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Cooperative Deconflicting Heading Maneuvers Applied to Unmanned Aerial Vehicles in Non-Segregated Airspace

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Abstract

This paper focuses on the conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). Firstly, the airspace conflict problem of UAVs is studied and a taxonomy of conflict situation is presented. The multi-UAV conflict is studied in virtue of the graph theory. The CDR problem is casted to a nonlinear optimization problem. Secondly, a two layered optimization algorithm, which combines stochastic parallel gradient descent (SPGD) method and Sequential quadratic programming (SQP) algorithm, is presented to solve the nonlinear optimization problem. Numerical simulations are performed to demonstrate the computational efficiency of this solver. Thirdly, the proposed algorithm is extended to 3-D space. Finally, the proposed algorithm is demonstrated on several scenarios. The results demonstrate that the proposed method outperform the existing algorithms. It can obtain conflict free solutions that would not lead to unnecessary detors.

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Abbreviations

CDR:

Conflict detection and resolution

UAVs:

Unmanned aerial vehicles

SPGD:

Stochastic parallel gradient descent

TCAS:

Traffic alert and collision avoidance system

ATCs:

Air Traffic Controllers

SQP:

Sequential quadratic programming

CM:

Conflict matrix

GA:

Genetic algorithm

p i :

The position of Ai

r i :

The safe radius of Ai

v i :

The speed of Ai

D i(p i,r i):

Safe region of UAV Ai

ψ i(t):

The horizontal heading(course angle) of Ai

ϕ i :

The maneuver degree of the horizontal heading of Ai

τ :

Look forward time period

V O i|j τ :

The velocity obstacle of Aj considers Ai.

w i(t):

The horizontal turn rate

c i j :

variable to denote the conflict relationship between Ai and Aj

d ij(t):

Distance between Ai and Aj at t

d i j m i n :

The minimum distance between Ai and line Sij

S i j :

Displacement of Aj relative to Ai

P P i j :

The interaction point of line Sij and its perpendicular line from position Ai

ζ i j :

The intersection angle between Sij and AiEj

E j :

Terminal reach position of Aj in τ

α,γ :

Proportion coefficients

f f u e l :

Additional fuel cost function

β :

Coefficient transfers flight distance to fuel cost

\({\phi _{d}^{i}}\) :

The angle difference between current direction to the preference direction

R ϕ i,ϕ j f e a s i b l e :

Feasible solution region of Ai and Aj

J:

Global utility function

c k :

Standardization function describe the relationship between Ai and Aj

λ o(x):

Indicator function

γ i(t):

Vertical climbing angle

δ i :

The maneuver degree of the pitch angle of Ai

μ i :

Pitch angle rate

s v :

The direction vector of relative velocity vij in 3-D

u i :

Preference motion direction of Ai in 3-D environment

\({u_{i}^{c}}\) :

Current motion direction of Ai in 3-D environment

\({u_{i}^{n}}\) :

Changed motion direction of Ai in 3-D environment

𝜗 i :

Intersection angles between ui and \({u_{i}^{c}}\)

𝜗 c :

Intersection angles between \({u_{i}^{c}}\) and \({u_{i}^{n}}\)

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Authors and Affiliations

  1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China

    Jian Yang, Dong Yin, Lincheng Shen & Qiao Cheng

  2. Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands

    Xu Xie

Authors
  1. Jian Yang
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  2. Dong Yin
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  3. Lincheng Shen
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  4. Qiao Cheng
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  5. Xu Xie
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Correspondence to Dong Yin.

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Yang, J., Yin, D., Shen, L. et al. Cooperative Deconflicting Heading Maneuvers Applied to Unmanned Aerial Vehicles in Non-Segregated Airspace. J Intell Robot Syst 92, 187–201 (2018). https://doi.org/10.1007/s10846-017-0766-4

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