Collision avoidance based on centralized coordination of vehicle motions

Abstract

To provide a collision avoidance system that centralizes transporter and navigation analysis for centralized coordination and control of a group of transporters.SOLUTION: A computer-implemented method executed by a centralized coordinated transporter guidance system includes the steps of: centrally communicating with or acquiring analytical data about a plurality of transporters or objects detected by the centralized coordinated transporter guidance system; detecting, based on the analytical data, a collision event between one or more pairs of transporters and objects; determining trajectory coordination information for one or more of the pairs of transporters and objects involved in the collision event; and outputting the trajectory coordination information to allow the transporter to perform a correction of its trajectory.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] Collision avoidance (CA) in vehicles can involve avoiding other known objects (e.g., other vehicles, missiles, bullets, or some form of projectile) or unknown objects (e.g., buildings, solid structures, or other vehicles), where an object can be any physical object, including a vehicle. Current CA techniques focus on collision avoidance between objects with different or opposing trajectories, and not on addressing collisions between objects with close, parallel trajectories and velocities. Summary of the Invention

[0002] In one exemplary aspect, a computer-implemented method executed by a centralized coordinated vehicle guidance system may include obtaining analytical data for a plurality of vehicles or objects centrally communicating with or detected by the centralized coordinated vehicle guidance system, detecting a collision event between a vehicle-object pair based on the analytical data, determining trajectory adjustment information for a vehicle in the vehicle-object pair involved in the collision event, and outputting the trajectory adjustment information to cause the vehicle to perform a correction to its trajectory.

[0003] In another exemplary aspect, a computer program product includes a computer-readable storage medium having program instructions embodied thereon, the program instructions being executable by a computing device of a centrally coordinated vehicle guidance system to cause the computing device to perform operations including obtaining analytical data for a plurality of vehicles or objects centrally communicating with or detected by the centrally coordinated vehicle guidance system, detecting a collision event between vehicle-object pairs based on the analytical data, determining trajectory adjustment information for a vehicle in the vehicle-object pair involved in the collision event, and outputting the trajectory adjustment information to cause the vehicle to perform a correction to its trajectory.

[0004] In another exemplary aspect, a system includes a processor, a computer-readable memory, a non-transitory computer-readable storage medium associated with a computing device of a centrally coordinated vehicle guidance system, and program instructions executable by the computing device to cause the computing device to perform operations, the operations including obtaining analytical data for a plurality of vehicles or objects that are in central communication with or detected by the centrally coordinated vehicle guidance system, detecting a collision event between a vehicle-object pair based on the analytical data, determining trajectory adjustment information for a vehicle in the vehicle-object pair involved in the collision event, and outputting the trajectory adjustment information to cause the vehicle to perform a correction to its trajectory. [Brief explanation of the drawings]

[0005] [Figure 1] 1 illustrates an exemplary overview and environment according to aspects of the present disclosure. [Figure 2] 1 illustrates example components and operation of a collision avoidance system (CAS) according to an aspect of the present disclosure. [Figure 3] 1 illustrates an example flowchart of a process for identifying vehicles at risk of collision with other vehicles and objects using a coordinated and centralized vehicle communication approach according to aspects of the present disclosure. [Figure 4] 10 illustrates an example flowchart of a process for generating and outputting delta velocity information for adjusting the trajectory of a vehicle at risk of collision. [Figure 5] 1 shows an exemplary trajectory map illustrating a steer-off approach to avoid a collision between two vehicles traveling substantially the same path. [Figure 6] 1 illustrates an exemplary trajectory map showing a dog-leg approach to avoiding a collision between a vehicle and an object traveling on paths in substantially opposite directions. [Figure 7] 2 illustrates exemplary components of a device that may be used within the environment of FIG. 1. DETAILED DESCRIPTION OF THE INVENTION

[0006] Particular examples of the present disclosure will now be described with reference to the accompanying drawings. Like reference numerals refer to like elements. It should be understood, however, that the accompanying drawings are only illustrative of various embodiments described herein and are not intended to limit the scope of the various technologies described herein. The drawings show and illustrate various examples of the present disclosure.

[0007] Current collision avoidance system (CA) techniques focus on collision avoidance between opposing objects with different or opposite trajectories, rather than non-opposing objects that may have close, parallel trajectories and velocities. Therefore, current CA techniques may not adequately detect situations in which one vehicle veers off course, thereby posing a risk of striking another vehicle or object, such as in the case of friendly vehicles (e.g., team member aircraft, space vehicles, satellites, and / or other vehicles in a formation, spacecraft, vehicles on a road, etc.) that may be moving in relatively the same direction. Furthermore, existing systems are poor at detecting collisions involving multiple vehicles and providing collision avoidance guidance within a three-dimensional domain, or at providing real-time collision avoidance for space vehicles. Therefore, aspects of the present disclosure may include a collision avoidance system (CAS) that unifies vehicle and navigation analysis data for centralized coordination and control of a group of vehicles. In this manner, collisions can be avoided whether the vehicles are moving in relatively the same direction or whether the vehicles are moving in relatively opposite directions. Additionally, aspects of the present disclosure can be used to provide collision avoidance guidance (e.g., for a space vehicle) in a 3D domain involving multiple vehicles. Additionally, aspects of the present disclosure provide real-time collision avoidance (e.g., for use with a space vehicle).

[0008] In some embodiments, a centralized mission computer or centralized coordinated vehicle guidance system can monitor vehicle operations and navigation analysis data for each vehicle in the fleet, estimate the trajectory of each vehicle in the fleet, determine based on those estimated trajectories whether one or more of the vehicles are at risk of collision, and provide guidance vectors to adjust the trajectories of one or more of the vehicles to avoid the collision. As an illustrative example, the centralized coordinated vehicle guidance system can manage and coordinate the navigation of spacecraft (which may be moving in similar directions and trajectories), thereby avoiding collisions (e.g., with other vehicles or objects) and minimizing travel disruptions while also minimizing fuel losses from adjusting the trajectories of the spacecraft.

