IEEE Robotics & Automation Magazine - December 2018 - 17

x (m)

0.2

Master

-0.2
0

5

10

15

20
Time (s)
(a)

25

30

35

y (m)

0.2

Master

z (m)

Slave

-0.2
5

10

15

20
Time (s)
(b)

25

30

35

0.2

Master

40

Slave

0
-0.2
0

Base Position (m)

40

0
0

Base Rotation (°)

Slave

0

0.2

5

x

10

y

15

20
Time (s)
(c)

25

30

35

40

15

20
Time (s)
(d)

25

30

35

40

15

20
Time (s)
(e)

25

30

35

40

z

0
-0.2

0

5

10

2
Ψ

θ

φ

0
-2

0

5

10

Figure 5. (a)-(c) The position of the master and slave end effectors for a peg-in-hole task experiment with a 40-ms round-trip delay.
(d) and (e) The base positions and orientations change significantly with respect to the slave motion.

Perception
The existing techniques for perception in aerial manipulation
are, in most cases, methods adapted from aerial vehicles with-
out manipulation skills to provide positional accuracy and
place recognition [28]. The AEROARMS I&M use cases,
however, require perception modules that go beyond the state
of the art to
● accurately localize the vehicle, during both the navigation
and manipulation phases
● localize and register the target pipes
● detect the objects involved in the I&M tasks
● assist the aerial robot in picking up or releasing the crawler.
Aerial manipulators are expected to work in realistic indus-
trial scenarios, which are complex, dynamic, unstructured,
and obstacle dense and have poor or no GNSS reception;
thus, the techniques employed must be able to adapt to
unknown dynamic environmental and illumination conditions.

Perception techniques will benefit from various sensors with
different physical principles and fuse their readings. The
ones used in AEROARMS include three-dimensional (3-D)
lidar, stereo cameras, IMUs, optical flow sensors, and
ultrawideband-range sensor nodes deployed in the sce-
nario [Figure 6(a)].
In the AEROARMS I&M use cases, two operational stages
can be distinguished: 1) navigation in the environment and 2)
manipulation in close proximity to obstacles. Real-time, accu-
rate, and robust 6-DoF pose estimates of the robot are com-
puted on board by a multisensor filter based on Monte Carlo
localization (MCL) that integrates the measurements from
the 3-D lidar, the stereo camera, and the ultrawideband nodes
using a precomputed multisensor map.
In flights prior to I&M missions, a multisensor mapping
scheme builds separate maps for each sensor and treats them
as different layers of the multisensor map, with all layers
december 2018

*

IEEE ROBOTICS & AUTOMATION MAGAZINE

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17



IEEE Robotics & Automation Magazine - December 2018

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