory optimizer 130 can identify a distance range for a location waypoint that enables the location waypoint to appear X miles ahead or behind the initially specified location. On the other hand, the position of the mobile waypoint in the course of the course can be adjusted until a static waypoint is reached or an intermediate point between the mobile waypoint and an adjacent waypoint is reached.

[0051] At block 280, the trajectory optimizer 130 performs a global evaluation of the multi-dimensional space defined by the course according to the range of the moving waypoints determined at block 270. The trajectory optimizer 130 retains the values ​​of the heading, altitude, and speed to which the aircraft is to change at the moving waypoints, but varies where within the course those changes occur. In some aspects, the trajectory optimizer 130 performs the global evaluation via the DIRECT algorithm or another global maximum / minimum detection algorithm known to those of ordinary skill in the art to detect a series of positions in an elite trajectory that maximizes the efficiency advantage for the moving position.

[0052] Once identified, the trajectory optimizer 130 sends the optimal trajectory (i.e., the trajectory determined to have the highest efficiency advantage) and the locations and values ​​of the waypoints therein to one or more navigation devices 140. For example, the autopilot of an aircraft requesting an optimized course may receive the course, thereby controlling the aircraft to follow the course according to the optimal trajectory. In another example, a pilot may request several courses that are optimized via different weightings of efficiency advantage elements (e.g., one that prioritizes passenger comfort, short duration flight, fuel efficiency, etc.) and select one of the several courses for use by the aircraft. In another example, the navigation device 140 of an ATC device receives the optimized course to change the route of aircraft around a developing storm to minimize the negative impact on those aircraft. The aircraft receives the selected optimized course and instructs the pilot to follow the course and / or automatically follows the optimized course. The method 200 may then end.

[0053] Figure 3 is a flow chart of a method 300 for performing probability evaluation when optimizing a vertical course. In some aspects, the method 300 may be used as a method for Figure 2 260 of the method 200 described above.

[0054] At box 310, the trajectory optimizer 130 generates an initial parameter probability distribution based on the allowed waypoint parameter values. The trajectory optimizer 130 investigates the values ​​of altitude and speed specified in each parameter of the initial parameters (e.g., those set at box 220 of method 200). The counts of each parameter value are tallied at each waypoint to determine how many percentages of values ​​the probabilistic trajectory set needs to include to represent the exhaustive set. Tables 1 and 2 show different example counts for one hundred trajectories with three potential values ​​of speed and altitude, respectively. The value of heading is omitted in the current example so as not to distract from the described inventive concept, but the heading value may be included in various aspects. The trajectory optimizer 130 may perform separate tallies of parameter values ​​to create separate tables to be represented by the probabilistic trajectory set.

[0055] Table 1

[0056] Waypoint 1 Waypoint 2 Waypoint 3 Speed ​​1 = 64 Speed ​​1 = 18 Speed ​​1 = 25 Speed ​​2 = 18 Speed ​​2 = 64 Speed ​​2 = 25 Speed ​​3 = 18 Speed ​​3 = 18 Speed ​​3 = 50

[0057] Table 2

[0058] Waypoint 1 Waypoint 2 Waypoint 3 Height 1 = 10 Height 1 = 0 Height 1 = 5 Height 2 = 20 Height 2 = 50 Height 2 = 90 Height 3 = 70 Height 3 = 50 Height 3 = 5

[0059] At block 320, the trajectory optimizer 130 generates a trajectory based on a predefined number (N P ) generates a probabilistic trajectory set using the parameter probability distribution of the trajectory of the waypoint. For example, the probabilistic trajectory set generated by the trajectory optimizer 130 using the example distributions in Tables 1 and 2 will include a predefined number of trajectories such that the probabilistic trajectory set will make the waypoint 1 64% of the speed at 1 , 18% is set as speed 2 , 18% is set as speed 3 , while also making waypoints 1 10% of the height at 1 , 20% is set as height 2 , 70% is set as the height 3 The trajectory optimizer 130 generates a set of probabilistic trajectories to include similar representative values ​​of heading, speed, and altitude for all waypoints included in the trajectory.

