=Paper=
{{Paper
|id=Vol-2745/paper5
|storemode=property
|title=Model-Based Automated Flight Path Planning for an Ultralight Aircraft (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2745/paper5.pdf
|volume=Vol-2745
|authors=Belén Santos León,Jane Jean Kiam,Axel Schulte
|dblpUrl=https://dblp.org/rec/conf/aiia/LeonKS20
}}
==Model-Based Automated Flight Path Planning for an Ultralight Aircraft (short paper)==
https://ceur-ws.org/Vol-2745/paper5.pdf
Model-Based Automated Flight Path Planning
for an Ultralight Aircraft?
Belén Santos León1 , Jane Jean Kiam2 , and Axel Schulte2
Technical University of Munich, Germany b.santos@tum.de
1
2
Bundeswehr University Munich, Germany {jane.kiam,axel.schulte}@unibw.de
Abstract. Flight guidance is tending towards more automation in order
to increase flight safety and efficiency. An important focus is the develop-
ment and integration of an automated flight path planning system able
to assist the pilot in unexpected emergencies. This is challenging due
to the dependance on numerous internal and external constraints, but
essential for a single-pilot aircraft. We propose the use of a model-based
flight path planner in this paper that is based on PDDL+ (Planning
Domain Definition Language). We leverage the ability of the language to
model hybrid domains, which combine discrete and continuous behav-
ior, enabling hence the modelling of aircraft dynamics as well as discrete
events.
Keywords: Flight path planning · Model-based planning · Automated
guidance system · 3D trajectory generation · PDDL+.
1 Introduction
Over the last years, the increase of automation in aviation has been driven by
several factors, including the necessity to reduce the environmental impact of
air transport, to cope with a higher demand and the need to increase safety
and efficiency, to decrease considerably the accidents caused by human error, to
minimize the pilot’s workload and to reduce costs [3, 5].
Unmanned aviation has motivated substantially the inclusion of automation
in flight guidance, but this can also be beneficial for other air vehicles, such as
ultralight aircraft. Ultralight aviation is commonly practiced for sport or recre-
ational purposes by non-professional pilots, which may not react properly under
contrary circumstances such as severe weather or mechanical failure. This fact,
together with the single-pilot configuration of many ultralight aircraft, where all
the workload relies on a single person, may lead to fatal accidents [6], many of
them could be avoided with properly designed automation in flight guidance.
A crucial task in the development of an automated flight guidance system is
the integration of a path planning system capable of generating feasible flight
trajectories while considering the aircraft’s characteristics as well as the external
?
Copyright ©2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
environment. Most techniques in flight path planning are only geometric-based,
leading often to flight paths that are non-feasible [11,13]. However, an automated
planner requires a more complex planning approach like the offered by PDDL+
planners [7], which are derived mainly for general AI planning, but are capable
of considering continuous processes and discrete events. In [10], it is proven for
the first time that PDDL+ planners are also capable of performing kinodynamic
flight path planning in complex environments.
In this paper, a flight path planning system based on PDDL+ is presented
together with the modelling of the planning problem as an extension of the model
presented in [10]. The developed planning system is integrated together with a
flight control module into a flight guidance system. The trajectories calculated
for several scenarios under normal and abnormal conditions are tested with the
flight simulator X-Plane [12], a powerful simulation software that considers a
realistic model of an ultralight motorized aircraft with a complete global scenery
and huge set of customization parameters for the aircraft and its environment.
We demonstrate that the calculated flight paths are viable.
2 Flight Path Planning in PDDL+
PDDL+ is an extension of PDDL, a standard language for modelling planning
domains [7]. In PDDL+, hybrid domains are supported, i.e., systems which ex-
hibit both continuous and discrete behavior, providing hence an attractive al-
ternative for flight path planning problems.
This work exploits results obtained in [10], in which a flight path planner
was developed for the automated 2D trajectory generation of a High-Altitude
Pseudo-Satellite (HAPS). Although a different kind of platform, the flight dy-
namics modelled in the previous work for a fixed-wing aircraft can be exploited,
and extended to include 3D flight plans for an ultralight aircraft, exhibiting also
the benefit of using a model-based flight path planner.
The planning task in PDDL+ is formulated in two separate files: the domain
file and the instance or problem file (see Fig. 2). In the domain file, a series
of operators describing the dynamics of any fixed-wing aircraft and how they
are affected by internal or external factors. The instance file includes initial and
goal state, as well as all variables which uniquely define the target aircraft and
all parameters that describe the current planning scenario or environment, i.e.
aircraft’s current conditions, weather forecast or the positions of obstacles.
