Assessing the airspace availability for sUAS operations in urban environments: A topological approach

using keep-in and keep-out geofence

Jungwoo Cho, Yoonjin Yoon

Korea Advanced Institute of Science and Technology

Note: This work has recently been published in Cho, J., & Yoon, Y. (2018). How to assess the capacity of urban airspace: A topological approach using keep-in and keep-out geofence. Transportation Research Part C: Emerging Technologies, 92, 137-149.

Any questions, comments, and feedbacks are more than welcome. Please contact us via jjw9171@kaist.ac.kr

ICRAT 2018 Doctoral Symposium

Contents

Introduction• Background

• Related literatures

• Research objective

Methodology• Terminology and geofence specifications

• Alpha shape method

• 3-D airspace availability assessment framework

Result• Individual effect of keep-out and keep-in geofence

• Dual effect of keep-out and keep-in geofence

Conclusion

Further studies

2

BackgroundIntroduction

3

1. FAA. FAA Aerospace Forecast Fiscal Years 2018-2038. 2018. 2. SESAR. Opening Remarks, SESAR U-space Workshop, Apr. 2017. 3. SESAR. European drones outlook study. Unlocking the value for Europe. 2016.

Expected increase in the number of drones in activity (US)1

4

(Europe)2,3

Estimated impact measured in flight hours and distance2,3

2017 2022

Recreational 1.1M 1.9M - 3.2M

Commercial 0.11M 0.45M - 0.72M

2017 2025 2035

Recreational 1-1.5M 7.0M 7.0M

Commercial 0.01M 0.2M 0.4M

BackgroundIntroduction

44

NASA UTM SESAR U-space

S’pore uTM-UAS K-UTM / J-UTM

1. Kopardekar et al. (2016). Unmanned aircraft system traffic management (UTM) concept of operations. 16th AIAA Aviation Technology. In Integration, and Operations Conference

2. SESAR (2017). U-space Blueprint.3. Civil Aviation Authority of Singapore (2017). Call-for-proposal to develop solutions for innovative

UAS operations in Singapore.

4. Salleh, M. F. B., & Low, K. H. (2017). Concept of Operations (ConOps) for Traffic Management of Unmanned Aircraft Systems (TM-UAS) in Urban Environment. AIAA Information Systems-AIAA Infotech@ Aerospace.

5. Yoon, Y. (2018). Introduction to K-UTM. In Global UTM Conference, March 2018.6. Ushijima, H. (2017). UTM Project in Japan. In Global UTM Conference, 26 June 2017.

BackgroundIntroduction

54

NASA UTM

• Airspace operation and geofencing• Route planning and re-routing• Separation management • Strategic de-confliction• Contingency management

SESAR U-space

• Airspace management• Identification and tracking• Mission management• Conflict management• Emergency management• Flight monitoring

S’pore uTM-UAS3,4

• Airspace design and geofencing• Fleet management • Navigation solutions• Sense-and-avoid technology• Failure management

K-UTM / J-UTM

• Airspace management and geofencing

• Risk-based traffic flow management

• Sense and avoid technology

1. Kopardekar et al. (2016). Unmanned aircraft system traffic management (UTM) concept of operations. 16th AIAA Aviation Technology. In Integration, and Operations Conference

2. SESAR (2017). U-space Blueprint.3. Civil Aviation Authority of Singapore (2017). Call-for-proposal to develop solutions for innovative

UAS operations in Singapore.

4. Salleh, M. F. B., & Low, K. H. (2017). Concept of Operations (ConOps) for Traffic Management of Unmanned Aircraft Systems (TM-UAS) in Urban Environment. AIAA Information Systems-AIAA Infotech@ Aerospace.

5. Yoon, Y. (2018). Introduction to K-UTM. In Global UTM Conference, March 2018.6. Ushijima, H. (2017). UTM Project in Japan. In Global UTM Conference, 26 June 2017.