[0009] As further described herein, the centralized coordinated vehicle guidance system can adjust vehicle trajectories based on monitoring real-time analytical information about each associated vehicle and / or detected object. Exemplary analytical information that may be monitored may include navigation system data, position data, planned trajectory data, motion data, fuel consumption data, fuel level information, etc. Additionally, as part of the trajectory adjustment, the centralized coordinated vehicle guidance system may consider vehicle maneuverability, handling performance, power, acceleration / velocity, vehicle fuel efficiency, etc. In this manner, the centralized coordinated vehicle guidance system can unify vehicle data and operations to accurately track vehicle trajectories. This centralized coordination of vehicle data and vehicle operations can predict potential collisions well in advance. Additionally, the centralized coordinated vehicle guidance system can provide guidance vectors and / or control commands to adjust vehicle trajectories to avoid collisions. Additionally, collisions of vehicles traveling in substantially the same direction can be avoided.

[0010] Embodiments of the present disclosure may include systems, methods, and / or computer-readable storage media at the most detailed level of integration possible as technically possible. A computer program product may include computer-readable storage medium(s) having stored thereon computer-readable program instructions for causing a processor to perform aspects of the present disclosure.

[0011] 1 illustrates an exemplary overview and environment according to an embodiment of the present disclosure. As shown in FIG. 1, environment 100 includes transporters 110-1 through 110-N (where N is an integer greater than or equal to 2), a centralized coordinated transporter guidance system 120, and a network 130.

[0012] Vehicle 110 may include any type or variety of vehicles, such as ground vehicles, spacecraft, aircraft, etc. In some embodiments, each vehicle 110 may include sensor system 112, navigation system 114, guidance and control 116, and / or other computational and propulsion components to support the operation, guidance, navigation, and / or motion of vehicle 110. In one embodiment in which vehicle 110 is a spacecraft, sensor system 112 may include object detection sensors, motion sensors, temperature sensors, fuel level sensors, vehicle motion sensors, and / or any other variety of sensors. Navigation system 114 may include one or more computing devices that provide navigation services for vehicle 110 and may track the actual and planned route, path, and / or trajectory of vehicle 110. In some embodiments, navigation system 114 may track vehicle motion information such as speed, acceleration, position, etc. Guidance and control 116 may include one or more computing devices that control and / or guide the trajectory of vehicle 110. In some embodiments, each vehicle 110 involved in collision avoidance may communicate with a centrally coordinated vehicle guidance system 120 for centralized coordinated control of that vehicle 110. As described herein, each vehicle 110 may provide vehicle analysis data to the centrally coordinated vehicle guidance system 120. Exemplary vehicle analysis data may include sensor readings, speed, acceleration, position, actual and planned trajectories, vehicle specifications (e.g., vehicle type, vehicle size, maneuverability, technical specifications, etc.), and the like.

[0013] The centralized coordinated vehicle guidance system 120 may include one or more computing devices that centralize vehicle operation, control, and / or analysis data. Additionally, the centralized coordinated vehicle guidance system 120 may monitor vehicle and object analysis data to detect potential collisions between multiple vehicles 110 and / or multiple other objects. In some embodiments, the centralized coordinated vehicle guidance system 120 may be implemented within a vehicle 110. Additionally or alternatively, the centralized coordinated vehicle guidance system 120 may be a ground-based unit or a distributed collection of ground-based systems, servers, and computing devices. As further shown in FIG. 1 , the centralized coordinated vehicle guidance system 120 may include an object analysis component 121, a vehicle and object status processor 122, a look processor 124, a collision avoidance system (CAS) 126, and a guidance processor 128.

[0014] The object analysis component 121 may detect the presence of an object within the vicinity of the vehicle 110. As described herein, the object may include a vehicle not associated with the centrally coordinated vehicle guidance system 120, a stationary object, an airborne object, a celestial body, etc. In some embodiments, the object analysis component 121 may obtain analytical data associated with the object, such as the object's shape / dimensions, an image of the object, the object's type, speed, acceleration, path of travel, etc.

[0015] Vehicle and object status processor 122 may include one or more computing devices that ingest vehicle analysis data and object analysis data and provide all or a portion of the vehicle and / or object analysis data to look processor 124, CAS 126, and / or guidance processor 128. In some embodiments, vehicle and object status processor 122 may process, modify, crop, trim, and / or filter the object analysis data.

[0016] The look processor 124 may include one or more computing devices and determines the field of view of the vehicle 110 relative to its propulsion system (e.g., the field of view of sensors mounted on the vehicle 110). The look processor 124 may provide a look vector to the CAS 126, which indicates to the CAS 126 the direction and position with which the sensor readings are associated. The look vector allows the CAS 126 to more accurately predict potential collisions and allow adjustments to be made to avoid collisions.

[0017] The CAS 126 may include one or more computing devices that receive look vectors (e.g., from the look processor 124), processed vehicle analysis data, and / or processed object analysis data (e.g., from the vehicle and object status processor 122). The CAS 126 may monitor the received data and detect potential collisions between one or more vehicle 110 and / or objects (e.g., based on predicted trajectories, velocities, accelerations, etc.). In some embodiments, the CAS 126 may detect potential collisions further based on calculating a zero-effort miss value and a time to zero-effort miss. The CAS 126 may determine a delta velocity value that the guidance processor 128 may convert into a guidance vector. In some embodiments, the guidance processor 128 may output the guidance vector to the vehicle 110. The vehicle 110 may convert the guidance vector into propulsion system commands that, when executed, alter the trajectory of the vehicle 110-1 to avoid a potential collision.