[0060] At block 330 , the trajectory score calculator 135 determines an efficiency metric for each trajectory in the set of probabilistic trajectories and identifies a predefined number (N ) of trajectories that are an “elite” subset of the set of probabilistic trajectories based on the efficiency metric. e ). These elite trajectories include trajectories within the upper range of the evaluation trajectory set for a given iteration of blocks 320-350. In other words, the trajectory score calculator 135 selects from N P The probability of the trajectories is concentrated to select the N with the highest efficiency metric. e trajectories that may be output from method 300 or used as the basis for a subsequent set of probability analyses.

[0061] At block 340, the trajectory optimizer 130 determines whether convergence has been reached. Convergence may be reached when the efficiency merit values ​​of all trajectories in the elite trajectory set exceed a predefined threshold if a predefined number of iterations have occurred, or when the observed change in the average efficiency merit value of the elite trajectory set between iterations falls below a difference threshold. If convergence has been reached, the method 300 may output the elite trajectory set and end. If convergence has not been reached, the method 300 proceeds to block 350.

[0062] At block 350 , the trajectory optimizer 130 calculates the trajectory based on N eThe trajectory optimizer 130 then generates a subsequent parameter probability distribution for the elite set of N trajectories. The trajectory optimizer 130 investigates the values ​​of altitude and speed specified in each elite trajectory. The counts of each parameter value are summed at each waypoint to determine what percentage of values ​​a subsequent probability trajectory set needs to include to represent the elite set. After block 350, the method 300 returns to block 320. The trajectory optimizer 130 may use the parameter probability distributions from the elite trajectories of the current iteration as the N values ​​in the next iteration. P The basis of the probability sets of the trajectories is iterated through blocks 320 - 350 several times.

[0063] FIG. 4A to FIG. 4C An example channel is shown for which one element of a trajectory is being optimized. FIG. 5A to FIG. 5C Shows the different elements of the trajectory being optimized FIG. 4A to FIG. 4C Example waterway. Figure 4A A first state 401 of a first course element vector 410 is shown, which may be defined by a set of elements (eg, heading, speed, altitude) of an elite trajectory. Figure 5A A first state 501 of a second channel element vector 510 is shown, which is represented by FIG. 4A to FIG. 4C The elite track shown is defined by a different set of elements (e.g., heading or speed when altitude is shown in the first course element vector 410). As will be appreciated, a course includes a heading element, an altitude element, and a speed element of the course of the course; each element can be visualized by a separate course element vector 410 / 510. FIG. 4A to FIG. 4C The first course element vector 410 shown represents the value of one element of the route; other elements may be shown in a similar one-element format (e.g., FIG. 5A to FIG. 5C ), or in other aspects, several elements may be shown in a multi-dimensional space (e.g., 3D, 4D, 5D, etc.).

[0064] Along the first channel element vector 410, Figure 4A Several waypoints 420a-420g (generally, waypoints 420) are shown. Figure 5A Corresponding waypoints 520a-520g (generally, waypoint 520) are shown in FIG. Figure 4B , waypoints 420 are categorized as static waypoints 430a-430d (generally, static waypoints 430) or mobile waypoints 440a-440c (generally, mobile waypoints 440), which are shown together with corresponding ranges 450a-450c (generally, ranges 450). Similarly, in Figure 5B, waypoints 520 are categorized as static waypoints 530a-530d (generally, static waypoints 530) or mobile waypoints 540a-540c (generally, mobile waypoints 540), which are shown together with corresponding ranges 550a-550c (generally, ranges 550).

[0065] Static waypoints 430 / 530 represent waypoints 420 / 520 for which no element of the route defined by the trajectory being optimized has changed (e.g., one or more of heading, altitude, or speed may change, but no element of the waypoints 420 / 520 has changed in a particular trajectory). Moving waypoints 440 / 540 represent waypoints 420 / 520 for which the route is adjusted by one or more of heading, altitude, or speed in the trajectory. Route elements may be changed for one element (such as at waypoints 420d / 520d) or for multiple elements (such as at waypoints 420c / 520c and 420f / 520f). For example, because the value of the element represented by the second course element vector 510 changes at waypoint 520d, even if the value of the element represented by the first course element vector 410 remains constant at waypoint 420d, the corresponding waypoint 420d / 520d is also classified as moving.