Background

6

Introduction

at 200 ft AGL

Copyright ⓒ2018, TRUE KAIST

Urban airspace is very likely to be intruded by existing structures

It is important to Identify not just free but usable/flyable airspace

0 ft

200 ft

600 ft

400 ft

Related literatures

Static waypoints are selected based on various geospatial elements (building rooftop, road, canals, metro, etc) as well as its stringent altitude limit (60m AMSL)

< Metropolis project > < uTM-UAS project >

Four airspace designs were proposed and evaluated in a virtual urban environment

Capacity, safety, and efficiency measures

Minimum cruising altitude of 300 ftAGL and 100 ft above the tallest building

7

Introduction

(Schneider et al., 2014; Sunil et al., 2015; Hoekstra et al., 2016; Sunil et. al., 2016a; Sunil et al., 2016b)

(Low, Gan & Mao, 2016; Salleh and Low, 2017; Salleh et al., 2018).

Related Literatures

Currently, regulatory bodies have imposed sUAV operational restrictions based on proximity to population and man-made structures. • Minimum keep-out distance ranging from 30 to 150 meters

State Flight purpose Weight category

Minimum

distance from

people

Minimum distance

from a building or

structure

Altitude

limitReference

AustraliaRecreational 100g up to 25kg 30m 30m

120m AGLAC 101-3,

AC 101-10Commercial 100g up to 25kg 30m unspecified

Canada

Recreational250g up to 1kg 30m unspecified

90m AGL

Interim Order

No. 81kg up to 35kg 75m unspecified

Commercial250g up to 1kg 30m 30m

AC 600-0041kg up to 25kg 150m 150m

Hong Kong

Recreational

7kg or less

unspecified unspecified

90m AGL

Guidelines on

Operations of

Unmanned Aircraft

SystemsCommercial 30m 30m

JapanRecreational

/commercial200 g or more 30m 30m 150m AGL

Act to Amend the

Aviation Act No. 67

SingaporeRecreational

/research7kg or less unspecified unspecified 60m AMSL AC UAS-1

South KoreaRecreational

/commercial25kg or less unspecified unspecified 150m AGL

Enforcement Rule of

the Aviation Act

United KingdomRecreational

/commercial20kg or less 30m 50m 120m AGL

Air Navigation Order

2016

United StatesRecreational

/commercial25kg or less unspecified unspecified 120m AGL 14 CFR Part 107

6

State-wise operational requirement on the minimum distance from people and buildings

Introduction

Related Literatures

9

Introduction

Keep-out geofence

used to define a protection boundary that a vehicle should not intrude

mainly focuses on protecting the static ground structures

Keep-in geofence

a similar concept with the Containment Limit (CL) of jet aircraft, which is defined based on the Total System Error (TSE)

concerns the vehicle’s operational feasibility

[Atkins, 2014; Hayhurst et al., 2015; Dill et al., 2016]

adopted from (DLR, 2017)

[D’Souza et al., 2016; C. Johnson et al., 2017; M. Johnson et al., 2017]

Research Objective

10

Introduction

We apply two types of the geofencing method to identify usableairspace, as a first step to assess the urban airspace capacity

The main objective is to analyze urban airspace by incorporating vehicle operational requirements (keep-in) as well as providing an adequate level of protection for surrounding environments (keep-out)

Terminology and geofence specifications

11

Methodology

Keep-out geofence is modeled as a buffer space around the objects, whereas keep-in geofence is used to contain a vehicle in a spherical ball and modeled using alpha-shape method

Free airspace: airspace that is free of static obstacle

Usable airspace: airspace that is not only free of static obstacles but also not affected by geofencing

Alpha shape method

Alpha shape method is a computational geometric technique used to construct a shape of a finite point set using a spherical ball or 𝜶−𝒃𝒂𝒍𝒍

𝛼 defines the radius of the ball, hence the level of details of the shape

Methodology

< Alpha shape of point set with 𝛼-ball >adopted from Sun et al., (2016)

12

• Given a set of points S, suppose that u is 𝛼-ball of radius 𝑟, where u is called empty if 𝑢 ∩ 𝑆 = 𝜙.