[0018] Network 130 may include one or more wired and / or wireless networks. For example, network 130 may include a cellular network (e.g., a second-generation (2G) network, a third-generation (3G) network, a fourth-generation (4G) network, a fifth-generation (5G) network, a long-term evolution (LTE) network, a global system for mobile (GSM) network, a code division multiple access (CDMA) network, an evolutionary data optimized (EVDO) network, etc.), a public land mobile network (PLMN), and / or another network. Additionally or alternatively, network 130 may include a local area network (LAN), a wide area network (WAN), a metropolitan network (MAN), a public switched telephone network (PSTN), an ad hoc network, a managed Internet Protocol (IP) network, a virtual private network (VPN), an intranet, the Internet, an optical fiber-based network, and / or combinations thereof, or other types of networks. In an embodiment, network 130 may include copper transmission cables, optical fiber, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers.

[0019] The quality of the devices and / or networks in environment 100 is not limited to those shown in Figure 1. Indeed, environment 100 may include additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, and / or devices and / or networks arranged differently than those shown in Figure 1. Also, in some implementations, one or more of the devices in environment 100 may perform one or more functions described when performed by another one or more of the devices in environment 100. The devices in environment 100 may be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections.

[0020] FIG. 2 illustrates example components and operation of a collision avoidance system (CAS) according to aspects of the present disclosure. As shown in FIG. 2, the CAS 126 may include a collision detection component 210 and a collision avoidance component 220. In some examples, the collision detection component 210 may include one or more computing devices that receive vehicle and object analysis data and identify vehicles 110 that are at risk of collision (e.g., with other vehicles 110 and / or objects). As described herein, the collision detection component 210 may generate a zero effort miss value (Z) and a time to zero effort miss (T Z Any suitable collision detection technique may be used to determine whether a pair of vehicles 110 and / or a vehicle 110 and an object are at risk of collision, such as by calculating Z and T. Z If the value for does not meet a safety threshold, the collision detection component 210 may detect that the vehicle 110 is at risk of collision. In some embodiments, the collision detection component 210 may generate a collision event report that identifies the vehicle 110 at risk of collision. Further operation of the collision detection component 210 is described in more detail herein in connection with FIG. 3.

[0021] Collision avoidance component 220 may include one or more computing devices that receive collision event reports (e.g., from collision detection component 210) and generate collision avoidance guidance data. In some embodiments, collision avoidance guidance data may include vectors, navigation instructions / commands, etc., that, when received and executed by vehicle 110, cause vehicle 110 to adjust its trajectory to avoid collisions. Further operation of collision avoidance component 220 is described in more detail herein with respect to FIG. 4.

[0022] 2, the CAS 126 may incorporate an update loop to update the collision avoidance guidance data in a loop. For example, the CAS 126 may monitor vehicle and object analysis data and continue to generate updated collision avoidance guidance data to continuously monitor collision risk in real time and provide avoidance guidance to the vehicle 110 to avoid collision in real time.

[0023]

[0023] Figure 3 illustrates an exemplary flowchart of a process for identifying vehicles at risk of collision with other vehicles and objects using a coordinated and centralized vehicle communication approach according to aspects of the present disclosure. The blocks of Figure 3 may be implemented within the environment of Figure 1 and are described, for example, using the reference numerals of elements depicted in Figure 1. The flowchart illustrates the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure.

[0024] 3, process 300 may include generating a data structure identifying vehicle-object pairs within a defined system (block 310). For example, collision detection component 210 may generate a data structure to identify vehicle-object pairs within a defined system (e.g., a defined boundary, area, coordinate set, etc.). A "vehicle-object pair" includes a group having one vehicle and one detected object within the defined system (e.g., a pair including one vehicle and another vehicle, or a pair including one vehicle and another type of object). As an example, assume four vehicles (e.g., collision detection component 210-1, collision detection component 210-2, collision detection component 210-3, and collision detection component 210-4) are associated with centralized coordinated vehicle guidance system 120 and are within the defined system. In this scenario, one vehicle-object pair includes collision detection component 210-1 and collision detection component 210-2. Another vehicle-object pair includes collision detection component 210-1 and collision detection component 210-2. Similarly, another vehicle-object pair includes collision detection component 210-1 and collision detection component 210-3, and so on. Collision detection component 210 may store information identifying all of the vehicle-object pairs.

[0025] Process 300 may also include determining analytical data for the vehicle-object pair (block 320). For example, the collision detection component 210 may obtain analytical data for a particular vehicle-object pair identified in the data structure (e.g., vehicle or object analytical data for each vehicle 110 or object). As previously described, exemplary vehicle analytical data may include sensor readings, speed, acceleration, position, actual and planned trajectory, vehicle specifications (e.g., vehicle type, vehicle size, maneuverability, technical specifications, etc.), etc. Additionally or alternatively, vehicle analytical data may include relative speed, position, acceleration, etc. between each vehicle-object pair. Exemplary object analytical data may include object shape / dimensions, object image, object type, speed, acceleration, travel path, etc. In some examples, the vehicle and / or object analysis data can be used to plot or map future predicted trajectories between two vehicles 110 in a vehicle-object pair, and between a vehicle 110 and an object in a vehicle-object pair. As described herein, the vehicle and / or object analysis data can be used to detect vehicle collision events, as described in more detail herein.