[0066] The mobile waypoint 440 / 540 is associated with a range over which the position of the mobile waypoint 440 / 540 is varied to evaluate how to optimize the course represented by the course vector 410 / 510. A search algorithm (e.g., a DIRECT algorithm) is used to find a position in the multidimensional space of trajectories to which the mobile waypoint 440 / 540 can be moved, within the space defined by the range 450 / 550, to maximize the efficiency metric calculated for the particular trajectory being optimized. The mobile waypoint 440 / 540 in the second state 402 / 502 is in Figure 4C and Figure 5C From the corresponding Figure 4B and Figure 5B The position of the moving waypoints 440 / 540 in the starting position in the course affects when / where in the course the changes indicated by those moving waypoints 440 / 540 occur.

[0067] Reference is made herein to the aspects presented in the present disclosure. However, the scope of the present disclosure is not limited to the specifically described aspects. On the contrary, any combination of the following features and elements, whether or not related to different aspects, is contemplated as implementing and practicing the contemplated aspects. In addition, although the aspects disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given aspect is not limiting the scope of the present disclosure. Therefore, except as expressly stated in the claims, the attached aspects, features, aspects and advantages are merely illustrative and are not considered to be elements or limitations of the appended claims. Similarly, reference to the "present invention" should not be interpreted as a summary of any inventive subject matter disclosed herein, and should not be considered to be elements or limitations of the appended claims except as expressly stated in the claims.

[0068] Furthermore, the present disclosure includes implementations according to the following clauses:

[0069] Clause 1. A method comprising the steps of:

[0070] generating a plurality of waypoints with allowed parameters for the route, at which a course of the aircraft including a heading element, an altitude element, and a speed element is adjustable;

[0071] calculating a number of unique trajectories available based on the plurality of waypoints;

[0072] In response to determining that the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite set of trajectories including those trajectories having an efficiency metric within an upper bound of the set of trajectories being evaluated;

[0073] identifying a moving waypoint in a trajectory of the elite trajectory set;

[0074] performing a global optimal path evaluation, wherein the positions of the mobile waypoints are adjusted within the associated trajectory to identify an optimal trajectory for the aircraft on the course; and

[0075] The optimal trajectory is provided to the aircraft to follow the course according to the optimal trajectory.

[0076] Clause 2. The method of clause 1, wherein, in response to determining that the number of unique trajectories is less than a threshold number of trajectories:

[0077] generating an exhaustive set of trajectories corresponding to the unique trajectories; and

[0078] An exhaustive evaluation is performed on the exhaustive set of trajectories to identify an elite set of trajectories including those trajectories having efficiency metrics within an upper bound of the exhaustive set of trajectories.

[0079] Clause 3. The method according to any one of clauses 1 to 2, wherein the step of probability assessment further comprises the following steps:

[0080] generating an initial parameter probability distribution according to the plurality of waypoints;

[0081] generating a set of probabilistic trajectories of a predefined number of trajectories based on the initial parameter probability distribution; and

[0082] The elite trajectory set is selected from the probabilistic trajectory set.

[0083] Clause 4. The method according to clause 3, wherein the step of selecting the elite trajectory set from the probabilistic trajectory set further comprises the following steps:

[0084] generating a subsequent parameter probability distribution based on the waypoints included in the elite trajectory set;

[0085] generating a set of subsequent probabilistic trajectories of a predefined number of trajectories based on the subsequent parameter probability distribution; and

[0086] A new trajectory set is selected from the subsequent probability trajectory set as the elite trajectory set.

[0087] Clause 5. The method of any one of clauses 1 to 4, wherein the moving waypoint is a waypoint at which at least one element of the route is adjusted in a particular trajectory of the elite trajectory set; and

[0088] When performing the global optimal path evaluation, waypoints in the specific trajectory at which at least one element of the route is not adjusted are removed from the specific trajectory.

[0089] Clause 6. A method according to any one of clauses 1 to 5, wherein the position of the mobile waypoints is adjusted within the associated trajectory within a range defined by intermediate points between the mobile waypoints located on the trajectory.