• For any subset T of S with cardinality p+1 (p=0,1,2,3), p-simplex is geometrically interpreted as the convex hull of Tdenoted as 𝜎𝑇.

• 𝜎𝑇 is called 𝛼-exposed if there exists an empty open ball u satisfying 𝜕𝑢 ∩ 𝑆 = 𝑇, where 𝜕𝑢 is the surface of u.

• The set of 𝛼-exposed p-simplices is referred as the alpha shape given filtration radius 𝑟.

Alpha shape method

Alpha shape method is a computational geometric technique used to construct a shape of a finite point set using a spherical ball or 𝛼−ball

𝛂 defines the radius of the ball, hence the level of details of the shape

Methodology

13

• Given a set of points S, suppose that u is 𝛼-ball of radius 𝑟, where u is called empty if 𝑢 ∩ 𝑆 = 𝜙.

• For any subset T of S with cardinality p+1 (p=0,1,2,3), p-simplex is geometrically interpreted as the convex hull of Tdenoted as 𝜎𝑇.

• 𝜎𝑇 is called 𝛼-exposed if there exists an empty open ball u satisfying 𝜕𝑢 ∩ 𝑆 = 𝑇, where 𝜕𝑢 is the surface of u.

• The set of 𝛼-exposed p-simplices is referred as the alpha shape given filtration radius 𝑟. < Implementation of alpha shape with increasing

radius in 2-D >

Hypothetical caseMethodology

14

Hypothetical caseMethodology

15

Hypothetical caseMethodology

16

Hypothetical caseMethodology

17

3-D airspace availability assessment framework

18

Methodology

Define𝛤be the discretized 3-D lattice of the region of interest with a unit cube of size 𝜖.

𝜞 = {𝑔𝑙𝑚𝑛: 1 ≤ 𝑙 ≤ 𝑁𝑥, 1 ≤ 𝑚 ≤ 𝑁𝑦, 1 ≤ 𝑛 ≤ 𝑁𝑧}

Define three subsets of 𝛤 as follows:

𝜞𝒐 ={𝑔𝑙𝑚𝑛 ∈ 𝛤: 𝑔𝑙𝑚𝑛 is occupied by static obstacles}

𝜞𝒐𝒖𝒕𝜹 = {𝑔𝑙𝑚𝑛 ∈ 𝛤:𝑔𝑙𝑚𝑛 is closed by keep-out geofence of size 𝜹}

𝜞𝒊𝒏𝒓 ={𝑔𝑙𝑚𝑛 ∈ 𝛤:𝑔𝑙𝑚𝑛 is closed by alpha shapes of radius 𝒓}

When a grid is blocked by static obstacles, we call it occupied. Likewise, when a grid becomes

unavailable due to geofence, we call it closed.

The availability of cell 𝑔𝑙𝑚𝑛 defined as an indicator function

𝑐𝑒𝑙𝑙 𝑔𝑙𝑚𝑛; 𝛿, 𝑟 = ൝0, 𝑔𝑙𝑚𝑛 ∈ 𝛤𝑜 ∪𝛤𝑜𝑢𝑡

𝛿 ∪𝛤𝑖𝑛𝑟

1, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

The usability of airspace at altitude 𝒌 is defined as

𝑼 𝒌;𝜹,𝒓 ≔σ1≤𝑙≤𝑁𝑥,1≤𝑚≤𝑁𝑦

𝑐𝑒𝑙𝑙(𝑔𝑙𝑚𝑛;𝛿,𝑟)

𝑁𝑥×𝑁𝑦

Results and discussions

19

Results & discussion

Study area:

built-up area of size 3 km by

3 km in Gangnam, Seoul

Cell in black: static obstacles

Cell in grey: airspace closed

by keep-out

Cell in red: airspace closed

by alpha shape

Altitude, k (0-150m):

