Eigenständige Erkundung komplexer Umgebungen mit einem Hubschrauber UAV und dem Sampling basierten Missionsplaner MiPlEx

Transcription

1 Eigenständige Erkundung komplexer Umgebungen mit einem Hubschrauber UAV und dem Sampling basierten Missionsplaner MiPlEx Florian-Michael Adolf DLR Institut für Flugsystemtechnik Abt. Unbemannte Luftfahrtzeuge Braunschweig DGLR Workshop Q3.3 UAV Autonomie Automa<sierung Unbemannter Lu?fahrtzeuge, 19.März 2013

2 Folie 2 Background Support Acquisition of Situational Awareness in Hazardous Environments Earthquake, Chile 2010 Texas City disaster April 16, 1947: Complex docks building. [Special Collections, University of Houston Libraries] Tepco Fukushima Daiichi Reactor, Japan 2011 [Air Photo Service + Rotomotion/Hélipse]

3 Folie 3 Problem Description Autonomous Terrain Exploration State of terrain a priori unknown Remote control link may be disturbed when flying out-of-sight Intermediate paths depend on acquired terrain data 3-D structures with overhangs might exist! Repetitive path changes UAV with terrain mapping sensor

4 Folie 4 Autonomous Rotorcraft Testbeds for Intelligent Systems (ARTIS) Magnetometer Camera Sonar Computer Vision Power Supply Flight Control Telemetry GPS IMU

5 Folie 5 Terrain Mapping Remotely Piloted Aircraft System (RPAS) Structure of interest Geo-referenced point cloud Area of interest [Stefan Krause, 2010]

6 Folie 6 Obstacle Detection and Mapping [Andert et al., 2009]

7 Folie 7 Approach Online Mapping and Multi-Query Path Planning Sensor FOV UAV with terrain mapping sensor Online Mapping [Andert et al., 2009 / Krause 2010] Raw obstacle data (e.g. point cloud, depth image) Geo-referenced polygon obstacles + Replans efficiently on the way from A to B + Efficient multiple path queries 1. Roadmap expansion into unknown terrain 2. Decision making: Best next waypoint B Online Path Replanning [F.Adolf et al., 2010] Path updates Path Following + Flight Control [S.Lorenz et al., 2010]

8 Folie 8 Simulation Setup Closed Loop Flights in Unknown Terrain 3-D LIDAR Model Vehicle state update 50 m detection range ARTIS Closed Loop Simulation 180 degree scan plane 360 degree of 2-D scan plane Roadmap-Based Planner Path-based velocity command (V K, gamma, chi) Laser beam collision detection Extracted polygons A Priori Unknown Polygons

9 Folie 9 Roadmap-Based Decision Making Greedy Mapping: Select Best Next Waypoint B Mapping vertex B map B map Uniform edge costs Roadmap perimeter defines volume to be mapped A A A A 1 A 2 Mapped vertices Current A to B path, no path segment to B map

10 Folie 10 Roadmap-Based Decision Making Greedy Mapping: Select Best Next Waypoint B Strategies to mark vertices as mapped: 1. Visited: Physically passed or reached by the vehicle. 2. Scanned: All edges to and from a vertex have been inside sensor FOV. 3. Uninformative: If vertex is detected by mapping sensor, it is not considered to provide useful information anymore. 1) Visited proximity radius threshold 2) Scanned 3) Uninformative All edges Edge at least once completely within FOV Mapping sensor FOV

11 Folie 11 Exploration Scenario Urban Terrain Berlin Potsdamer Platz A

12 Folie 12 Simulation Results Exploration of Urban Terrain Rotating LIDAR sensor Initial roadmap perimeter UAV

13 Folie 13 Simulation Result Exploration of Urban Terrain Flown path UAV Rotating LIDAR Current mapping path Remaining narrow corridor (width < 20 m)

14 Folie 14 Simulation Result Exploration of Urban Terrain Total mission time within max. flight time of ARTIS Terrain almost fully mapped Efficient replanning Trajectories always well clear of obstacles

15 Folie 15 Summary Online task (Re-)Planning using a Sampling-based Roadmap: 1. Greedy mapping as example application 2. (Re-)Planning benefits from multi-query property 3. Exploration behavior deterministic* Ideas for improvements: - Vertex marking strategy linked to real sensor instead of known FOV. - Non- uniform edge costs: Risk probability, account for GPS- denied loca<ons, landing sites etc.

16 Folie 16 Danke für Ihre Aufmerksamkeit! Fragen? Anregungen? Paper zum Vortrag unter oder direkt via AIAA Multi-Query Path Planning for Exploration Tasks with an Unmanned Rotorcraft