The kinematic model of the ultralight airplane to be encoded in PDDL+, in-
cludes the influence of the wind speed and it is formulated under the assumption
of spherical Earth:
uwind (t) + vT AS (t) cos θ(t) sin χ(t)
λ̇(t) = , (1)
(R + h(t)) cos φ(t)
vwind (t) + vT AS (t) cos θ(t) cos χ(t)
φ̇(t) = , (2)
R + h(t)
ḣ(t) = vT AS (t) sin θ(t), (3)
where λ, φ and h are the WGS84 coordinates of the aircraft: longitude, latitude
and altitude, R is the Earth’s mean radius, χ and θ are the yaw and pitch angles,
vTAS is the aircraft’s True Air Speed and uwind , vwind are the wind components
on the horizontal plane.
Fig. 1 depicts some of the included code to generate 3D trajectories. The
action to increase pitch rate is done analogously to decrease it; with the calcu-
lated pitch rate, the new pitch angle is determined and after that, the altitude is
updated. In addition, climb rate is calculated and constrained to be maintained
within its minimum and maximum values, which are platform-specific.
Fig. 1: PDDL+ Formulation to calculate pitch angle, altitude and constraint
climb rate.
3 System Description, Preliminary Test and Validation
In this section, the flight guidance system developed for the validation of the
proposed flight path planner is presented. Preliminary tests using simple sce-
narios such as a climb phase, a cruise phase, etc., are performed to provide an
overview of the validity and the quality of the plan computed by the planner.
Moreover, the results of the test will help to detect system faults and areas
for future improvement. The system architecture is depicted in Fig. 2, with the
following subsection providing more details on each subsystem.
Fig. 2: Validation system architecture
3.1 System Architecture
As shown in 2, a weather plug-in is included to extract weather forecast in-
formation online from the National Oceanic and Atmospheric Administration
(NOAA) [1]. The forecast defines 3-dimensional weather conditions at different
altitudes and divides the airspace horizontally into a square grid of 0.25x0.25
degrees in longitude and latitude. The key weather parameters to consider are
1) the wind condition, relevant to determine the translational movement of the
aircraft, and 2) the cloud coverage, so that areas with expected high coverage
clouds are defined in the planner as obstacles which must be avoided to comply
with Visual Flight Rules (VFR). While weather forecast information is used for
planning the flight paths, weather nowcast information is used for simulating the
planned paths.
An independent planner is required to solve the planning problem modelled in
PDDL+ as described in Section 2. In [10], ENHSP (Expressive Numeric Heuristic
Search Planner) is tested with favorable results at solving flight path planning
problems for a fixed-wing UAV. ENHSP supports PDDL+ semantics and hy-
brid domains, while building its heuristics on additive interval-based relaxation
(AIBR) to accelerate the search. AIBR is a pruning-safe relaxation, i.e. the re-
laxed problem has no solution only if the non-relaxed problem is not solvable
either [14]. This relaxation simplifies numeric planning, especially problematic
for complex behaviors, approximating reachable values with an upper and lower
bound. ENHSP, compared to other domain-independent planners which also
support PDDL+, is capable of solving more efficiently hybrid problems with
non-linear numeric operations, as demonstrated in [8]. These properties make
ENHSP a suitable planner for our need.
A compact map visualization to display the planned trajectory, the aircraft’s
current position, and the weather map is also integrated in the system, serving
as an interface between the flight guidance system and the pilot in the cockpit.
A proportional-integral-derivative (PID) controller for lateral, longitudinal
and velocity control developed in Matlab/Simulink environment which commu-
nicates with the flight simulator (X-Plane) via UDP (User datagram protocol)
has been developed for automatically guiding the aircraft to follow the planned
path [4]. The controller receives the flight plan position and airspeed at each
time step and commands the required control surface deflections and throttle
input to follow the desired trajectory. The integration step of the controller is
set to 0.01 s to be aligned with the data transfer rate from X-Plane, while the
execution step set for ENHSP used to solve the planning problem is 1 s. The
single-cockpit ultralight aircraft in X-Plane built according to the characteristics
of Aerolite 103 is chosen to validate the system. The characteristics of the aircraft
used for modelling the planning problem are adopted from from the aircraft’s
specifications [2] and the aircraft’s model designed in X-Plane.