60 m AMSL

Keep-out distance, 𝜹 :

5 to 50 meters

Keep-in radius, r:

5 to 50 meters

* Built-up area is defined as any group of structures such as houses, factories, service stations, grain elevators, apartment buildings, or other man-made structures that could interfere with UAV operations (Transport Canada, 2017b)

Results and discussions

20

• The topmost solid curves are the raw availability with no geofencing, or 𝑈 𝑘; 0,0

• Overall, the effect of geofencing was most restrictive in the lower altitude, roughly when 𝑘≤40

• 𝛅 of 10 or 20 meters closed the majority of free space when 𝐤≤40, indicating that the area is populated with static obstacles and that those are located in close proximity

• In the keep-in scenario, the lower airspace was less prone to the effect of geofencing

• Such a difference comes from the unique capability of alpha shape method to identify corridors that can safely accommodate flights with a certain keep-in requirement.

• 𝑈 40; 20,0 = 6.8% and 𝑈 40; 0,20 =20.7%

• 𝑈 70; 20,0 = 45.2% and 𝑈 70; 0,20 =68.3%

• The keep-out scenario generated open spaces that are spread out in silos, while the keep-in scenario preserved several corridors connecting the open segments

Keep-out only

Keep-in only

1

keep-out only keep-in only

1

< Airspace usability curves of keep-out only and keep-in only scenarios >

< 2D snapshots of individual effect of keep-out and keep-in geofence of size 20 at altitude 40 m>

𝑈 𝑘; 𝛿, 𝑟 : Airspace usability𝑘 : altitude (m)𝛿: keep-out parameter (m)𝑟: keep-in parameter (m)

keep-out only keep-in only

1

Results and discussions

21

Keep-out only

Keep-in only

1

< 2D snapshots of individual effect of keep-out and keep-in geofence of size 20 at altitude 70 m>

< Airspace usability curves of keep-out only and keep-in only scenarios >

𝑈 𝑘; 𝛿, 𝑟 : Airspace usability𝑘 : altitude (m)𝛿: keep-out parameter (m)𝑟: keep-in parameter (m)

• The topmost solid curves are the raw availability with no geofencing, or 𝑈 𝑘; 0,0

• Overall, the effect of geofencing was most restrictive in the lower altitude, roughly when 𝑘≤40

• 𝛅 of 10 or 20 meters closed the majority of free space when 𝐤≤40, indicating that the area is populated with static obstacles and that those are located in close proximity

• In the keep-in scenario, the lower airspace was less prone to the effect of geofencing

• Such a difference comes from the unique capability of alpha shape method to identify corridors that can safely accommodate flights with a certain keep-in requirement.

• 𝑈 40; 20,0 = 6.8% and 𝑈 40; 0,20 =20.7%

• 𝑈 70; 20,0 = 45.2% and 𝑈 70; 0,20 =68.3%

• The keep-out scenario generated open spaces that are spread out in silos, while the keep-in scenario preserved several corridors connecting the open segments

1

Dual effect of keep-out and keep-in geofence

22

Results & discussion

< 2D snapshots of the dual effect of keep-out and keep-in geofence of size 10 at altitude 70 m>

(a) 40≤k≤120

(b) k=40

(c) k=70

(d) k=100

1

𝑈 𝑘; 𝛿, 𝑟 : Airspace usability𝑘 : altitude (m)𝛿: keep-out parameter (m)𝑟: keep-in parameter (m)

Dual effect of keep-out and keep-in geofence

23

Results & discussion

< 3-D contour plot of airspace usability from the dual application of keep-out and keep-in geofence >

(a) 40≤k≤120

(b) k=40

(c) k=70

(d) k=100

1

• k=40 yields 𝑼(𝒌; 𝜹, 𝒓) of 40% or less in most parameter combinations, whereas k=70 and 100 result in 𝑈(𝑘; 𝛿, 𝑟) larger than 30% and 70%, respectively

• Even though 𝑈 70; 15, 8 =𝑈 70; 5,33 = 50%, reducing 𝛿from 15 to 5 meters increases the amount of usable airspace by 17% given 𝑟=8. On the other hand, increasing 𝑟 from 8 to 33 meters reduces the amount of usable airspace by 8% given 𝛿=15.

