This presentation shows an online multi-query path planner for exploration tasks planned onboard an unmanned helicopter. While the desirable properties of roadmaps can be exploited in offline path planning, the dynamic nature of exploration scenarios hinders to utilize conventional roadmap planners. Hence, the presented path planning approach utilizes a deterministically sampled roadmap which is dynamically indexed in real time. To address situations of partial terrain knowledge, the roadmap can be extended from its a priori dimensions towards locations of unknown terrain that are outside its original, a priori boundaries. The multi-query property of the planning system allows for combinatorial optimization such that a rapidly acting decisional autonomy is achievable during exploration flights. D*-Lite is used as dynamic heuristic path searcher in order to re-plan efficiently. Inspired by the original work on this path search algorithm, the roadmap graph is augmented with an exploration vertex which steers the exploration behavior of the vehicle. As a result, the presented roadmap guides an unmanned rotorcraft through a priori unknown urban terrain in real time.

1. Florian-Michael Adolf & Simon Schopferer
DLR Institute of Flight Systems
Dept. Unmanned Aircraft
Braunschweig
RSS Workshop on „Resource-Efficient Integration of Perception, Control and Navigation for MAVs “, 28th June 2013
Online Roadmaps for Task-based
Navigation in Urban Terrain

2. Background
Support Acquisition of Situational Awareness in Hazardous Environments
Tepco Fukushima Daiichi Reactor, Japan 2011
[Air Photo Service + Rotomotion/Hélipse]
Earthquake, Chile 2010
Texas City disaster April 16, 1947:
Complex docks building.
[Special Collections, University of Houston Libraries]
www.DLR.de • Chart 2 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

3. Autonomous Rotorcraft Testbed for Intelligent Systems (ARTIS)
Unmanned rotorcraft midiARTIS (MTOW 14 kg)
shown with stereo-based obstacle detection.
www.DLR.de • Chart 3 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

4. Obstacle Detection and Mapping
[Andert et al., 2009]
www.DLR.de • Chart 4 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

5. Problem Description
State of terrain
a priori unknown
UAV with terrain
mapping sensor
Autonomous Terrain Exploration
www.DLR.de • Chart 5
1. Navigate from „A to B“ safely
2. Task-based Navigation: Determine „B“ online
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

6. 3D Path Planner Task PlannerTask Planner
Generate waypoints for tasks
(e.g. search areas)
Optimize the order in which
waypoints are visited
Assign paths to each vehicle
Find collision free connections
between each pair of waypoints
Smooth effective path if possible
Highly coupled problem domains:
Task Planning Path Planning
“…and me,
the task
ordering”
“I need the
costs…”
www.DLR.de • Chart 6
Automated Task-to-Path Decomposition
Mission Planning Problem
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

7. System Overview
Online Mapping and Multi-Query Path Planning
UAV with terrain
mapping sensor
“Raw” obstacle data
(e.g. point cloud, depth image)
Online Mapping
[Andert et al., 2009 / Krause 2010]
Geo-referenced
polygon obstacles
Online Path RePlanning
[F.Adolf et al., 2010]
Path Following + Flight Control
[S.Lorenz et al., 2010]
Path updates
Sensor
FOV
www.DLR.de • Chart 7 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin
See also PRM for Rotorcraft [Petterson et.at. 2006],
Online feeds with stereo-camera [Hrabar et.al. 2008]
Local planner extension by [Scherer et.al. 2011]

8. „Classical“ Pseudo random sample distribution (PRM) Lattice grid sample distribution (LRM):
non-orthogona + non-uniform
Quasi-random sample distribution (QRM):
steered randomness using Halton sequences
Roadmap-based Path Planner
www.DLR.de • Chart 8
Efficient & Persistent Free Space Representation
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

9. www.DLR.de • Chart 9
Online Roadmap
Accelerated Graph Updates using Spatial Indices
Query Obstacles
AABB w/ Safety Distance
Effective Object of Interest
Coarse Voxels
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

10. DLR’s test site “Rosenkrug”: Flight test obstacle data fed into the roadmap
-Test site “Rosenkrug”-Test site “Rosenkrug”
www.DLR.de • Chart 10
Online Roadmap
Accelerated Graph Updates using Spatial Indices
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

11. UAV
www.DLR.de • Chart 11
Roadmap Connection Strategy
Treatment of UAV Vertex in Graph
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

12. Quasi-random
Roadmap with
30 m sample
distance
Roadmap Connection Strategy
Treatment of UAV Vertex in Graph
www.DLR.de • Chart 12
UAV Sensor FOV
Roadmap Sample
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

