wind efficient path planning and reconfiguration of uas in future...

“Wind Efficient Path Planning and Reconfiguration of UAS in Future ATM”

Twelfth USA/Europe Air Traffic Management Research and Development Seminar (ATM2017)

Wind Efficient Path Planning and Reconfiguration of UAS in future ATM

L. Rodriguez, F. Balampanis, J.A. Cobano, I. Maza and A. Ollero

Robotics, Vision and Control Group University of Seville (Spain)

Outline

Motivation Wind Estimation and Prediction Process

Flight Duration Enhancement Requirements for Atmospheric Energy Harvesting Wind Vector Estimation Wind Field Identification Energy Analysis

Flight plan generation and reconfiguration Complex Area Partitioning Considering Aerial

Restrictions Constrained Delauny Triangulation

Simulation and Test Cases Results Conclusions Future Work

Motivation

• Challenges on Integration of UAS in ATM/UTM (Autonomous Operations): – Communications Safety and Reliability – Detect and Avoid Techniques – Smart Flight Planning – Robust Path Planning – Flight duration enhancement – Flight plan reconfiguration capabilities

WIND ESTIMATION AND PREDICTION PROCESS

Flight duration enhancement

• Constrained by payload/fuel capabilities of the platform (specially for battery powered UAS)

• Different techniques have been studied to provide the system with energy from external sources (solar, wind)

• Atmospheric Energy Harvesting (AEH) has been considered as an alternative to increase the flight duration without extra payload.

• AEH shall be a cost effective alternative (wind sensors may increase the cost)

Requirements for AEH

• Estimation of actual wind before flight (wind reports/anemometers) for pre-planning and during flight (vector estimation)

• Use of accurate wind models for wind field characterization • Fast prediction algorithm to characterize the field in short time • HW architecture with the use of COTS components • SW compatible with open source architectures

(MAVLINK/ROS) to allow easy validation and verification. • Allow integration with other SW modules (flight plan/trajectory

generation and optimization/path following)

Wind Vector Estimation

• Wind vector estimations uses a Direct Computation (DC) method that uses GNSS information with air-relative speed:

�̇�𝑟 = 𝑉𝑉𝑎𝑎 + 𝑊𝑊

• Wind velocity, in the inertial frame 𝐼𝐼 is the sum of a steady component and a

stochastic process:

𝑊𝑊𝐼𝐼 = 𝑊𝑊𝑆𝑆𝐼𝐼 + 𝑊𝑊𝑇𝑇

𝐼𝐼

• The wind stochastic process is, at the same time, the sum of three phenomena, shear, discrete and continuous gusts.

𝑊𝑊𝑇𝑇

𝐼𝐼 = 𝑊𝑊shear +𝑊𝑊dgust +𝑊𝑊cgust

Wind models

𝑊𝑊shear = 𝑊𝑊20ln ℎ𝑧𝑧0

ln20𝑧𝑧0

𝑊𝑊dgust = �𝑤𝑤𝑑𝑑𝑑𝑑

2 1 − cos𝜋𝜋𝜋𝜋𝐻𝐻

0 < 𝜋𝜋 < 2𝐻𝐻

0 𝜋𝜋 > 2𝐻𝐻

And the continuous gust is an approximation of the Von Kármán turbulence power density spectrum given by the following Dryden transfer functions:

𝐻𝐻𝑢𝑢 𝜋𝜋 = 𝜎𝜎𝑢𝑢2𝑉𝑉𝑎𝑎𝐿𝐿𝑢𝑢

𝜋𝜋 + 𝑣𝑣𝑎𝑎𝐿𝐿𝑢𝑢

𝐻𝐻𝑣𝑣 𝜋𝜋 = 𝜎𝜎𝑣𝑣3𝑉𝑉𝑎𝑎𝐿𝐿𝑣𝑣

𝜋𝜋 + 𝑣𝑣𝑎𝑎3𝐿𝐿𝑣𝑣

𝜋𝜋 + 𝑣𝑣𝑎𝑎𝐿𝐿𝑣𝑣

2 𝐻𝐻𝑤𝑤 𝜋𝜋 = 𝜎𝜎𝑤𝑤3𝑉𝑉𝑎𝑎𝐿𝐿𝑤𝑤

𝜋𝜋 + 𝑣𝑣𝑎𝑎3𝐿𝐿𝑤𝑤

𝜋𝜋 + 𝑣𝑣𝑎𝑎𝐿𝐿𝑤𝑤

Wind Shear Wind 1-Cos Gust Model

Wind shear characterization

• The Wind Identification System (WIS) performs a statistical analysis of accumulated wind estimates together with off-boar information such as weather reports, wind database or ground sensors.

• Wind measurements are typically distributed following a Weibull distribution. The Weibull Probability Density Function is indexed for two factors: shape (k), and scale (v) :

𝑓𝑓 𝑊𝑊 =𝜅𝜅𝜈𝜈

𝑊𝑊𝜐𝜐

𝜅𝜅−1

𝑒𝑒𝑊𝑊𝜐𝜐 𝜅𝜅

• The most probable wind speed at a certain location can be expressed as a

function of the Weibull parameters

𝑊𝑊 𝑟𝑟 = υ 1 −1𝜅𝜅

1𝜅𝜅

• Use a Genetic Algorithm (GA) in order to estimate the Weibull parameters

Discrete and Continuous Gust Characterization

• The analytic expression for probability density distribution of a gust velocity is:

