On-board estimation, navigation and control technologies addressing safety problems of robotic flight operations in urban environment with atmospheric hazards and component failures contingencies are presented

Stepanyan, Vahram

NASA Technical Reports Server

 Advanced	Technologies	for	Ar2ﬁcial	
Intelligence	in	Flight	Applica2ons	
	
Vahram	Stepanyan	
ASRA	
NASA	Ames	Research	Center	
	
	
	
	Engineering	the	Evolu2on	
	FAST	Global	Innova2on	Forum		
	Yerevan,	Armenia	
October	29-31,		2018		
https://ntrs.nasa.gov/search.jsp?R=20180007480 2019-08-31T18:19:24+00:00Z
 Flight Intelligence
Same	
Performance	
	
v  Safety	
v  	Predictability	
v  	Society	acceptance	
v  ………………………………..	
Human	pilot	
controlled	ﬂying	machines	
Ar2ﬁcial	intelligence		
controlled	ﬂying	machines	
And	same	quality	
Passenger	comfort,	noise,	emission,	… 	
Picture	ref.	hQps://www.healthytravelblog.com/2013/08/29/safety-2ps-for-kids-ﬂying-alone/	
																					hQp://www.connec2vity4ir.co.uk/ar2cle/159844/Innovators-challenged-to-use-AI-to-boost-aircraX-performance.aspx	
Picture	ref.	hQps://www.ﬂightsafetyaustralia.com/2017/07/ge[ng-smart-ar2ﬁcial-intelligence-and-avia2on/	
																					hQps://itunes.apple.com/us/app/f-sim-space-shuQle/id352670055?mt=8	
																				hQps://alis.alberta.ca/occinfo/occupa2ons-in-alberta/occupa2on-proﬁles/helicopter-pilot/	
 Human Controlled Flight
•  Reasoning	
•  Training	
•  Memory	
•  Crea2vity	
•  Etc.	
o  Fa2gue	
o  Distrac2on	
o  Stress	
o  Panic	
o  Etc.	
Pilot	is	the	authority	
Ø  Monitoring	
Ø  Communica2on	
Ø  Nego2a2on	
Ø  Decision	making	
How	can	we	do	be8er	?	
 Intelligent Pilot Assistance
Technology requirements 
•  Reliable state estimation 
•  Maneuverability margin predictions 
•  Real-time pilot cueing 
How to prevent? 
Design onboard AI to assist the pilot Accidents happen 
5 
Reference: AIAA 2004-4811; Authors: Wilborn and Foster 
5 Second Control Limit TL2
1 Second Control Limit TL1
Maximum Possible Control
Lateral Control Input (%)
0 
0 100 -100 
100 
Current Control input
Lo
ng
itu
din
al 
Co
ntr
ol 
Inp
ut 
(%
)
 Margins From Piloting Perspective
Los-of-control	Criteria	 Intelligent	computa2ons	
 The Predictive Architecture
Es2mated		
Dynamics	
Adaptive 
Prediction 
Control  
Deficiency 
LoC Boundary  
Estimation Pilot Aid 
Aircraft 
   Pilot 
Nominal Flight  
Control System 
Reference Dynamics 
Adaptive Control Augmentation 
(Optional loop) 
Command
Pilot Cues
 LOC Example Without Cueing
LeX	wing	damage	with	no	pilot		visual	cue	
Pilot Cue (amber box) on left not displayed to pilot
 LOC Example With Cueing
LeX	wing	damage	with	pilot		visual	cue	
	
Flying Robot’s Architecture
Dynamic	
Planner	
Trajectory	
generator	
Resilient	
Controller	
Real	Time	
Es2mator	
Sensors	
User	
interface	
Onboard	Compu=ng	Power	
Environment	
Mission design
Offline planning
Advanced	
technologies	
for	ﬂying	
robots	
Pilot	is	out-of-the-loop												Ar=ﬁcial	Intelligence	is	ﬂying	
10	
Dynamic Ground 
Objects (DGO) 
Static Ground 
Objects (SGO) 
Other Aircraft 
Detect,	Operate-
Near,	and	Avoid-
Endangering	SGOs	
	
Detect,	Operate-Near,	and	
Avoid-Endangering	DGOs	
Hazard	Footprint	Awareness,		
Risk	Minimiza6on/Avoidance,	
Health	Monitoring	
Detect,	Operate-Near,	
Avoid-Endangering	Other	
AircraX	
UAS 
Environment 
Challenges 
Atmospheric 
Uncertainty 
Failures and 
Contingencies 
Degraded	RF,	SAT-
COM,	GNSS	
	