[0090] Clause 7. The method of any one of clauses 1 to 6, wherein the waypoints with allowed parameters specify at least one criterion selected from the group consisting of:

[0091] longitude;

[0092] latitude;

[0093] Cabin noise levels;

[0094] Ambient noise level;

[0095] The available thrust of the aircraft;

[0096] Elapsed flight time;

[0097] The aircraft's altitude above sea level;

[0098] The aircraft's altitude above ground;

[0099] The weight of the aircraft;

[0100] Flight cumulative efficiency advantages; and

[0101] Aircraft operational events.

[0102] Clause 8. A system, comprising:

[0103] processor;

[0104] A memory including instructions that, when executed by the processor, enable the system to:

[0105] generating a plurality of waypoints with allowed parameters for the route, at which a course of the aircraft including a heading element, an altitude element, and a speed element is adjustable;

[0106] calculating a number of unique trajectories available based on the plurality of waypoints;

[0107] In response to determining that the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite set of trajectories including those trajectories having an efficiency metric within an upper bound of the set of trajectories being evaluated;

[0108] identifying a moving waypoint in a trajectory of the elite trajectory set;

[0109] performing a global optimal path evaluation, wherein the positions of the mobile waypoints are adjusted within the associated trajectory to identify an optimal trajectory for the aircraft on the course; and

[0110] The optimal trajectory is provided to the aircraft to follow the course according to the optimal trajectory.

[0111] Clause 9. The system of clause 8, wherein, in response to determining that the number of unique trajectories is less than a threshold number of trajectories, the system is further enabled to:

[0112] generating an exhaustive set of trajectories corresponding to the unique trajectories; and

[0113] An exhaustive evaluation is performed on the exhaustive set of trajectories to identify an elite set of trajectories including those trajectories having efficiency metrics within an upper bound of the exhaustive set of trajectories.

[0114] Clause 10. A system according to any one of clauses 8 to 9, wherein, in order to perform the probability assessment, the system is further enabled to:

[0115] generating an initial parameter probability distribution according to the plurality of waypoints;

[0116] generating a set of probabilistic trajectories of a predefined number of trajectories based on the initial parameter probability distribution; and

[0117] The elite trajectory set is selected from the probabilistic trajectory set.

[0118] Clause 11. The system of clause 10, wherein, in order to select the elite trajectory set from the probabilistic trajectory set, the system is further enabled to perform the following operations:

[0119] generating a subsequent parameter probability distribution based on the waypoints included in the elite trajectory set;

[0120] generating a set of subsequent probabilistic trajectories of a predefined number of trajectories based on the subsequent parameter probability distribution; and

[0121] A new trajectory set is selected from the subsequent probability trajectory set as the elite trajectory set.

[0122] Clause 12. The system of any of clauses 8 to 11, wherein the moving waypoint is a waypoint at which at least one element of the route is adjusted in a particular trajectory of the elite trajectory set; and

[0123] When performing the global optimal path evaluation, waypoints in the specific trajectory at which at least one element of the route is not adjusted are removed from the specific trajectory.

[0124] Clause 13. The system of any one of clauses 8 to 12, wherein the position of the mobile waypoints is adjusted within the associated trajectory within a range defined by intermediate points between the mobile waypoints located on the trajectory.

[0125] Clause 14. The system of any of clauses 8 to 13, wherein the waypoints with allowed parameters specify at least one criterion selected from the group consisting of:

[0126] longitude;

[0127] latitude;

[0128] Cabin noise levels;

[0129] Ambient noise level;

[0130] The available thrust of the aircraft;

[0131] Elapsed flight time;

[0132] The aircraft's altitude above sea level;

[0133] The aircraft's altitude above ground;

[0134] The weight of the aircraft; and

[0135] Aircraft operational events.

