ROS Interfaced Autonomous Control System in the Loop Real time CarMaker simulated sensor data for system under test

Transcription

1 ROS Interfaced Autonomous Control System in the Loop Real time CarMaker simulated sensor data for system under test Graham Lee*, Zaour Raouf, Abdul Nasser Raouf, Jakobus Groenewald and Gunwant Dhadyalla Dr Graham Lee, Lead Engineer 12 th September 2018

2 Outline About WMG INTACT Project ROS Virtual Environment Configuration Base Map & Localisation Further Work

3 About WMG An academic department within the science faculty Established in 1980 by Professor Lord Bhattacharyya as Warwick Manufacturing Group to facilitate technology transfer and knowledge creation for Industry 500+ people (800+ university and industry) working in 7 buildings Training over 1,500 individuals in the UK and abroad (from school to post experience) Co-located with Jaguar Land Rover & Tata Motors European Technical Centre National Automotive Innovation Centre (NAIC)

4 WMG Intelligent Vehicles Research Our Vision: To test or evaluate any new technology (infrastructure, communications and on-vehicle) in representative real world conditions with a driver in the loop. EPSRC funded 3xD Simulator - Full vehicle integration degree cylindrical screen with HD projection and surround sound - Driver monitoring - Enclosed in RF shielded room (Faraday Cage) - Emulation of a complex external signal environments such as GPS, ITS, 3G/4G.

5 INTACT INnovative Testing of Autonomous Control Techniques Innovate UK, 1.1M project Partners: RDM Group and WMG. Develop a simulator concept, to enable the design, test and evaluation of a reduced cost and optimised autonomous control system (ACS) for a L-SATS (Low- Speed Autonomous Transport System) Integrate the ACS with a safe, controlled and scientifically rigorous virtual environment. Simulate real time ACS sensor and vehicle data: LiDAR, camera, odometry, vehicle kinematics. Apply hazard analysis technique to determine scenarios and ensure test coverage. Conduct scenario based tests

6 Overview Key: ROS Network

7 What is ROS? Robotics Operating System (ROS) ROS is an open-source, meta-operating system for your robot. It provides the services you would expect from an operating system, including hardware abstraction, low-level device control, implementation of commonly-used functionality, message-passing between processes, and package management. It also provides tools and libraries for obtaining, building, writing, and running code across multiple computers.

8 ROS Key Features Software Reuse Less time spent on setup and creating interfaces, more time spent on exploring new ideas. Modular Design - Pick and choose the parts for your project. Distributed Computation Provides communication between multiple processes that can be hosted on multiple processing platforms in more than one location. Multi-lingual C++, Python, Matlab BSD License - Allows reuse in commercial and closed source products. Drawbacks of ROS Real time computation (Ubuntu, not true hard real time OS) Lack of support for embedded systems Security ROS2 looking to address some of these issues

9 Schematic Configure virtual sensors and vehicle to operate in real time freespace UAQ VDS UAQ UAQ Create base map Pod localisation ACS scenario tests

10 Sensor Configuration - LiDAR Physical Sensor Max Scanning Range 30m Scanning Angle (field of view) 270 Angular Resolution 0.25 Number of Beams per Scan 1081 Scanning Frequency 40Hz Virtual Freespace Sensor Config Virtual Freespace Sensor Scanning Angle (field of view) x-plane Field of View (degrees) y-plane Scanning Frequency 270 Number of Beams 1082 Beam divergence 0.08mrad Beam divergence: beams, use every 50 th beam Result = 1082 beams Beam Divergence = 0.08mrad 1 (minimum that can be defined) 20 Hz

11 Sensor Configuration LiDAR ROS Message

12 Sensor Configuration Stereo Camera Physical Sensor VDS Config Resolution 640x480 Frame Rate 30 Chroma Y16, RGB24 Sensor Type CCD Sensor Format 1/3" Pixel Size 6 µm Video Data Stream (VDS) Resolution 640x480 pixels Field of View Frame Rate 10 Hz Colour Format Monochrome CarMaker Raw Camera Data Object Detection Algorithm ROS SENSOR_MSGS/CAMERAINFO.MSG SENSOR_MSGS/IMAGE.MSG ROS MSG OBJECTS CO-ORDINATES Public ROS Nodes