𝑓𝑓 𝜎𝜎𝜔𝜔 =2𝜋𝜋

1𝑏𝑏 𝑒𝑒

−12𝜎𝜎𝜔𝜔𝑏𝑏

where σw is the standard deviation of the wind vector and b is the Root Mean Squared turbulence which depends on the altitude and the type of turbulence. • For continuous gust a standard Gaussian Process (GP) regression can be

incorporated in order to perform a short-term prediction to determine a covariance vector q 𝑥𝑥, X and a linear prediction �̅�𝑝(𝑋𝑋)

�̅�𝑝 𝑋𝑋 = q 𝑥𝑥, X Q(X,X) + 𝜎𝜎𝑛𝑛2I −𝟏𝟏W�𝑧𝑧

Wind Identification Algorithm

Obtain Wind Estimates

Store Estimates in DB

Get Weibull Parameters

Find M.P. Wind Speed

Calculate Prandtl Ratio

GRP PDF

Energy Analysis

• Energy provides an assessment on how the aircraft performs while executing

• Starting from the total energy of the UAS

𝐸𝐸𝑎𝑎 = −𝑚𝑚𝑚𝑚𝑚𝑚 +12𝑚𝑚𝑉𝑉𝑎𝑎2

• The energy from a wind field can be expressed in terms

of the Weibull parameters:

𝐸𝐸𝑊𝑊 ≈12𝜌𝜌𝜈𝜈3Γ 1 +

3𝜅𝜅

Energy Analysis • Energy can be expressed as a function of the trajectory

𝐸𝐸� 𝑞𝑞 𝑡𝑡 = � 𝑐𝑐1𝑣𝑣(𝑡𝑡)3 +𝑐𝑐2𝑣𝑣(𝑡𝑡)

1 +𝑎𝑎(𝑡𝑡)2 − 𝐴𝐴𝑣𝑣(𝑡𝑡) 2

𝑣𝑣(𝑇𝑇)2𝑚𝑚2

𝑑𝑑𝑡𝑡𝑇𝑇

12𝑚𝑚(𝑣𝑣 𝑇𝑇 2 − 𝑣𝑣 0 2

where v(t) is the first derivative of the trajectory function and a(t) is the second derivative of the trajectory function. • Thus how much energy is actually gained from the full energy on the wind

field can be evaluated.

FLIGHT PLAN GENERATION AND RECONFIGURATION

Complex Area Partitioning Considering Aerial Restrictions

• Missions are often handled by the means of a grid decomposition of areas in order to accomplish complete coverage

• Coastal area tasks with their numerous no fly zones or complex shores, impose a dynamical approach

• Constrained Delaunay Triangulation (CDT)

Constrained Delauny Triangulation

• Introduces forced edge constrains as part of the input and in such a way, complex areas can be triangulated, creating a triangular mesh

• Each centroid of every triangle can be considered as a waypoint in the flight plan

• Every triangle has a cost, based on several task, area or agent related criteria. In our case, this cost is related to the wind information

• Consider 2D scenarios with restricted areas and several UAS to cover them.

• CDT is not computationally expensive and allow the online reconfiguration or re-planning.

Wind Efficient Waypoint Sequencing Process

Test Cases

• A test case in the Seattle Region with imposed aircraft restrictions is considered.

• Two UAS with the same FoV will cover the zone. • Each UAS has a different autonomy coverage capabilities

Test Cases

• UAS1 (80%) – UAS2 (20%) • Sequence of waypoints generated for each UAS. • Sequence criterion is from the outer to inner.

Test Cases

• Simulation of flight of the UAS (APM Planner Ground Station)

Test Cases

• The Aerosonde UAV was selected as the simulation platform

• A HITL setup was utilized with the COTS Pixhawk Autopilot and the use of the MAVLINK communication protocol system

• Matlab/Simulink was utilized to perform the wind estimation and identification in real time

• A 6DOF model was selected • SITL experiments were performed

with the WEWS algorithm • Three cases are considered with

different winds

• A standard gasoline engine • 55 CC • Power of 5.6 HP

Simulations

• Flight plan from the decomposition considering no wind

Simulations

• Online replanning of the FP considering sustained east wind

Speed: 5m/s Direction: -90º

Results

• Wind estimation results for sustained wind

Results

• Distribution of estimates following the Weibull distribution

Simulations

• FP from the decomposition considering a discrete gusts.

Results

• Wind estimation results for a discrete gust identified

Discrete gust takes place (3000 sec) +2m/s

Results

• Distribution of estimates that follow the Weibull Shape (discrete gust)

Results

Parameter With no wind reconfiguratio

With Sustained Wind

With Discrete Gust

Flight Duration 3,20h 3.16h 3.19h

Medium Altitude 100 m ASL 100 m ASL 100 m ASL

Total Energy Consumption

22385.66 W/h 19913.408 W/h 20421.783 W/h

Average Wind Speed 0 m/s 5 m/s 6,24 m/s

Average Airspeed 20,93 m/s 16,74 m/s 15,23 m/s

Conclusions

• Area decomposition ensures the maximum coverage of a given area due to the triangular cells.

• The cell weighting method allows the generation online reconfiguration of the intended waypoint sequence considering potential airspace restrictions in the context of current and next generation airspace.

• Results shows improvements up to 11% of efficiency with low winds and up to 9% in the presence of gust and shear with online sequence reconfiguration.

• Wind identification and smart area decomposition into UAS flight management functions permit a more efficient use of airspace even in adverse meteorological conditions.

Future Work

• Incorporation of the described system to a trajectory generation system and a trajectory tracking system.

• Continue with energy analysis to optimize on a waypoint to waypoint basis the trajectory to allow maximizing the energy gain.

• Analyze the energy transfer mechanism in different wind scenarios.

• Full test campaign with experiments is being prepared in order to validate and verify the different functions for safe-long duration missions.

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