[0026] Step 300 calculates the zero effort miss vector (Z) and the time (T Z For example, the collision detection component 210 may further include calculating Z and time T as part of detecting a vehicle collision event between a vehicle and object pair. Z In some embodiments, Z may represent the minimum relative position vector that occurs when a first vehicle 110 or object passes a second vehicle 110 or object, assuming that the thrust vector of the second object vector stops and both objects coast under gravity-only acceleration. That is, Z represents the point of closest approach, while T Z represents the time when this point occurs. Z(T Z ) B is smaller than the combined size of the intruding objects, and TZ A value of T at or near 0 corresponds to a vehicle collision event and indicates that a collision may be imminent. Z may represent the severity of the collision event. When the future predicted trajectory between the vehicle 110 and the object in the vehicle-object pair is plotted on a trajectory map, T at or near 0 may be Z The values ​​may be represented on a trajectory map as collisions between multiple vehicles 110 or between a vehicle 110 and an object (eg, as described in more detail in connection with FIGS. 5 and 6).

[0027] Step 300 also calculates Z and T Z The process may also include determining whether Z and T meet a safety threshold (block 340). Z may determine whether the closest point Z is large enough and the time T Z (Z and T are sufficiently long). Z Safety thresholds for Z and T may be configurable and may take into account the size of the vehicle and object, the safety distance, and the reactive capabilities of the vehicle 110 to perform collision avoidance maneuvers. Z The safety threshold for may be based on the level of detection. For example, an appropriately conservative level of detection may allow for earlier detection, resulting in less disruptive collision avoidance maneuvers and less gas consumption, whereas an overly conservative level of detection may result in unnecessary collision avoidance maneuvers.

[0028] For example, if Z and TZ do not meet safety thresholds (block 340—NO), process 300 may further include storing an indication that the pair (e.g., the vehicle-object pair in question) is at risk of collision in a collision event report (block 350). In some embodiments, collision detection component 210 may output a collision event report to collision avoidance component 220. In that case, the collision event report indicates that the vehicle-object pair is at risk of collision. In some embodiments described herein, collision avoidance component 220 uses the collision event report to analyze the trajectory of each vehicle between each vehicle-object pair at risk of collision and generate guidance vectors, delta velocity information, trajectory adjustment control commands, etc., which, when executed, cause vehicle 110 to take action to avoid the collision.

[0029] As further shown in FIG. 3 , process 300 may return to block 320. Blocks 320-350 may thereby be repeated for each vehicle-object pair identified in the data structure generated in step 310. In this manner, a collision risk may be identified for each vehicle-object pair. In some embodiments, collision detection component 210 may add a collision event report each time a collision risk is identified for a vehicle-object pair. Additionally, if a new vehicle 110 is associated with centrally coordinated vehicle guidance system 120 and / or a new object is detected, the data structure may be updated to reflect the newly associated and detected vehicle 110 and / or object. Process 300 may then be repeated. Also, at block 340, Z and T Z If the safety threshold is met (block 340 - YES), the process 300 may return to block 320 without storing an indication that the pair in question is at risk of collision, since meeting the safety threshold indicates that no risk of collision exists.

[0030] 4 shows an example flowchart of a process for generating and outputting delta velocity information for adjusting the trajectory of a vehicle at risk of collision. The blocks of FIG. 4 may be implemented within the environment of FIG. 1 and are described, for example, using the reference numerals of elements depicted in FIG. 1.

[0031] 4, process 400 may include identifying a reference vehicle 110 at risk of collision (block 410). For example, collision avoidance component 220 may identify the reference vehicle 110 at risk of collision based on a collision event report or an indication that a vehicle-object pair is at risk of collision (e.g., as identified by collision detection component 210 according to process 300 of FIG. 3). In some embodiments, collision avoidance component 220 may identify the reference vehicle 110 as one of the vehicles 110 in the vehicle-object pair.

[0032] Process 400 may also include identifying all vehicles and objects that are at risk of collision with the reference vehicle 110 (block 420). For example, the collision avoidance component 220 may identify all vehicles 110 and objects that are at risk of collision with the reference vehicle 110 (e.g., from a collision event report). As described above, the collision event report identifies a collision risk for each vehicle-object pair. As an illustrative example, if the reference vehicle 110 is vehicle 110-1, the collision avoidance component 220 may identify that vehicle 110-1 is at risk of collision with vehicle 110-2 and / or another object based on information obtained from the collision event report.

[0033] The process 400 may further include determining a time-to-use condition (TUC) value (block 430). In some embodiments, the TUC value may represent a level of urgency for adjusting the reference vehicle 110, and T ZThe TUC value may be based on the reference vehicle 110's speed, as well as the distance or range between the reference vehicle 110 and a vehicle or object at risk of collision with the reference vehicle 110. The TUC value may also be based on a maximum allowable threshold change in speed for the reference vehicle 110 to avoid a collision. It can be used to define a collision avoidance or trajectory adjustment approach (e.g., a gradual "steering off" approach or a sharper "dogleg" approach). For example, as described in further detail herein, a larger change in speed may increase the likelihood of avoiding a collision, with a lower deviation in the trajectory path / angle (i.e., a lower level of disruption to the original trajectory of the reference vehicle 110), but at the expense of greater fuel consumption. Conversely, a smaller change in speed may result in lower fuel consumption, but may require more frequent trajectory changes to avoid a collision (i.e., a higher level of disruption to the original trajectory). Thus, the TUC value may take into account a maximum allowable threshold change in speed for the reference vehicle 110. It may be based on an upper limit on fuel consumption, the performance of the reference vehicle 110, maneuverability, etc. Additionally or alternatively, the TUC value may take into account the maximum allowable change in orbital path or angle.

[0034] Process 400 may also include selecting a trajectory adjustment approach based on the TUC value (block 440). For example, collision avoidance component 220 may select a trajectory adjustment approach based on the TUC value. In some embodiments, the trajectory adjustment approach may represent the degree or sharpness of the trajectory adjustment (e.g., a gradual or "steer-off approach" adjustment or a sharp "dogleg approach" adjustment). In some embodiments, collision avoidance component 220 may store a threshold value that specifies which adjustment approach to select based on the TUC value. Additionally or alternatively, the trajectory adjustment approach may be further based on additional factors such as vehicle fuel efficiency, maneuverability, mission objectives, maneuvers, destination information, etc.