13. Simulation Setup
Closed Loop Flights in ‘Unknown’ Terrain
3-D LIDAR Model
50 m detection range
180 degree
scan plane 360 degree rotation @1Hz
of 2-D scan plane
Vehicle state update
ARTIS Closed Loop Simulation
Laser beam
collision detection
A Priori ‘Unknown’ Polygons
Extracted polygons
Path-based velocity
command
(VK, gamma, chi)
Roadmap-Based Planner
www.DLR.de • Chart 13 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

14. Accumulated terrain over six test cases of benchmark scenarios in San Diego.
www.DLR.de • Chart 14
Online Navigation
Example in Unknown Urban Terrain
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

15. 0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
8 m 10 m 12.5 m 15 m 17.5 m 20 m
total (avg)
replan (avg)
max
min
CPU Time over Planner Resolution
40%
15%
www.DLR.de • Chart 15
CPU Time on i7-based PC for different sample distances.
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

16. CPU Time Statistics
CPU Time on i7-based PC for different sample distances.
www.DLR.de • Chart 16
With initial planning
Edge and cost updatesPolygon world
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

17. B
A Initial path
Online
Polygon
Updates
Non-traversable
roadmap edges
B
A
Replanned path
*) Results presented at AHS-Forum 68, 2012
Problem:
Linear free-space representation
is not an ideal path geometry for
fast(er) navigation*
www.DLR.de • Chart 17
Online Roadmap Navigation
Path Smoothing Desired
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

18. 1) Revise connection
strategy:
Case dependent steering
of vertex in front of the
rotorcraft
2) Generate collision free
and smooth geometry
within field of view
3) Consider sensor FOV and
hover capability: Special
cases for multiple goal
waypoints
UAV
dstop
Linear extrapolated q‘
UAV
dstop
Heuristic
extrapolation(s)
B
A
B
A
B
A
www.DLR.de • Chart 18
Smoothing + Roadmap Connection
Locally Bounded Feasible Planning
Sensor FOV
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

19. Large deviation from planned path due to
vehicle‘s dynamic limits
Risk of safety distance violation
Local region for feasible planning:
Scales with maximum acceleration/turn rate and velocity
Allows smooth connection to linear path
www.DLR.de • Chart 19
Smoothing + Roadmap Connection
Locally Bounded Feasible Planning
[S.Schopferer, 2013]
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

20. Accumulated terrain over six test cases of benchmark scenarios in San Diego.
www.DLR.de • Chart 20
Online Navigation
Comparing Smoothed Trajectories + CPU Overhead
Start position “A3”
Goal region “A”
Urban
Scenarios
Relative Difference of
FHCS to Linear Mode
[%]
A1 5.2%
A2 5.8%
A3 6.7%
A4 3.3%
A5 4.1%
A6 3%
Mean +4.7%+4.7%
With collision
detection less than
0.1% of CPU time
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

21. Example Scenario
Urban Terrain from “Berlin Potsdamer Platz”
www.DLR.de • Chart 21
“Finally we fly smoothly
from A to B…”
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

22. Roadmap-Based Decision Making
Roadmap perimeter defines
volume to be mapped
A
Greedy Mapping: Select „Best Next“ Waypoint „B“
Uniform
edge costs
A
Bmap
Mapping
vertex
1 2
A2
Bmap
„Mapped“
vertices
A1
A0
Current „A to B“ path,
no path segment to Bmap
www.DLR.de • Chart 22
“Now fly something
more useful…”
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

23. A
Exploration Scenario
Urban Terrain “Berlin Potsdamer Platz”
www.DLR.de • Chart 23 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

24. Simulation Results
Rotating
LIDAR
sensor
UAV
Initial roadmap
perimeter
Exploration of Urban Terrain
www.DLR.de • Chart 24 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

25. Simulation Result
Exploration of Urban TerrainExploration of Urban Terrain
Remaining narrow corridor
(width < 20 m)
UAV
Rotating
LIDAR
Flown path
Current mapping path
www.DLR.de • Chart 25 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

26. Simulation Result
Exploration of Urban Terrain
Efficient
replanning
Terrain almost
fully mapped
Total mission time within max. flight time of ARTIS
Trajectories
always well clear
of obstacles
www.DLR.de • Chart 26 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

27. Summary
1. Online Roadmap as Persistent Path
Database (“A to B”)
2. Online Task (Re-)Planning using the
Roadmap (“generate B”):
a) Greedy mapping as example application
b) (Re-)Planning benefits from multi-query
property
www.DLR.de • Chart 27 > Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin

28. Questions?
Ideas?
…
Thank You!
Paper for approach see http://elib.dlr.de/76395/
or directly via AIAA http://arc.aiaa.org/doi/pdf/10.2514/6.2012-2452
“Multi-Query Path Planning for Exploration Tasks with an Unmanned Rotorcraft”
www.DLR.de • Chart 28
Florian.Adolf@dlr.de
> Task-based Nav with Roadmaps > Florian-M. Adolf • MiPlEx > 28th June 2013, Berlin