Aerospace and Electronic Systems Magazine September 2016 - 42


An Advanced Sense and Collision Avoidance Strategy for Unmanned Aerial Vehicles in Landing Phase

Figure 2.

Figure 3.

A model of forces in UAV inertial frame.

The relative motion of a UAV and an Intruder.

Swarm Optimization (PSO) algorithm is applied to yield a realtime optimal path. In [21], a modified PSO is adopted to formulate a fuel-cost minimum path by basis spline (B-spline) curves
to avoid potential collisions. Motivated by the collective behavior
of honeybees to search food around the hive, the Artificial Bee
Colony-based path planner [22] is proposed to seek a flight trajectory within the specific constraints under complex combat field
environments. The path planning problem is cast by Ant Colony
Optimization, based on which is an optimal path considering the
enemy threats and the maximum constraint of the yaw angle produced [23]. A Genetic Algorithm (GA) method is adopted in [24]
to create the shortest collision-free path for an unmanned quadrotor after multiple recombination and mutation in the population.
The principle of AH methods is to deal with a set of solutions in
each of the iterations. The population is changed in each iteration
such that the achievement of the solution may not be guaranteed
after iterations.

PROBLEM FORMULATION

the moment derived from the thrust. The slow dynamics with respect to the longitudinal motion are modeled by x, z positions:

(2)
where state vector X = [x, z]T, control input vector u =
[de, dth]T, A = diag(m, m), B = [0, mg]T,

, and

. As indicated in Figure 2, (x, z) is the UAV
position with respect to the center of gravity in the inertial axis,
where the positive x-direction points east and positive z-direction
points upward. de is the elevator deflection limited to [-25°, 10°].
dth is the throttle angle ranging from 0.5° to 10°. m denotes the aircraft mass. For the parameters of
, MX,u,
, and
, please refer to the Appendix.

FORMULATION

The specific problem to be formulated is how a UAV is being navigated from a start position to a destination, while bypassing multiple intruders' threats in the longitudinal plane.

UAV LONGITUDINAL MODEL
In this article, the UAV longitudinal dynamics are only deployed
for the landing phase. The longitudinal dynamics involve slow dynamics along the x - z axis, whilst the fast dynamics relate to pitch
motion. The longitudinal motions are derived from Euler-Lagrange
formulation [25]. In terms of time-scale separation, the slow and
fast dynamics can be decoupled. Considering the pitch motion, the
fast dynamics can be presented by the function of pitch angle q,
and pitch rate :

From Figure 1, collision avoidance maneuvers rely on the parameters from the conflict detection process. Whether or not the UAV
trajectory has to be re-planned for avoiding multiple threats is determined by the detection unit. The miss distance following the
closest point approach [26] is investigated to define the conflict
in this study. Since the potential collision takes place at the stage
of UAV landing, the conflict detection between UAV and multiple
aircraft is mainly focused on the longitudinal plane. A simplifying
assumption of conflict detection is that intruders follow the prescribed trajectories without any avoidance maneuver. The velocity
of the intruder can therefore be calculated. The relative motion of
the UAV and one intruder is indicated in Figure 3.
Following the procedure of the closest approach distance, the
miss distance vector
can be derived as:
(3)

(1)
where Iy is the moment of aircraft inertial,
scribes the pitch moment relevant to the elevator, and
42

dedth is

where
is the distance vector locating Intruder with respect to
UAV, and the unit vector lies in the direction of the relative velocity vector of UAV with respect to the Intruder:

IEEE A&E SYSTEMS MAGAZINE

SEPTEMBER 2016



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