[0035] In general, an orbit adjustment approach may be selected to create a threshold separation between the reference vehicle 110 and the potentially collision-prone vehicle or object with minimal change in orbit and / or velocity (thereby reducing fuel consumption and disruption of the original orbit of the reference vehicle 110). While a steer-off approach may minimize changes in orbit direction (thus minimizing disruption of the mission objectives of the reference vehicle 110), a steer-off approach may require a larger change in velocity than a dogleg approach to achieve the threshold separation between the reference vehicle 110 and the potentially collision-prone vehicle or object. Thus, if the change in velocity required to avoid a collision (e.g., create separation) using a steer-off approach is below an acceptable threshold, a steer-off approach may be used, thereby minimizing changes in orbit direction and minimizing disruption of the orbit of the reference vehicle 110 (e.g., in situations where the reference vehicle 110 and the potentially collision-prone vehicle / object are moving in substantially the same direction). On the other hand, if the change in speed required to avoid a collision using a steer-off approach exceeds an acceptable threshold, a dog-leg approach may be selected instead (e.g., in a situation where the reference vehicle 110 and the vehicle / object at risk of collision are traveling in substantially opposite directions). Similarly, if a steer-off approach cannot avoid a collision even at the maximum potential speed of the reference vehicle 110, a dog-leg approach may be selected.

[0036] In some embodiments, different trajectory adjustment approaches may be selected. That is, the trajectory adjustment approach may be a quantitative value, such as the angle or direction of the trajectory adjustment, whereby the angle / direction is selected to minimize the change in direction and velocity while still creating sufficient separation to avoid collision. In general, the angle at which the trajectory is adjusted and the change in velocity may be minimized while still creating sufficient separation to avoid collision.

[0037] The process 400 may further include determining an orbit adjustment based on the orbit adjustment approach (block 450). For example, the collision avoidance component 220 may determine the orbit adjustment based on the orbit adjustment approach. In some embodiments, the orbit adjustment may specify delta velocity information, which may be either negative (e.g., to slow down the reference vehicle 110) or positive (e.g., to speed up the reference vehicle 110). Additionally or alternatively, the orbit adjustment may specify a guidance vector corresponding to a change in orbit direction and / or angle. Generally, the collision avoidance component 220 may determine the orbit adjustment based on the Z and T Z The collision avoidance component 220 may minimize changes in velocity and / or trajectory direction while meeting safety thresholds (e.g., thresholds representing the relative position between the reference vehicle 110 and other vehicles / objects, and thresholds representing the time relative to this relative position). Additionally or alternatively, the collision avoidance component 220 may optimize or minimize changes to other factors such as acceleration, fuel consumption, or trajectory perturbations. In the event that multiple vehicles are involved in a potential collision with the reference vehicle 110, the collision avoidance component 220 may determine a trajectory adjustment approach and delta velocity for each of the vehicles involved.

[0038] The process 400 may also include outputting orbit adjustment information (block 460). For example, the collision avoidance component 220 may output orbit adjustment information (e.g., for the reference vehicle 110). In some embodiments, the orbit adjustment information may take the form of guidance vectors, velocity / acceleration changes, propulsion control directives / commands, etc., which, when executed, alter the orbit of the reference vehicle 110 to avoid a collision.

[0039] 4, process 400 may be repeated for each reference vehicle 110 at risk of collision and for each vehicle-object pair (e.g., identified in the collision event report). In this manner, the trajectory of each vehicle 110 at risk of collision may be adjusted in a manner that minimizes disruptions and fuel consumption. Also, by repeating process 400 for each vehicle 110 involved in the risk of a collision event, collision avoidance component 220 may determine whether the trajectory adjustment (e.g., delta velocity information, corresponding guidance vector, etc.) generated in block 460 may result in a collision with a different vehicle. Accordingly, collision avoidance component 220 may repeat process 400 to make further adjustments to avoid further collisions. In some embodiments, before outputting the delta velocity information (e.g., at block 460), the collision avoidance component 220 performs a simulation to determine whether the trajectory adjustment will result in collision avoidance between the reference vehicle 110 and a vehicle or object within the vehicle-object pair in question, and whether a collision may occur between the reference vehicle 110 and a different vehicle or object outside the vehicle-object pair in question.

[0040] As described above in connection with FIGS. 3 and 4 , the operation and / or control of the vehicles 110 may be centrally coordinated by centrally collecting vehicle and object analytical data to track and monitor the relative positions of the vehicles and objects. This centralized collection of analytical data allows the centrally coordinated vehicle guidance system 120 to monitor the trajectories of the vehicles and objects, proactively predict collision risks, and mitigate collision avoidance actions. This allows collisions to be avoided while taking into account the trajectories of all vehicles 110 associated with the centrally coordinated vehicle guidance system 120 and all objects detected by the centrally coordinated vehicle guidance system 120. In this manner, collisions can be avoided whether the vehicles 110 are moving in relatively the same direction or whether the vehicles 110 are moving in relatively opposite directions. Furthermore, collision avoidance guidance can be provided in a 3D domain (e.g., for a space vehicle) that includes multiple vehicles 110. Furthermore, real-time collision avoidance (e.g., for use with a space vehicle) can be provided.