Winds	and	
microbursts	
Avoid	endangering	
objects	in	environment.	
Ground 
Operators 
C3	and
	Surve
illance
	
Requir
ement
s	
 Challenges of Robotic Flight 
Courtesy of C. Ippolito
11	
 Technology Requirements of Robotic Flight 
Ø  Estimator shall provide in real tome
•  Vehicle state and location in the environment
•  Obstacles locations and motion
•  Atmospheric disturbance
•  Detect and identify component failures 
Ø  Dynamic planner shall plan/replan in real time providing
•  Man maid strictures and terrain avoidance
•  Static and dynamic ground obstacle avoidance
•  Cooperative dynamic air obstacle avoidance
•  Acceptable air and ground risk.
Ø  Trajectory generator shall provide
•  Feasible trajectories in real time
•  Power required to traverse the trajectory
•  Minimum endurance and maximum vehicle range
•  Acceptable time time of flight
Ø  Resilient controller shall provide  
•  Stability of the vehicle 
•  Acceptable tracking performance and flight envelop
•  Compensate for failures and disturbances
•  Flight within approved 4D volume in all phases
How can we safely operate in 
high density urban environment?
12 
Use case 1: 
point-to-point 
Operator 
Customer 
Fire department 
Pull-outs 
Park 
Use case 2: 
Emergency
(High-priority Flight)
Emergency	
Landing 
1 2 
3 
Take-off	
Climb out	
Approach	
Descent	
Landing	
4 Off-nominal 
cases 
 Use case: Point-to-Point Operation
Objective
Autonomously fly the UAV in the uncertain 
wind field using onboard sensors and 
estimation algorithms. 
Challenges
•  Real-time wind estimation
•  Real time re-planning to accommodate the wind
•  Required power estimation for the new plan
•  Decision making: continue or abort
•  Find alternate landing site to abort
•  Fly UAV though approved volume and change 
plan to land to alternate landing site taking into 
account wind and battery constraint.
•  How reliable is the wind estimation?
•  Is the mission still possible?
•  Is the flight safe for the vehicle and environment?
•  Are the predicted performance bounds acceptable?
 Contingency Example: Wind
 Urban wind Field Specifics
		
Ø  Wind characteristics
•  Turbulent air flow
•  Isolated roughness
•  Wake interference
•  Skimming flow
•  Hard to predict
Ø  Wind field modeling
•  Digital 3D mapping
•  Heavy computations
•  Large memory
Ø  Not feasible onboard
Ø  Expensive transmission
Ø  Local Measurement
•  Isolated roughness
•  No infrastructure
•  Too expensive
 Wind Information
Can	on-board	sensors	and	compact	CFD	models	provide	
										suﬃciently	accurate	and	robust	wind	es2mates?		
Wind Estimation in Reflection
Wind	ﬁeld	is	generated	using	CFD	and	city	digital	map		
 Typical Component Failure
Is it still possible to safely fly this vehicle?
Time, sec
34 36 38 40 42 44 46 48 50
Cu
rre
nt
, a
m
ps
0
0.5
1
1.5
2
2.5
Failure Identification Test
 Propeller came off 
Half throttle voltage  
Is applied 
 Sample rate is 0.1sec 
0 2 4 6 8 10 12 14 16 18 20
f 1
0.5
1
1.5
2
Individual motor thrusts
0 2 4 6 8 10 12 14 16 18 20
f 2
0
0.5
1
1.5
0 2 4 6 8 10 12 14 16 18 20
f 3
0.5
1
1.5
2
Time in seconds
0 2 4 6 8 10 12 14 16 18 20
f 4
0
0.5
1
1.5
 Resilient Control Application
Ø  Motor 2 fails at t=8 sec 
Ø  Vehicle switches to safe  mode 
•  Find nearby emergence landing site 
•  Land 
Failure identification and intelligent control 
reconfiguration stabilizes the vehicle 
0 2 4 6 8 10 12 14 16 18 20
Ro
ll
-20
0
20
40
Euler angles in degrees
0 2 4 6 8 10 12 14 16 18 20
Pi
tch
-40
-20
0
20
Time in seconds
0 2 4 6 8 10 12 14 16 18 20
Ya
w
-1500
-1000
-500
0
500


Advanced Technologies for Artificial Intelligence in Flight Applications

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server