[0136] Clause 15. A non-transitory computer-readable medium comprising instructions that, when executed by a processor, enable the processor to perform operations comprising:

[0137] generating a plurality of waypoints with allowed parameters for the route, at which a course of the aircraft including a heading element, an altitude element, and a speed element is adjustable;

[0138] calculating a number of unique trajectories available based on the plurality of waypoints;

[0139] In response to determining that the number of unique trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the unique trajectories to identify an elite set of trajectories including those trajectories having an efficiency metric within an upper bound of the set of trajectories being evaluated;

[0140] identifying a moving waypoint in a trajectory of the elite trajectory set;

[0141] performing a global optimal path evaluation, wherein the positions of the mobile waypoints are adjusted within the associated trajectory to identify an optimal trajectory for the aircraft on the course; and

[0142] The optimal trajectory is provided to the aircraft to follow the course according to the optimal trajectory.

[0143] Clause 16. The computer-readable medium of clause 15, wherein in response to determining that the number of unique trajectories is less than a threshold number of trajectories:

[0144] generating an exhaustive set of trajectories corresponding to the unique trajectories; and

[0145] An exhaustive evaluation is performed on the exhaustive set of trajectories to identify an elite set of trajectories including those trajectories having efficiency metrics within an upper bound of the exhaustive set of trajectories.

[0146] Clause 17. The computer-readable medium of any one of clauses 15 to 16, wherein the operation of probability evaluation further comprises the following operations:

[0147] generating an initial parameter probability distribution according to the plurality of waypoints;

[0148] generating a set of probabilistic trajectories of a predefined number of trajectories based on the initial parameter probability distribution; and

[0149] The elite trajectory set is selected from the probabilistic trajectory set.

[0150] Clause 18. The computer-readable medium of clause 17, wherein selecting the elite trajectory set from the probabilistic trajectory set further comprises the following operations:

[0151] generating a subsequent parameter probability distribution based on the waypoints included in the elite trajectory set;

[0152] generating a set of subsequent probabilistic trajectories of a predefined number of trajectories based on the subsequent parameter probability distribution; and

[0153] A new trajectory set is selected from the subsequent probability trajectory set as the elite trajectory set.

[0154] Clause 19. The computer-readable medium of any of Clauses 15 to 18, wherein the moving waypoint is a waypoint at which at least one element of the route is adjusted in a particular trajectory of the elite trajectory set; and

[0155] When performing the global optimal path evaluation, waypoints in the specific trajectory at which at least one element of the route is not adjusted are removed from the specific trajectory.

[0156] Clause 20. The computer-readable medium of Clause 15, wherein the position of the moving waypoints is adjusted within the associated trajectory within a range defined by intermediate points between the moving waypoints located on the trajectory.

[0157] While the foregoing is directed to various aspects of the present invention, other and further aspects of the invention may be envisaged without departing from the basic scope thereof, and the scope of which is to be determined by the appended claims.

Claims

1. A method for vertical channel optimization, the method comprising the following steps: Generating (220) a plurality of waypoints (420, 520) with allowed parameters for the course, at which a course of the aircraft including a heading element, an altitude element and a speed element is adjustable; calculating (230) a number of tracks available based on the plurality of waypoints (420, 520); In response to determining that the number of trajectories is greater than a threshold number of trajectories, performing a probability evaluation (260) on the trajectories to identify an elite set of trajectories including a plurality of trajectories from the set of evaluated trajectories having values ​​of the efficiency metric greater than an upper range threshold; distinguishing (270) mobile waypoints (440, 540) from static waypoints in the plurality of trajectories in the elite trajectory set; performing a global optimal path evaluation (280), wherein the position of the mobile waypoint is adjusted within the associated trajectory to identify an optimal trajectory for the aircraft on the course; as well as The optimal trajectory is provided to the aircraft to follow a course according to the optimal trajectory.

2. The method according to claim 1, wherein: In response to determining that the number of trajectories is less than a threshold number of trajectories: generating (240) an exhaustive set of trajectories corresponding to the trajectories; and An exhaustive evaluation (250) is performed on the exhaustive set of trajectories to identify an elite set of trajectories including a second plurality of trajectories from the exhaustive set of trajectories having values ​​of an efficiency metric greater than a second upper range threshold.