• Although larger 𝛿 might seem like a simple solution for enhanced safety, our results show that it comes at a cost of greatly reducing a usable airspace that can safely accommodate vehicles of various containment limits

Conclusions

24

Conclusions

Airspace assessment framework effectively identifies usable airspace even in a highly built-up environment

Our approach captures the inherent benefits of the individual geofencing method as well as their tradeoffs

It also has the potentials to identify departure/arrival locations and design ascent/descent routes

Framework is applicable to any geospatial dataset given various geofence parameters

Further studies

25

Further studies

The proposed framework provides a crucial dataset to study geospatial continuity in 3-D.

Identifying the continuity of open segments will be necessary for structured urban airspace design and path planning.

We apply various topological analysis techniques, including topological data analysis (Carlsson, 2009) to model such continuity of usable airspace.

1

adopting Mapper algorithm

2D representation

3D representation

Usable airspace

Topological clustering

26

Further studies

Mapper algorithm

1. Choose distance metric: Euclidean

2. Filter function to map data to a single value: horizontal and vertical projection

3. The ranges of each filter application are divided into overlapping segments or intervals using two parameters cover and overlap. Cover (resolution) controls how many intervals each filter range will be divided into

4. Compute the Cartesian product of the rangeintervals and assign the original data points to theresulting regions based on the filter values.

5. Perform clustering in the original space for each region. Each cluster represents a node. The nodes are connected by an unweighted edge if any clusters have points in common.

6. By joining clusters in feature space, a topological network is derived in the form of a graph.

Nerve of a covering

Topological equivalence

Mapper algorithm

Sample illustration

27

Further studies

Sample illustration

28

Further studies

Network representation of airspace

29

Further studies

Airspace can be turned into discrete shape combination

Simplified representation of airspace can be derived in a network form

Segmentation of usable airspace, A(k=60, 𝜹=10, 𝒓=5)

Network representation of airspace

𝐴 𝑘; 𝛿, 𝑟 : Usable airspace at altitude k given geofence parameters 𝑘 : altitude (m); 𝛿: keep-out parameter (m); 𝑟: keep-in parameter (m)

Network representation of airspace

30

Further studies

Filtrations at multiple resolution settings

Clusters may change with scale, but connectivity is preserved

𝐴 𝑘; 𝛿, 𝑟 : Usable airspace at altitude k given geofence parameters 𝑘 : altitude (m); 𝛿: keep-out parameter (m); 𝑟: keep-in parameter (m)

𝑨 𝒌; 𝜹, 𝒓 = (𝟔𝟎, 𝟏𝟎, 𝟏𝟎) 𝑨 𝒌; 𝜹, 𝒓 = (𝟔𝟎, 𝟏𝟎, 𝟏𝟎)

Network representation of airspace

31

Network shape also changes with geofence parameters (keep-out and keep-in)

𝐴 𝑘; 𝛿, 𝑟 : Usable airspace at altitude k given geofence parameters 𝑘 : altitude (m); 𝛿: keep-out parameter (m); 𝑟: keep-in parameter (m)

𝑨 𝒌; 𝜹, 𝒓 = (𝟕𝟎, 𝟏𝟎, 𝟏𝟎) 𝑨 𝒌; 𝜹, 𝒓 = (𝟕𝟎, 𝟑𝟎, 𝟏𝟎)

Further studies

Betweenness Centrality

32

Further studies

2.5D/3D view

33

Further studies

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Acknowledgement

36

This research was supported in part by Ministry of Land, Infrastructure and

Transport of Korean government under Grant 18USTR-B127901-01 and by BK21

Plus project of the National Research Foundation of Korea Grant.