Aerospace and Electronic Systems Magazine September 2016 - 38


An Autonomous Quadrotor Avoiding a Helicopter in Low-Altitude Flights
s, which gives an avoidance duration of 1.6 s. The tracking delay
in roll (f) is long (~ 0.44 s) since the error between the desired
roll and the actual roll is large at the beginning of the avoidance.
During the avoidance, the quadrotor is under angle control, and the
position control signal Xd and Yd follow the actual position X and
Y, instead. After the safety maneuver, the quadrotor switches back
to position control smoothly. After arriving at the destination (t ≥
32s), the quadrotor still has nonzero roll and pitch to counteract the
wind disturbance. Note that full thrust (U1) is applied during the
avoidance, indicating the choice of amax is the highest achievable
acceleration. Nonetheless, the quadrotor follows the generated
path closely in position control mode, indicating that the generated path works well with the sliding mode controllers, under wind
disturbance and measurement noise.
The efficiency of the safety controller could also be evaluated
by the total flight time. In this scenario, the total flight time without avoidance is 27.5 s, and that with avoidance is 31.8 s, which
gives an overall navigation delay of 4.3 s. After deducting the 1.6
s avoidance maneuver, the navigation time is increased by 2.7 s.
If the destination were 10 mi away, the total flight time would be
around 10 min. The optimality ensures that the extra time spent on
avoidance is not significant compared with the whole trip.

In this article, a safety controller is presented in the context of
high-speed manned-unmanned aircraft collision avoidance. A
computationally feasible 2D optimal safety controller minimizing
the avoidance duration is modified to be suitable for a 3D quadrotor model with RI and aerodynamic drag. The combined hybrid
controller is capable of performing optimal avoidance when flying
to a destination autonomously.
Compared with existing techniques, the proposed safety controller has two main advantages. First, it has a sense of optimality
compared with [31], [32], [44], [45]. The controller is only enabled
when necessary. Second, it is computationally feasible, which
is hard to achieve with existing optimization techniques such as
mixed-integer programming [36] and other numerical methods on
solving the Hamilton-Jacobi equations [46].
Some future work remains. First, a more sophisticated 2D
model could be deployed. Second, the safety controller should be
improved to handle a small multihelicopter avoidance scenario.
Third, the hybrid controller should be integrated with a pathfollowing algorithm, which is necessary to avoid multi-drone-todrone collision in air highways [47]. Lastly, a hardware implementation is necessary to confirm its effectiveness in real life.

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[7]
[8]
[9]
[10]

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[14]

[15]

[16]

[17]

[18]

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CONCLUSION

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SEPTEMBER 2016

http://utm.arc.nasa.gov/docs/Amazon_Determining%20Safe%20Access%20with%20a%20Best-Equipped,%Best-Served%20Model%20for%20sUAS[2].pdf http://www.gov/docs/Amazon_Determining%20Safe%20Access%20with%20a%20Best-Equipped,%Best-Served%20Model%20for%20sUAS[2].pdf http://www.gov/docs/Amazon_Determining%20Safe%20Access%20with%20a%20Best-Equipped,%Best-Served%20Model%20for%20sUAS[2].pdf http://utm.arc.nasa.gov/docs/GoogleUASAirspaceSystemOverview5pager[1].pdf http://utm.arc.nasa.gov/docs/GoogleUASAirspaceSystemOverview5pager[1].pdf http://utm.arc.nasa.gov/docs/Amazon_Revising%20the%20Airspace%20Model%20for%20the%20Safe%20Integration%20of%20UAS[6].pdf http://utm.arc.nasa.gov/docs/Amazon_Revising%20the%20Airspace%20Model%20for%20the%20Safe%20Integration%20of%20UAS[6].pdf http://utm.arc.nasa.gov/docs/Amazon_Revising%20the%20Airspace%20Model%20for%20the%20Safe%20Integration%20of%20UAS[6].pdf http://www.artsys360.com/ https://www.hokuyo-aut.jp/02sensor/07scanner/utm_30lx.html https://www.ptgrey.com/support/downloads/10132

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine September 2016