[0041] FIG. 5 illustrates an exemplary trajectory map showing a steer-off approach to avoid a collision between two vehicles traveling substantially the same path. Trajectory map 510 shows the predicted trajectories of two vehicles 110 (e.g., "Veh1" and "Veh2"), which may be determined based on analytical data (e.g., vehicle velocity, acceleration, motion, navigation data, etc.) for each of Veh1 and Veh2. As shown in trajectory map 510, Veh1 and Veh2 are traveling in a similar direction and along a similar path, but may be at risk of collision (e.g., based on the predicted trajectories, zero-effort miss value Z, and time T, as described above in connection with process 300). Z In this situation, a collision avoidance maneuver may be performed (e.g., by collision avoidance component 220 according to process 400). The results of such a maneuver may be displayed in trajectory map 520. In that case, Veh2 adjusts its orbital path to avoid a collision with Veh1. For example, collision avoidance component 220 may adjust its orbital path to avoid a collision with Veh1 (e.g., Z and T) while minimizing orbital heading changes, velocity changes, fuel consumption, etc.Z 5, a "stair-off" approach may be used because there is a sufficient amount of time to make trajectory adjustments without requiring sharper adjustments.

[0042] FIG. 6 illustrates an exemplary trajectory map illustrating a dog-leg approach to avoiding a collision between a vehicle and an object traveling on paths in substantially opposite directions. Trajectory map 610 illustrates a predicted trajectory of vehicle 110 (e.g., "Veh") and object ("Obj"), which may be determined based on analytical data (e.g., velocity, acceleration, motion, navigation data, etc.) for each of Veh and Obj. As illustrated in trajectory map 610, Veh and Obj are traveling in substantially opposite directions and along substantially opposite paths, and a collision may be possible (e.g., based on the predicted trajectory, zero-effort miss value Z, and time T, as described above in connection with process 300). Z In this situation, a collision avoidance maneuver may be performed (e.g., by collision avoidance component 220 according to process 400). The results of such a maneuver may be displayed in trajectory map 620. Veh then adjusts its orbital path to avoid a collision with Obj. For example, collision avoidance component 220 may adjust its orbital path to avoid a collision with Obj (e.g., Z and T) while minimizing orbital heading changes, velocity changes, fuel consumption, etc. Z 5, a "dogleg" approach may be used because the amount of time to make the trajectory adjustment is relatively short and therefore requires sharper adjustments than a "steer-off" approach.

[0043] Figure 7 illustrates exemplary components of a device 700 that may be used within the environment 100 of Figure 1. The device 700 may correspond to the vehicle 110 and the centralized coordinated vehicle guidance system 120. Each of the vehicle 110 and the centralized coordinated vehicle guidance system 120 may include one or more of the devices 700 and / or one or more components of the devices 700.

[0044] As shown in FIG. 7, device 700 may include a bus 705, a processor 710, a main memory 715, a read-only memory (ROM) 720, a storage device 725, an input device 770, an output device 775, and a communication interface 740.

[0045] The bus 705 may include a path that allows communication between the components of the device 700. The processor 710 may include a processor, microprocessor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), or another type of processor that interprets and executes instructions. The primary memory 715 may include random access memory (RAM) or another type of dynamic storage device that stores information or instructions to be executed by the processor 710. The ROM 720 may include a ROM device or another type of static storage device that stores static information or instructions used by the processor 710. The storage device 725 may include a magnetic storage medium, such as a hard disk drive, or a removable memory, such as flash memory.

[0046] Input device(s) 770 may include components that allow an operator to input information into device 700, such as control buttons, a keyboard, a keypad, or another type of input device. Output device(s) 775 may include components that output information to an operator, such as light-emitting diodes (LEDs), a display, or another type of output device. Communication interface 740 may include any transceiver-like components that allow device 700 to communicate with other devices or networks. In some implementations, communication interface 740 may include a wireless interface, a wired interface, or a combination of a wireless interface and a wired interface. In an example, communication interface 740 may receive computer-readable program instructions from a network and transfer the computer-readable program instructions into a computer-readable storage medium (e.g., storage device 725) for storage.

[0047] Device 700 may perform certain operations, as described in detail below. Device 700 may perform these operations in response to processor 710 executing software instructions contained in a computer-readable storage medium, such as primary memory 715. A computer-readable storage medium may be defined as a non-transitory memory device and should not be construed as being a transitory signal itself, including signals such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through a wire. A memory device may include memory space within a single physical storage device or memory space spread across multiple physical storage devices.

[0048] Software instructions may be read into primary memory 715 from another computer-readable storage medium, such as storage device 725, or from another device via communication interface 740. The software instructions contained in primary memory 715 may instruct processor 710 to perform operations that will be described in more detail herein. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform the operations described herein. Thus, the implementations described herein are not limited to any specific combination of hardware circuitry and software.

[0049] In some implementations, device 700 may include additional components, fewer components, different components, or components in a different arrangement than that shown in FIG.

[0050] Aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to examples of the present disclosure. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0051] The computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine, whereby the instructions, executed by the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. The computer-readable program instructions may also be stored on a computer-readable storage medium capable of instructing a computer, programmable data processing apparatus, and / or other device to function in a particular manner, whereby the computer-readable storage medium on which the instructions are stored comprises an article of manufacture, the article comprising instructions that implement aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0052] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specific logical function(s). In some alternative implementations, the functions described in the blocks may occur out of the order described in the figures. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may often be executed in reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a special-purpose hardware-based system that performs specific functions or functions, or may be executed by a combination of special-purpose hardware and computer instructions.

[0053] Embodiments of the present disclosure may include systems, methods, and / or computer-readable storage media at the most detailed level of integration possible as technically possible. A computer program product may include computer-readable storage medium(s) having stored thereon computer-readable program instructions for causing a processor to perform aspects and / or processes of the present disclosure.

[0054] In an embodiment, the computer readable program instructions may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or either source code or object code written in any combination of one or more programming languages, including object oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.

[0055] In some examples, an electronic circuit (including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA)) may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to customize the electronic circuit for purposes of implementing aspects of the present disclosure.

[0056] The computer-readable program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to execute a series of operational steps to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / acts defined in the block(s) of the flowcharts and / or block diagrams.

