Your SlideShare is downloading. ×
Arte Y Robotica
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Arte Y Robotica

464

Published on

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
464
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Exploring the IOCT’s IOCT s large fleet of helicopter robots Mario A Gongora & Benjamin N. Passow A. N
  • 2. About us Dr. Mario A. Gongora D M i AG • Senior Lecturer Benjamin N. Passow • PhD student Project: Using a fleet of helicopters to: • research aspects of emergent societal behaviours • sonic signatures to identify/control the fleet • Creative way to achieve control? • creativity in behaviour and artistic coordination?
  • 3. The Fleet • About 8 x Twister Bell 47 helicopters • Helicopter properties: • 34 cm rotor span • 210 grams net weight • Max 100 grams payload • Remote controlled • 4 actuators (2 servos, 2 motors) • flight duration: 10 minutes g
  • 4. How do they work Degrees of freedom: • 3 translational • 3 rotational Controlled by: y • Lift (overall rotors speed) • Heading (difference rotors speed) g( p) • Pitch (rotor blade angle) • Roll (rotor blade angle)
  • 5. Difficulties • Helicopters are unstable and nonlinear systems y • Difficult to achieve stability: • lik standing on a ball like di b ll • …so what do we need to do to:? • Making them autonomous • Letting them dance … without crashing!
  • 6. Autonomy • Autonomy = helicopter can control itself without the need for remote control • Embedded system developed • H li t can now hover Helicopter h autonomously using (lightweight): • Digital Di it l compass • Distances to ground • Classical control methods • Computation done on-board
  • 7. HeRo in detail Stabilising flybar Dual Rotors Digital Compass (CMPS03) Processing unit (Microchip dsPIC30F) 2 strong DC motors Battery pack 2 servos (LiPo 7.4V) 3 Sonar Sensors (SRF08)
  • 8. What we have • Current prototypes: • Capable of hovering at a predefined height • Future work: • Stable flight manoeuvres • Controlled flight of a whole swarm • Coordination among the swarm • Application in research & performance art
  • 9. Movie
  • 10. Restrictions & Limitations • DANGEROUS! No flying around people! • Limited payload • Remote: 100 gram max • Autonomous: 60 gram max • Limited flight duration: ~10 minutes • Indoor use only • Due to sensitivity to wind
  • 11. Optimising the Controller • Enhance stability by evolving controller’s parameters • Using Evolutionary Computing (GA) • Optimising 5 parameters of PID controller p gp • Implemented on host computer • Evaluation on real system • GA can run fully automatic once started
  • 12. GA Setup • P ibl solutions evaluated on real helicopter Possible l ti l td l h li t • Helicopter bound to turn-table • Controller to react to artificial perturbation to both sides • Fitness inverse proportional to amount of error to set point
  • 13. Movie
  • 14. GA Results • Found better solution than hand-tuning • Noise and uncertainties in real system: • Significant variability re-evaluating individuals l ti i di id l • Keeping GA running • GA finds more “consistent” solutions • Less variability y Fig. 1. GA (black) and hand tuned (gray) PID controllers response to heading perturbed by 90◦ at and more robustness t=0 and -90◦ at t=92. Mean of 12 individual tests for each controller
  • 15. Creative Approach • We need to enhance the stability, achieve in-air synchronisation as well as obstacle avoidance • Could be done adding many sensors • Helicopter would become too heavy p y • Instead we use a novel creative approach • Using the intrinsic sound signature of the helicopter • Not a single additional sensor is needed
  • 16. Control using Sound The helicopter s intrinsic sound signature is recorded and helicopter’s analysed by the HaRT robot
  • 17. HaRT Robot • HaRT - Humans and Robots Together • for human-robot interaction • (it doesn’t attempt to look humanoid) • Is controlled by super-computer • Recording and analysing Helicopter sound signatures • HaRT has microphones to record sound signatures • Super-computer analyses these
  • 18. Sound = Information • Motors and rotors generate vibrations • Vibration = Sound • Sound acquired by HaRT • Sound = Information • Where is the sound coming from? (Localisation) • The power of the motors reflects on the sound • The difference between motor speeds also affects the sound • Servos generate sound too
  • 19. Enhance control • Coordination information is fed back to helicopter using a Bluetooth link • Controller to incorporate this new information • “Too far left” – Fly to the right • “Other h li h helicopter close to the right” – Fly a bit left l h ih l bi l f • Etc. • Benefits: • Enable in-air synchronisation • Enhance stability
  • 20. Aesthetic Applications • HaRT to translate sonic signatures into musical calls • Swarm of helicopters in formation flight and dancing • Performance art using helicopter’s with coloured trails • Helicopter reacting to music / dancing (inverse control of performance) • Dancing helicopters with lights attached in darkness i h li i h li h h di d k • Many more…
  • 21. ?

×