3.2 Results of the Preliminary Tests
In the analyzed first scenario, the planner computes a trajectory for the ultra-
light aircraft to reach a certain position, requiring to perform several common
maneuvers, such as climb, turn, speed variation or fly at constant altitude. Exter-
nal environment consists of constant mild wind and no obstacles. However, more
scenarios under less favorable conditions, i.e. with stronger and time-varying
wind, forbidden areas or faulty aircraft, will be tested in the future for the sys-
tem validation. The calculated flight plan for this initial scenario, depicted in
Fig. 3a, is then compared with the simulated trajectory. Three key aspects from
the simulation are analyzed here:
– Position control: Figs. 3b and 3c show the lateral distance and altitude
discrepancy with respect to the desired trajectory at each time instant. The
discrepancy does not exceed 20 m at most segments of the flight path, and
reaches values over 40 m at some points.
– Speed control: It can be observed in Fig. 3d that the desired velocity is reached
and maintained with high accuracy at most time along the trajectory. Only
some fluctuations are present at the beginning of the flight path as well as
at the end, when the climbing segment starts.
– Plan duration: The obtained plan from ENHSP requires a total time of 1071
s to reach the goal position. In the simulation, it takes 1081 s to reach the
closest position to the goal state.
3.3 Discussions
The presented results, exhibit that the obtained PDDL+ plan is feasible as
the simulated plan is close to the desired trajectory. Mostly importantly, this
(a) 3D Flight Path (b) Lateral distance to flight plan
(c) Altitude discrepancy (d) Speed Discrepancy
Fig. 3: Test Results: Discrepancies between computed plans and simulated results
(using a realistic flight simulator).
confirms one of the benefits postulated in [10] on the reusability of a model-
based path planning method, i.e. the system description encoded in the domain
file for a high-altitude unmanned aircraft can be reused for another fixed-wing
piloted aerial platform flying at much lower altitudes, while the platform-specific
parameters are defined in the instance file. However, some insufficiencies of the
system were identified in the preliminary tests.
The peak of 1.8 m/s observed in the speed discrepancy plot (Fig. 3d) is the
result of a sudden speed reduction required for a climbing phase. This overshoot
and the initial disturbances, indicate a flaw in the speed controller. This fault
affects the capacity of the controller to follow the desired trajectory, leading to
worse distance and altitude discrepancy values on those phases, where the speed
diverges more from the desired value. These parameters are also influenced by the
longitudinal and lateral control subsystems, because of the lateral-longitudinal
coupling of the aircraft’s dynamics. Note that these discrepancies do not indicate
a lack of plan quality with respect to the plan feasibility.
4 Conclusion and Future Work
The presented flight planner is an extension of a 2D trajectory planner which
adds a third dimension in the trajectory generation. The independent formu-
lation of PDDL+ signifies that, if properly defined, the domain file could be
invariant and transferable to any other aircraft with equivalent dynamics. We
demonstrated, with this work, this aspect by exploiting the domain file encod-
ing the system behavior of a fixed-wing high-altitude unmanned aircraft for an
ultralight single-pilot fixed-wing aircraft. Hence, the task of implementing the
flight planner is considerably simplified, i.e. it is only necessary to encode the
platform-specific parameters in the instance file, e.g. cruise speed, yaw rate, pitch
angle, etc.
Further development of the planner will focus on the augmentation of fault-
tolerance capabilities, so that the planner will be able to identify abnormal con-
ditions and act accordingly, providing a flight path adapted to the unexpected
circumstances, such as extreme weather conditions, pilot’s incapacity or me-
chanical failure. For this purpose, unsupervised learning will be exploited to
classify the behavior of the platform in each adverse situation, divising hence
also situation-specific parameters for the platform to be included in the instance
files, e.g. airspeed limit during a motor failure, yaw rate in the case of a bro-
ken wind, etc. The application of such AI techniques will deliver more suitable
constraints and aircraft parameters to be defined in PDDL+ and consequently,
the calculated trajectories will adapt to each specific flight scenario and aircraft
requirements.
Particle Swarm Optimization (PSO) algorithm [9] will be implemented to
find optimal gain values. PSO varies the gain values iteratively and reduces
the Integral Time Absolute Error until the maximum number of iterations is
reached or the error is lower than a predefined value. This optimization algorithm
will enhance the robustness and reliability of the controller and therefore, the
discrepancies observed between the simulated flight and the calculated flight
plan will be substantially reduced.
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