3. The method according to any one of claims 1 to 2, wherein: The step of probability assessment also includes the following steps: generating (310) an initial parameter probability distribution based on the plurality of waypoints (420, 520); generating (320) a probabilistic trajectory set of a predefined number of trajectories based on the initial parameter probability distribution, such that the probability distribution of the parameters of the probabilistic trajectory set matches the initial parameter probability distribution; and The elite trajectory set is selected (330) from the probabilistic trajectory set.

4. The method according to claim 3, wherein: The step of selecting the elite trajectory set from the probabilistic trajectory set further comprises the following steps: generating (350) a subsequent parameter probability distribution based on the waypoints (420, 520) included in the elite trajectory set; generating (320) a set of subsequent probability trajectories of a predefined number of trajectories based on the subsequent parameter probability distribution, such that a second probability distribution of parameters of the elite trajectory set matches the subsequent parameter probability distribution; and A new trajectory set is selected (330) from the subsequent probabilistic trajectory set as the elite trajectory set.

5. The method according to any one of claims 1 to 2, wherein: The moving waypoint (440, 540) is a waypoint (420, 520) at which at least one element of the route is adjusted in a particular track of the elite track set; and When performing the global optimal path evaluation, waypoints (430, 530) in the particular trajectory at which at least one element of the route is not adjusted are removed from the particular trajectory.

6. The method according to any one of claims 1 to 2, wherein: The position of the mobile waypoints is adjusted within the associated trajectory within a range defined by intermediate points between the mobile waypoints (440, 540) located on the trajectory.

7. A system for vertical channel optimization, the system comprising: processor; A memory including instructions that, when executed by the processor, enable the system to: Generating a plurality of waypoints (420, 520) with allowed parameters for the route, at which a course of the aircraft including a heading element, an altitude element, and a speed element is adjustable; calculating a number of available trajectories based on the plurality of waypoints (420, 520); In response to determining that the number of trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the trajectories to identify an elite set of trajectories including a plurality of trajectories from the set of evaluated trajectories having values ​​of the efficiency metric greater than an upper range threshold; distinguishing mobile waypoints (440, 540) from static waypoints in the plurality of trajectories in the elite trajectory set; performing a global optimal path evaluation, wherein positions of mobile waypoints are adjusted within an associated trajectory to identify an optimal trajectory for the aircraft on the flight path; as well as The optimal trajectory is provided to the aircraft to follow a course according to the optimal trajectory.

8. The system according to claim 7, wherein: In response to determining that the number of trajectories is less than a threshold number of trajectories, the system is further enabled to: generating an exhaustive set of trajectories corresponding to the trajectories; and An exhaustive evaluation is performed on the exhaustive set of trajectories to identify an elite set of trajectories including a second plurality of trajectories from the exhaustive set of trajectories having values ​​of an efficiency metric greater than a second upper range threshold.

9. The system according to claim 8, wherein: In order to perform the probability assessment, the system is also enabled to: generating an initial parameter probability distribution based on the plurality of waypoints (420, 520); Generating a predefined number of probability trajectory sets of trajectories based on the initial parameter probability distribution, such that the probability distribution of the parameters of the probability trajectory sets is consistent with the initial parameter probability distribution; and The elite trajectory set is selected from the probabilistic trajectory set.

10. A non-transitory computer-readable medium comprising instructions that, when executed by a processor, enable the processor to perform operations for vertical course optimization, the operations comprising: Generating a plurality of waypoints (420, 520) with allowed parameters for the route, at which a course of the aircraft including a heading element, an altitude element, and a speed element is adjustable; calculating a number of available trajectories based on the plurality of waypoints (420, 520); In response to determining that the number of trajectories is greater than a threshold number of trajectories, performing a probability evaluation on the trajectories to identify an elite set of trajectories including a plurality of trajectories from the set of evaluated trajectories having values ​​of the efficiency metric greater than an upper range threshold; distinguishing mobile waypoints (440, 540) from static waypoints in the plurality of trajectories in the elite trajectory set; performing a global optimal path evaluation, wherein positions of mobile waypoints are adjusted within an associated trajectory to identify an optimal trajectory for the aircraft on the course; as well as The optimal trajectory is provided to the aircraft to follow a course according to the optimal trajectory.

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