[0057] In embodiments, a service provider may offer to perform the processes described herein. In this case, the service provider may create, maintain, deploy, support, etc., the computer infrastructure that performs the process steps of the present disclosure for one or more customers. These customers may be, for example, any business that uses technology. In return, the service provider may receive payments from the customer(s) under subscription and / or fee agreements and / or the service provider may receive payments from selling advertising content to one or more third parties.

[0058] The foregoing description provides illustration and description, but is not intended to be exhaustive or to limit possible embodiments to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practicing the present embodiments.

[0059] In the embodiments shown in the figures, it will be apparent that various examples of the above description can be implemented in various forms of software, firmware, and hardware. The actual software code or specialized control hardware used to implement these examples is not limiting of the embodiments. Thus, the operation and behavior of these examples have been described without reference to specific software code. It will be understood that software and control hardware can be designed to implement these examples based on the description herein.

[0060] Although particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible embodiments. Indeed, many of these features can be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may directly depend on only one other claim, the disclosure of possible embodiments includes each dependent claim in combination with every other claim in the claim set.

[0061] While the present disclosure has been disclosed with respect to a limited number of embodiments, those skilled in the art, having the benefit of this disclosure, will appreciate numerous modifications and variations therefrom. It is intended by the appended claims to cover such modifications and variations as are within the true spirit and scope of the present disclosure.

[0062] While the scope of protection is determined by the appended claims, implementation of the present disclosure can be carried out in many ways, including but not limited to by the following clauses. Article 1. 1. A computer-implemented method executed by a centralized coordinated transporter induction system (120), comprising: obtaining analytical data for a plurality of transporters (110, 110-1) or objects centrally communicating with or detected by said centralized coordinated transporter guidance system (120); detecting a collision event between a vehicle and object pair based on the analyzed data; determining trajectory adjustment information for a vehicle of the vehicle-object pair involved in the collision event; and outputting the trajectory adjustment information to cause the vehicle to perform a correction of its trajectory. Article 2. Detecting the collision event includes calculating a zero effort miss value and a time value relative to the zero effort miss value; and 10. The method of claim 1, comprising determining that the zero effort miss value or the time value does not meet a safety threshold. Article 3. 3. The method of claim 1 or 2, further comprising selecting a trajectory adjustment approach based on a time-of-use condition value, wherein determining the trajectory adjustment is based on the selected trajectory adjustment approach. Article 4. 4. The method of any one of clauses 1 to 3, wherein multiple vehicles in the vehicle-object pairs move in substantially the same direction. Article 5. generating a data structure identifying a plurality of vehicle-object pairs for vehicles and objects associated with or detected by the centrally coordinated vehicle guidance system; detecting a collision event between each of the plurality of vehicle-object pairs identified in the data structure; determining a respective orbit adjustment for each vehicle in each of the plurality of vehicle-object pairs; and 5. The method of any one of clauses 1 to 4, further comprising outputting the respective trajectory adjustments that cause the respective vehicle to perform trajectory corrections. Article 6. 6. The method of claim 5, further comprising storing information about the detected crash event in a crash event report, wherein detecting the crash event is based on the crash event report. Article 7. The analysis data is Transporter sensor readings, Transporter velocity, Acceleration of the transporter, location of the transporter, the actual and planned trajectories of the vehicle; Transporter specifications, maneuverability of the transport; the shape of the object, the dimensions of the object, Image data of the object, Type of object, the speed of the object, The acceleration of the object, and 7. The method of any one of clauses 1 to 6, comprising at least one of: a path of movement of the object; Article 8. The orbit adjustment information is Changes in transporter velocity, Change in the direction of transporter movement, induction vector, Navigation data, and 8. The method of any one of clauses 1 to 7, including at least one of: Article 9. A computer program product including a computer-readable storage medium having program instructions embodied thereon, the program instructions being executable by a computing device (700) of a centrally coordinated transporter guidance system (120) to cause the computing device (700) to perform operations, the operations comprising: obtaining analytical data for a plurality of vehicles or objects centrally communicating with or detected by said centralized coordinated vehicle guidance system (120); detecting a collision event between a vehicle and object pair based on the analyzed data; determining trajectory adjustment information for a vehicle of the vehicle-object pair involved in the collision event; and outputting the trajectory adjustment information to cause the vehicle to perform a correction of its trajectory. Article 10. Detecting the collision event includes calculating a zero effort miss value and a time value relative to the zero effort miss value; and 10. The computer program product of clause 9, comprising determining that the zero effort miss value or the time value does not meet a safety threshold. Article 11. 11. The computer program product of clause 9 or 10, wherein the operations further include selecting a trajectory adjustment approach based on a time-of-use condition value, and determining the trajectory adjustment is based on the selected trajectory adjustment approach. Article 12. 12. The computer program product of any one of clauses 9 to 11, wherein multiple vehicles in the vehicle-object pairs move in substantially the same direction. Article 13. The operation further comprises: generating a data structure identifying a plurality of vehicle-object pairs for vehicles and objects associated with or detected by the centrally coordinated vehicle guidance system; detecting a collision event between each of the plurality of vehicle-object pairs identified in the data structure; determining a respective orbit adjustment for each vehicle in each of the plurality of vehicle-object pairs; and 13. The computer program product of any one of clauses 9 to 12, including outputting the respective trajectory adjustments to cause the respective vehicle to perform a trajectory correction. Article 14. 14. The computer program product of clause 13, wherein the operations further include storing information about the detected crash event in a crash event report. Article 15. The analysis data is Transporter sensor readings, Transporter velocity, Acceleration of the transporter, location of the transporter, the actual and planned trajectories of the vehicle; Transporter specifications, maneuverability of the transport; the shape of the object, the dimensions of the object, Image data of the object, Type of object, the speed of the object, The acceleration of the object, and 15. The computer program product of any one of clauses 9 to 14, comprising at least one of: a path of movement of the object; Article 16. The orbit adjustment information is Changes in transporter velocity, Change in the direction of transporter movement, induction vector, Navigation data, and 16. The computer program product of any one of clauses 9 to 15, comprising at least one of: Article 17. 1. A system including a processor (710), a computer-readable memory, a non-transitory computer-readable storage medium associated with a computing device (700) of a centrally coordinated transporter targeting system (120), and program instructions executable by the computing device (700) to cause the computing device (700) to perform operations, the operations comprising: obtaining analytical data for a plurality of vehicles or objects centrally communicating with or detected by said centralized coordinated vehicle guidance system (120); detecting a collision event between a vehicle and object pair based on the analyzed data; determining trajectory adjustment information for a vehicle of the vehicle-object pair involved in the collision event; and outputting the trajectory adjustment information to cause the vehicle to perform a correction of its trajectory. Article 18. Detecting the collision event includes calculating a zero effort miss value and a time value relative to the zero effort miss value; and 18. The system of clause 17, comprising determining that the zero effort miss value or the time value does not meet a safety threshold. Article 19. 19. The system of claim 17 or 18, wherein the operations further include selecting a trajectory adjustment approach based on a time-of-use condition value, and determining the trajectory adjustment is based on the selected trajectory adjustment approach. Article 20. 20. The system of any one of clauses 17 to 19, wherein multiple vehicles in the vehicle-object pairs move in substantially the same direction.

