Systems and methods for unmanned aerial vehicle (UAV) navigation are presented. In a preferred embodiment, a UAV is configured with at least one flight corridor and flight path, and a first UAV flight plan is calculated. During operation of the first UAV flight plan, the UAV visually detects an obstacle, and calculates a second UAV flight plan to avoid the obstacle. Furthermore, during operation of either the first or the second UAV flight plan, the UAV acoustically detects an unknown aircraft, and calculates a third UAV flight plan to avoid the unknown aircraft. Additionally, the UAV may calculate a new flight plan based on other input, such as information received from a ground control station.

Systems and methods for unmanned aerial vehicle navigation

Structures and protocols are presented for configuring an unmanned aerial device to participate in the performance of tasks, for using data resulting from such a configuration or performance, or for facilitating other interactions with such devices.

Unmanned device interaction methods and systems

Systems and methods for controlling a movable object within an environment are provided. In one aspect, a method may comprise: determining, using at least one of a plurality of sensors carried by the movable object, an initial location of the movable object; generating a first signal to cause the movable object to navigate within the environment; receiving, using the at least one of the plurality of sensors, sensing data pertaining to the environment; generating, based on the sensing data, an environmental map representative of at least a portion of the environment; receiving an instruction to return to the initial location; and generating a second signal to cause the movable object to return to the initial location, based on the environmental map.

Multi-sensor environmental mapping

Devices, systems, and techniques for generating a graphical user interface including a three-dimensional virtual containment space for flight of an unmanned aerial vehicle (UAV) are described. In some examples, the graphical user interface may be generated based on user input defining a virtual boundary for the flight of the UAV.

Autonomous airspace flight planning and virtual airspace containment system

Various systems, methods, for unmanned aerial vehicles (UAV) are disclosed. In one aspect, UAVs operation in an area may be managed and organized by UAV corridors, which can be defined ways for the operation and movement of UAVs. UAV corridors may be supported by infrastructures and/or systems supported UAVs operations. Support infrastructures may include support systems such as resupply stations and landing pads. Support systems may include communication UAVs and/or stations for providing communications and/or other services, such as aerial traffic services, to UAV with limited communication capabilities. Further support systems may include flight management services for guiding UAVs with limited navigation capabilities as well as tracking and/or supporting unknown or malfunctioning UAVs.

Unmanned aerial vehicles

A collision and conflict avoidance system for autonomous unmanned air vehicles (UAVs) uses accessible on-board sensors to generate an image of the surrounding airspace. The situation thus established is analyzed for imminent conflicts (collisions, TCAS violations, airspace violations), and, if a probable conflict or collision is detected, a search for avoidance options is started, wherein the avoidance routes as far as possible comply with statutory air traffic regulations. By virtue of the on-board algorithm the system functions independently of a data link. By taking into account the TCAS zones, the remaining air traffic is not disturbed unnecessarily. The system makes it possible both to cover aspects critical for safety and to use more highly developed algorithms in order to take complicated boundary conditions into account when determining the avoidance course.

Collision and conflict avoidance system for autonomous unmanned air vehicles (UAVs)

Es
wird ein Kollisions- und Konfliktvermeidungssystem fur
autonome unbemannte Flugzeuge (UAV) vorgeschlagen, bei dem das System
verfugbare Onboard Sensoren verwendet, um sich ein Bild
des umgebenden Luftraumes zu machen, die so erstellte Lage auf drohende
Konflikte (Kollisionen, TCAS Verletzungen, Luftraumverletzungen)
hin untersucht wird und bei Feststellung eines Problems eine Suche
nach Ausweichmoglichkeiten gestartet wird, wobei die Ausweichrouten,
soweit moglich, den vorgeschriebenen Luftverkehrsregeln
entsprechen Die Einfuhrung des Systems in autonomen
UAVs zur Konflikt- und Kollisionsvermeidung erlaubt es, diese im
zivilen und militarischen Luftraum parallel und transparent
zu konventionellen Flugzeugen einzusetzen Durch den Onboard Algorithmus
funktioniert das System unabhangig eines Datenlinks Durch
Berucksichtigung der TCAS Zonen erfolgt keine unnotige
Belastigung des restlichen Luftverkehrs Das hybride System
erlaubt, sowohl sicherheitskritische Aspekte abzudecken als auch
hoher entwickelte Algorithmen einzusetzen, um komplizierte
Randbedingungen bei der Bestimmung des Ausweichkurses zu berucksichtigen

Kollisions- und Konfliktvermeidungssystem für autonome unbemannte Flugzeuge (UAV)

This paper deals with the decision-making process, as required within an autonomously operating unmanned air vehicle. After describing the elements of this process and their interaction, formation flight is chosen as an example to explain the concept. Formation flight drives the basic concept to a new level of complexity, since it not only involves the decision making process within one aircraft, but also requires the decision making process to autonomously organize a group of aircraft. This is illustrated by the building, the maintaining and the breakup of a formation. Comparisons to human decision making show that the basic concept of decision making can be similar in between machines and human.

Autonomous Decision-Making Applied onto UAV Formation Flight

A functional architecture is proposed, which allows the autonomous operation of unmanned aerial vehicles. This hierarchical architecture is driven by certification constraints, the necessary interaction with the ground control stations and must enable future growth and variable levels of autonomy. On the highest level there are modules for vehicle and mission management. Subordinate to them is the management of payload, communications, onboard energy and flight itself. Within each of these modules, a process containing decision making, planning, execution and monitoring/interpretation is continuously running. Information is distributed across the architecture. (Re-) planning of the mission is shared across several modules, but with clear interfaces.

Functional Architecture for Autonomous Vehicle Operations

A collision and conflict avoidance system for an autonomous unmanned air vehicle (UAV) is provided. The system uses available on-board sensors to generate an image of the airspace surrounding the UAV. The sensor data is analyzed for imminent conflicts (collisions, TCAS violations, airspace violations), and, if a problem is detected, a search for possible avoidance routes is started. The avoidance routes as far as possible comply with statutory air traffic regulations. Depending on the available time, a short-term reactive algorithm using direct flight control system commands, or a medium-term path planning algorithm using a flight plan optimized under aeronautical and economical boundary conditions, may be used. In either case, the UAV may be returned to the original route. The system is particularly suited to allow the use of autonomous UAVs in both civil and military airspace.

Johannes Beck

Papers

Collision and conflict avoidance system for autonomous unmanned air vehicles (UAVs)

Kollisions- und Konfliktvermeidungssystem für autonome unbemannte Flugzeuge (UAV)

Autonomous Decision-Making Applied onto UAV Formation Flight

Functional Architecture for Autonomous Vehicle Operations

Collision avoidance system for autonomous unmanned air vehicles (UAVs)