IEEE Robotics & Automation Magazine - December 2018 - 19

FC 3
FC 4

FC 4096 FC 4096

FC 4096 FC 4096

Pool/2
Pool/2

3 × 3 Convolution, 512

3 × 3 Convolution, 512

Pool/2

3 × 3 Convolution, 512

3 × 3 Convolution, 512

Pool/2

3 × 3 Convolution, 256

3 × 3 Convolution, 256

Pool/2

3 × 3 Convolution, 128

3 × 3 Convolution, 128

Pool/2

3 × 3 Convolution, 64

Image

3 × 3 Convolution, 64

I

Regression
(x, y, z)
T

q
Quaternion

Regression
Head

Image Features
(a)

Features

Crawler in
Image Probability
Features

w
Hit Map
Bounding Box

(b)
Figure 7. An illustration of deep learning for precise aerial vehicle localization: (a) a deep-learning architecture for aerial vehicle
localization and (b) a deep-learning architecture for crawler localization.

treating dynamic constraints in a subspace defined by the
task. Both methods are presented in this section.

manipulator designs, which we named protocentric, for
which this property is ensured [35].

Navigation in Cluttered Environments
A motion-planning approach for aerial manipulators exploit-
ing the concept of differential flatness was developed. Differ-
ential flatness is very useful because it allows the direct
planning of trajectories in a lower-dimensional space without
the need to explicitly consider the fulfillment of the system's
complex nonlinear and underactuated dynamics. For instance,
it has been exploited for dynamic grasping using aerial
manipulators with a single-link arm [34].
In the context of AEROARMS, where an aerial manipu-
lator must interact by means of an end effector, it is most
helpful if the end-effector position and orientation belong to
a flat output of the system. Unfortunately, this is not the case
for any aerial manipulator. Our main contribution in this
field has been to prove that there exists a class of aerial

Motion Planning for Task-Constrained
Aerial Manipulation
An approach that reinforces the connection between motion
planning and control in the context of aerial manipulation was
developed [36]. The underlying idea uses the controller as a
local method to connect neighboring states within a global
motion-planning algorithm. The use of the controller inside
the planner guarantees the feasibility of the trajectory for the
real system, satisfying task constraints in addition to geometric,
kinematic, and dynamic limits. Furthermore, it allows better
prediction of the behavior of the closed-loop system. Another
advantage is that using a control method that treats the redun-
dancy of the system directly allows the planner to search for a
solution in the reduced and more relevant task space. Planning
at the task level enables a more straightforward formulation of
december 2018

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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19



IEEE Robotics & Automation Magazine - December 2018

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - December 2018

Contents
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