Results for “drone corridor”

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. There exists a gamut of approaches to solving merging and intersection crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to solving these problems that is based on distributed cooperation between the UAVs/UASs and the infrastructure. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing UAVs/UASs to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them.

Distributed↗

NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗

NASA NTRSDate not supplied

Achieving Equilibrium for Dense, Integrated, Vehicle Navigation

Drone usage has proliferated in recent years with many applications that have market-changing potential. Applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. In this paper, the term drone is used to mean both Unmanned Aerial Vehicle and small Unmanned Aircraft System vehicles. Flight infrastructure can currently only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay atone airport sends ripple effects through the system, causing more delays and missed connections. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the NAS is expected to skyrocket to millions, potentially congesting the airspace resulting in possible separation violations. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is a safety critical property for drones in the airspace. This paper addresses separation in time and in distance for high volume corridors (en-route) and lanes(on ground). The requirements and necessary conditions for maintaining proper separation and reaching maximum throughput for a given corridor/lane are addressed assuming unidirectional corridors/lanes where an aircraft arrives from one side and departs from the opposite side. Simulation results are presented that show the presented solution guarantees a set of drones to reach the equilibrium state by adjusting their speed based on their distance to the aircraft in front of them. The equilibrium state is defined as a state when a set of n aircraft moving at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as a state when the aircraft cannot move at their maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach using a large number of drones and evaluate the scalability of the proposed solution.

Mahyar R. Malekpour↗

NASA NTRS2019

Analysis of At-Altitude LTE Power Spectra for C2 Communications for UAS Traffic Management

The National Aeronautics and Space Administration’s (NASA) Unmanned Aircraft Systems Traffic Management (UTM) project works to develop tools and technologies essential for safely enabling civilian low-altitude small Unmanned Aerial Systems (sUAS, also known as drones) operations. This paper presents results of work completed in the paper [1] presented at the 2018 ICNS conference where proposed approaches were explored for evaluating and analyzing sUAS Command and Control (C2) links based on commercial cellular networks. This paper focuses on the UTM Project’s Technology Capability Level 3 (TCL-3) test results which address the communications portion identified within the same paper. A software defined radio (SDR) was flown as a sUAS payload to capture received signal spectrum in Long Term Evolution (LTE) frequency bands of interest. The purpose was to measure the RF environment at UTM altitudes to characterize the interference potential. The SDR payload was flown at various stationary altitudes where the LTE over-the-air complex (I/Q) samples were captured by the SDR and later post-processed. The SDR received inputs through an omnidirectional antenna. The complex samples captured were an aggregate of transmissions received from all line-of-sight (LOS) towers within the geographic area for the specific radio frequency bandwidth the SDR is programmed to capture. Using this approach, the complex samples captured do not distinguish between the various eNodeB's (Long Term Evolution (LTE) transmitting towers). The complex samples were post processed via a Discrete Fourier Transform (DFT) algorithm to view the captured spectrum along with the power levels across the captured LTE bandwidth. This SDR payload process of capturing complex samples was done at two different regions within the US: 1) NASA's Ames Research Center (ARC) in Moffett Field, CA, and 2) Griffiss Airfield in Rome, NY. The data capture at the ARC site was done at two physical locations within the Ames campus where many stationary altitude captures where done as high as 800 ft. above ground level (AGL). The data captured at the Griffiss Airport (also known as the NY Corridor Site) were acquired at one location with three specific stationary altitude levels – {Ground Level (GL), 300 ft., and 400 ft.}. The LTE spectrum power levels were captured for two LTE carriers, AT&T and Verizon, at both sites where their respective spectra and power levels were measured and compared at various altitudes. The overall results show that there is an increase in LTE spectrum power levels at higher altitudes for drones. A detailed analysis of this data and conclusions drawn from the results are presented in this paper.

Kerczewski, Robert J.↗