[0063] No element, act, or instruction used in this application should be construed as critical or essential unless explicitly described as such. Also, as used herein, the article "a" is intended to include one or more items and can be used interchangeably with "one or more." Where only one item is intended, the term "one" or similar language is used. Furthermore, the phrase "based on" is intended to mean "based at least in part on," unless explicitly stated otherwise.

Claims

1. A computer-implemented method executed by a centralized coordinated transporter guidance system (120), comprising: For vehicles and objects centrally communicating with or detected by the centrally coordinated vehicle guidance system, generating a data structure identifying a plurality of vehicle-object pairs, wherein the vehicle-object pairs are pairs including one vehicle and another vehicle or pairs including one vehicle and another type of object; obtaining analytical data for the vehicle and object pairs identified in the data structure; detecting a potential collision event among each of a plurality of vehicle-object pairs identified in the data structure based on the analyzed data; determining a predicted trajectory for each of a plurality of vehicle-object pairs identified in the data structure based on the analytical data; selecting a trajectory adjustment approach for the vehicle-object pair involved in the collision event based on a time use condition value, selecting a trajectory adjustment approach if the change in velocity required to avoid the collision using the steer-off approach is below a tolerance threshold, and selecting a dogleg approach if the change in velocity required to avoid the collision using the steer-off approach exceeds the tolerance threshold; determining trajectory adjustment information for at least one vehicle of the vehicle-object pair involved in the collision event based on the selected trajectory adjustment approach; and outputting the trajectory adjustment information to cause the at least one vehicle to perform a correction of its trajectory.

2. Detecting the potential collision event includes calculating a zero effort miss vector and a time relative to the zero effort miss vector; and determining that the zero effort miss vector or the time does not meet a safety threshold; 2. The method of claim 1, wherein the zero effort miss vector indicates a minimum relative position vector that occurs when one vehicle or object passes another vehicle or object, assuming that the thrust vector of the other vehicle or object is stopped and both the one and the other vehicle or object are coasting under gravitational acceleration alone, and the time relative to the zero effort miss vector indicates a time at which the position of the zero effort miss vector would occur.

3. For the vehicle-object pair involved in the collision event, the time use condition value is based on a time relative to a zero effort miss vector and a distance between the vehicles at risk of collision or a distance between the vehicle and the object at risk of collision. The method of claim 2.

4. A method described in any one of claims 1 to 3, wherein in selecting the trajectory adjustment approach, the stair-off approach is selected if the transporters or the objects constituting the transporter-object pair move in substantially the same direction, and the dog-leg approach is selected if the transporters or the objects constituting the transporter-object pair move in substantially opposite directions. determining a respective orbit adjustment for each vehicle within each of the plurality of vehicle-object pairs; and The method of claim 1 , further comprising outputting the respective trajectory adjustments that cause the respective vehicle to perform trajectory corrections.

6. The method of claim 5 , further comprising storing information about the detected crash event in a crash event report, and wherein detecting the potential crash event is based on the crash event report.

7. The analysis data is Transporter sensor readings, Transporter velocity, Acceleration of the transporter, location of the transporter, the actual and planned trajectories of the vehicle; Transporter specifications, maneuverability of the transport; the shape of the object, the dimensions of the object, Image data of the object, Type of object, the speed of the object, The acceleration of the object, and The method of claim 1 , further comprising at least one of: a path of movement of the object;

8. The orbit adjustment information is Changes in transporter velocity, Change in the direction of transporter movement, induction vector, Navigation data, and 8. The method of claim 1, further comprising at least one of: a vehicle propulsion control command;

9. A method according to any one of claims 1 to 8, wherein the time use condition value is based on a maximum allowable threshold for the change in speed of a reference vehicle to avoid a collision for a pair of a reference vehicle and a vehicle or object that is at risk of collision with the reference vehicle.

10. 10. A computer program product comprising a computer-readable storage medium having program instructions embodied thereon, the program instructions, when executed, causing a computing device (700) of a centrally coordinated transporter guidance system (120) to perform the method of any one of claims 1 to 9.

11. 10. A system comprising a processor (710), a computer-readable memory, and a non-transitory computer-readable storage medium associated with a computing device (700) of a centrally coordinated transporter induction system (120), wherein program instructions stored on the non-transitory computer-readable storage medium, when executed by the processor (710), cause the computing device (700) to perform the method of any one of claims 1 to 9.

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