13 Stereo Camera Calibration CarMaker Raw Camera Data Object Detection Algorithm Public ROS Nodes CarMaker VDS Configuration nviews 2 Socket 2210 SyncWindow 0.01 SyncTimeout 2 Global Parameters # default Stereo Camera Calibration ROS SENSOR_MSGS/CAMERAINFO.MSG SENSOR_MSGS/IMAGE.MSG ROS MSG OBJECTS CO-ORDINATES 0 { } Channel 0 (Camera Left) Width 640 Height 480 FieldOfView Export {grey} FrameRate 30 VPtOffset_x 2.8 VPtOffset_y 0.03 VPtOffset_z 1.3 xrot 0 yrot 0 zrot 0 1 { } Channel 1 (Camera Right) Width 640 Height 480 FieldOfView Export {grey} FrameRate 30 VPtOffset_x 2.8 VPtOffset_y VPtOffset_z 1.3 xrot 0 yrot 0 zrot 0 TCP/IP VDS Receiver, Visualiser and ROS Packer

14 Vehicle Configuration Kinematics Vehicle Dimensions, centre of mass, electric motor Tyres Steering Geometry

15 Vehicle Control Closing the Loop Request and status - pod control data - ROS topics /pod_to_acs and /acs_to_pod of custom message type. - Data exchanged include: wheel odometry, throttle, steering, park brake. - Wheel odometry calculated based on the physical properties of the tyre and wheel encoder counters characteristics. - Message rate 50Hz. True vehicle position for debugging - UAQs used to extract the true vehicle position in the global world frame - X, Y, Z, RX, RY, RZ co-ordinates packaged in ROS topic /trueodom of message type nav_msgs/odometry.msg

16 Schematic Configure virtual sensors and vehicle to operate in real time freespace UAQ VDS UAQ UAQ Create base map Pod localisation ACS scenario tests

17 Base Map Creation

18 Base Map Creation

19 Base Map Generation

20 Localisation

21

22 Scenario Definition Used STPA (Systems Theoretic Process Analysis) for hazard identification Systems approach for test scenario creation STPA Stage 0 Control Structure Definition STPA Stage 1 Unsafe Control Action identification STPA Stage 2 Process understanding. Hazard identification Scenario Creation methodology (including pass/fail criteria) Ongoing work - test automation and results management!

23 Approach and Performance Sensors - enough fidelity with entire simulation running at real time or better. First introduction of ROS in Matlab R2015a. Performance still not optimum. Simulink approach required balancing the work load across more than machine. CarMaker ROS Simulink vs C code approach RT ~ 0.9 ACS RT ~ 1.4

24 Further Work More realistic and detailed simulated sensor data Material properties Light propagation properties 3D LiDAR Multiple stereoscopic cameras (HDR, higher resolution) Complex collision boxes Optimise runtime performance - evaluate and measure real time performance Work extended in Innovate UK Smart ADAS Verification and Validation Methodology (SAVVY) project Radar Simulated data provided in front of camera

25 Summary Successfully integrated a hardware ACS with CarMaker Provided simulated sensor and vehicle data to the ACS using ROS Closed the control loop and demonstrated the localisation of the pod Highlighted the benefits and drawbacks of using ROS as the glue to create testing frameworks

26 ROS Interfaced Autonomous Control System in the Loop Real time CarMaker simulated sensor data for system under test Graham Lee*, Zaour Raouf, Abdul Nasser Raouf, Jakobus Groenewald and Gunwant Dhadyalla Dr Graham Lee, Lead Engineer Acknowledgements The authors would like to thank Innovate UK, RDM Group and WMG for funding this research. Special thanks to IPG Automotive for providing us with their software and for their ongoing technical support.