A decentralized control strategy for distributed air/ground traffic separation

Krozel, Jimmy; Peters, Mark; Bilimoria, Karl D.

doi:10.2514/6.2000-4062

Cited by 36 publications

(25 citation statements)

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“…The algorithm for predicting collision is the "Point of Closest Approach" (PCA) method. 17,20 This algorithm finds the instant of time t c at which the two vehicles are closest, and the miss distance r m between them at this instant. The principle of PCA is shown in Fig.…”

Section: B Reactive Collision Avoidance With Moving Obstaclesmentioning

confidence: 99%

“…Mathematical correlations between the guidance laws have also been established, which shows that the nonlinear differential geometric guidance and nonlinear geometric guidance are exactly correlated to each other with appropriate gain selections, while the linear aiming point guidance can only be approximately equivalent to the differential geometric guidance. Using an alternate Point of Closest Approach (PCA) 17,18 (in place of the collision cone philosophy) that assures a minimum safe distance between moving objects at all time, the proposed NGG and DGG algorithms have also been extended for collision avoidance with moving obstacles in both non-cooperative as well as cooperative environments. The algorithms developed have been validated from a number of simulation studies in both two-dimensional as well as three-dimensional scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…18. Each peak in the control represents each change in the aiming point 17. of 25 American Institute of Aeronautics and Astronautics Downloaded by CARLETON UNIVERSITY LIBRARY on July 30, 2015 | http://arc.aiaa.org | DOI: 10.Path found by DGG with and without autopilot lag; lag=0.Control plot for DGG with and without autopilot lag; lag=0.Path found by DGG, LGG and NGG in Control for DGG, LGG and NGG for 3D case 18 of 25 American Institute of Aeronautics and Astronautics Downloaded by CARLETON UNIVERSITY LIBRARY on July 30, 2015 | http://arc.aiaa.org | DOI: 10.XY view of the path found by DGG,LGG and NGG in three dimensions XZ view of the path found by DGG,LGG and NGG in three dimensions…”

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confidence: 99%

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Nonlinear Geometric Guidance and Di®erential Geometric Guidance of UAVs for Reactive Collision Avoidance

Mujumdar

Padhi

2010

AIAA Guidance, Navigation, and Control Conference

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Inspired by the philosophy of aiming point guidance in the missile guidance literature, two new nonlinear guidance laws, named as 'nonlinear geometric guidance' and 'differential geometric guidance', are proposed in this paper for reactive obstacle avoidance of UAVs. A collision cone approach is used to predict any possible collision with the obstacle and, if necessary, to compute an alternate aiming direction for the vehicle. The proposed guidance strategies attempt to quickly align the velocity vector of the vehicle along the aiming point within a part of the available time-to-go, which ensures quick reaction and hence safety of the vehicle. Mathematical correlations between the guidance laws have also been established, which shows that the nonlinear differential geometric guidance and nonlinear geometric guidance are exactly correlated to each other with appropriate gain selections, while the linear aiming point guidance can only be approximately equivalent to the nonlinear differential geometric guidance. Using an alternate similar approach in place of the collision cone philosophy, these algorithms have also been extended for collision avoidance with moving obstacles in both non-cooperative as well as cooperative environments. The algorithms developed have been validated from a number of simulation studies in both two-dimensional as well as three-dimensional scenarios. Results are also included with an autopilot lag compensation logic to make the guidance laws more realistic. NomenclatureX v vehicle position, m V vehicle velocity, m/s X ap aiming point, m t go time-to-go, s X r relative distance between UAV and obstacle, m β safety circle around the obstacle in the plane containing X r and V r 1 , r 2 tangents from UAV CG to the safety circle β d radius of the safety circle of obstacle u i , i = 1, 2 unit vectors joining the center of β to r i t 0 initial time, s X d destination point, m (u, v, w) velocity of vehicle along body (x, y, z) axis respectively, m/s (v * , w * ) desired velocity along body (y, z) axis respectively, m/s (θ y , θ z ) aiming angle in (XY, XZ) planes respectively, rad ((X ap ) XY , (X ap ) XZ ) projection of the aiming point on to (XY, XZ) planes respectively, m c 1 , c 2 projection constants (k v , k w ) DGG gains in (XY, XZ) planes, sLGG and NGG gains in (XY, XZ) planes, m/s 2 e v error in y-velocity, m/s (τ v , τ w ) time constant for (y, z) velocity settling respectively, s (T s v , T s w ) settling time for (y, z) velocity settling respectively, s (α, β) settling ratio for (y, z) velocity settling respectivelyrelative distance between UAV1 and UAV2, m R 0 initial separation between UAV1 and UAV2, m V i velocity of UAV i , i = 1, 2, m/s t c time of closest approach, s r m miss distance between UAV1 and UAV2, m r saf e predefined safety distance between UAV1 and UAV2, m V des i desired total velocity of UAV i , i = 1, 2, m/s r vs i vector sharing displacement for UAV i , i = 1, 2, m r res distance to resolve conflict, m a * y

