1. HARDWARE-IN-THE-LOOP TEST RIG FOR NEAR-EARTH AERIAL ROBOTICS Vefa Narli, Paul Y. Oh Drexel Autonomous Systems Lab (DASL) Mechanical Engineering and Mechanics, Drexel University 2006 ASME International Design Engineering Technical Conferences

2. Mission Environments Robots to handle dirty, dangerous and dull missions Cluttered Cluttered Low visibility Low visibility – Smoke – Dust – Weather conditions

3. Notional Mission Identify a building Identify features Hover Slow motion Bushes, wires, poles, etc. Notional Vehicle Cluttered and potentionally dangerous area

4. Implementation? Autonomy? Sensor Suite: Cluttered environ. Weather cond. Current approach: Ad-hoc with many crashes and band-aid fixes Gap: No Sensor metrics to design analytically “A MAV that flies like an airplane and hovers like a helicopter”, William E. Green, Paul Y. Oh, IEEE/ASME July 2005

5. Hardware-in-the-Loop (HITL) HITL: Real time Real hardware that is being designed Math model of the other parts of the system Literature: Test Bed to design insect inspired robotic control, (Reiser) Whirling Arm Test Bed to follow terrain, (Netter) Widely accepted T&E Approach

6. HITL for Aerial Robots XYZ Gantry (6-dof) Mockup of the air vehicle Real sensors Real time Real world obstacles Sensor data to the math model Test rig emulates the motion of the real vehicle

7. Realization: Systems Integrated Sensor Test Rig (SISTR) 2300 ft 3 0.1-3000lux NI 7831-R FPGA NI 6259 mDAQ Model Reference Adaptive Control Sponsor: NSF CAREER Grant #: 0347430 Real time, near-Earth missions (e.g. Hover-and-stare)

8. Model Reference Adaptive Control (MRAC) Adaptive control used to tune gains Error = 0, plant (gantry) emulates model (aircraft) Capable of near-hover speeds with decoupled eqns of motion

9. Sanity Check: Pendulum

10. HITL Tests Sensor Modeling Sensor Suite Design Collision Avoidance Tests DUST: 0-2.02 x 10 -4 lbs/ft 3 (Army Reg 70-38 Sec 2-8f(1): fine sand 1.32 x 10-4 lbs/ft 3 ) RAIN: (0.01 to 0.2 in/min) (Army Reg 70-38 Sec 2-8a 0.03 in/min flow rate) Sheet of rain: 8 ft W X 2ft D X 10 ft H

11. Filling the gap: Collected sensor metrics in varying lighting, rain, fog HITL Tests

12. Baseline Sensor Modeling Plywood Obstacle Baseline tests to show the sensor modeling capability of the test rig Cinder Block Obstacle Real world obstacles that an aerial robot would encounter 800 data per distance 3 in increments (9 in-30 in)

13. Test Case Example: IR Sensor Gaussian Can increase sensor accuracy by increasing the number of measurements Material/color affects the sensor response

14. 1D Collision Avoidance

15. Conclusions  No sensor metrics in near-Earth environments  Sensor suite design is crucial for autonomous flight  Current collision avoidance systems are not based on analytical design Contributions Hardware-in-the-loop test rig Lidar, optic flow, ultrasonic, and infrared sensor tests  with real world obstacles such as trees, walls made of cinder block and plywood, poles, and cables  with controlled lighting, rain flow rate, fog and dust density conditions

16. FUTURE WORK Incorporate aircraft dynamics for dynamic tests, and collision avoidance tests More sensor tests with different environments, and sensors Sensor suite design Autonomous aerial transport

17. Acknowledgements