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Section: B Reactive Collision Avoidance With Moving Obstaclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Nonlinear Geometric Guidance and Di®erential Geometric Guidance of UAVs for Reactive Collision Avoidance

Mujumdar

Padhi

2010

AIAA Guidance, Navigation, and Control Conference

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“…34,35,36,37,38 Many studies considering air traffic environments 39,40,41,42,43,44 There are numerous research efforts that use probabilistic approaches for trajectory/state propagation. 48,49,50,51,52,53,54,55,56,57,24 Some 48, 49 neglect wind effect in trajectory prediction.…”

Section: Introductionmentioning

confidence: 99%

Wind Field Estimation and Its Utilization in Trajectory Prediction

Kampoon

Okolo

Erturk

et al. 2015

AIAA Atmospheric Flight Mechanics Conference

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This work develops a method for onboard estimation of wind field with spatial and temporal variation based on local wind vector estimation and/or measurements from multiple aircraft flying in the same airspace. Aircraft flying in the same airspace of operation are considered airborne wind sensors scattered over the airspace because of the fact that aircraft carry along with them wind information inherent in their dynamics and kinematics. The onboard wind field estimation is formulated in the framework of parameter estimation based on various wind field models, which are different function of position and time. The online wind field estimation is utilized in trajectory prediction of aircraft flying in spatially and/or temporally varying wind. Various simulation cases are presented to demonstrate the feasibility of wind field estimation and the benefit of using such information in trajectory prediction.

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“…Up-untill-now there is no record that algorithmic methods can accommodate all the requirements stated above. There is a growing feeling in the area of large scale systems that evolutionary and self-organizing methods [14,15] can be successfully used to carry-out the challenging task of designing a realistic, multi-agent planner. Experimental tests under realistic conditions using airliners seems to support such an opinion, in particular, showing the advantages potential field methods have over classical approaches [36].…”

Section: Introductionmentioning

confidence: 99%

A decentralized, harmonic, potential field-based controller for steering dynamic agents in a cluttered environment

Masoud

2010

2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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In this paper a separation maintenance controller is developed for continuously steering a purposive, dynamic group of mobile agents away from each other in a confined, cluttered environment. The controller allows conflictfree, simultaneous use of space by the agents. It has a decentralized form that is constructed in conformity with the artificial life approach to behavior synthesis. The G-type control action used individually by the agents to govern their motion (also known as the control protocol) is extracted from a harmonic potential field. The overall controller governing the group (P-type control) has a decentralized, self-organizing, asynchronous nature with a computational effort that linearly grows with the number of agents. The capabilities of the controller are demonstrated by simulation. Proofs that the agents can avoid self and environment collision as well as converge to their targets are provided.

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A decentralized control strategy for distributed air/ground traffic separation

Cited by 36 publications

References 1 publication

Nonlinear Geometric Guidance and Di®erential Geometric Guidance of UAVs for Reactive Collision Avoidance

Nonlinear Geometric Guidance and Di®erential Geometric Guidance of UAVs for Reactive Collision Avoidance

Wind Field Estimation and Its Utilization in Trajectory Prediction

A decentralized, harmonic, potential field-based controller for steering dynamic agents in a cluttered environment

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