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Robotics applications of vision-based action selection Master Project Matteo de Giacomi.

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Presentation on theme: "Robotics applications of vision-based action selection Master Project Matteo de Giacomi."— Presentation transcript:

1 Robotics applications of vision-based action selection Master Project Matteo de Giacomi

2 Contents  Introduction  Controller Architecture  Webots implementation  Visual System  Amphibot II implementation  Conclusion

3 Introduction - Project Objectives - Related works - Used robots

4 Project Objectives Use Stereo Vision to make a real robot reactively:  Avoid Obsacles  Flee from Predators  Follow Preys

5 Related Works  Schema-based architecture [Arkin]  Potential Field [Andrews] [Kathib]  Steering [Reynolds]  Subsumption architecture [Brooks]

6 Used Robots  Amphibot II 8 body elements  Salamandra body elements and legs elements Control of Speed through a Drive signal and of the direction through a Turn signal

7 Controller Architecture - Overview - Behavioral Constants - Obstacle Avoidance

8 DRIVE,TURN correct behavior obstacles predator prey yes no Memory Turn, direction, pred_pos, prey_pos pred_pos, Pred_dist, fear Prey_pos, prey,_dist, persistance Controller Architecture Motor feedback Visual input error Disp_map pred_pos, pred_dist prey_pos, prey_dist motor position

9 Behavioral Constants  Reactivity (min time between two different behaviors)  Panic (when stuck, time after that the robot starts moving randomly)  Confidence (min distance to an object before collision danger is triggered)  Daring (min distance the robot can approach the predator)  Fear (time in fleeing state after having lost eye contact with the predator)  Persistence (while a prey is lost, time in search state before giving up)

10 Obstacle Avoidance (1)  Avoid Static Obstacles  Avoid Sudden obsacles (ex. foot)  Detect Dead-ends (requiring the implementation of Backward locomotion) FORWARD Turn = max(X) Drive = x center BACKWARD Turn = const. Drive = min(x center, max(x center, X\{x center })) Drive <= 0 Drive > 0

11 Obstacle Avoidance (2)  Avoidance is triggered if an obstacle is too close (see confidence) In a clutted environment, one tends to approach obstacles more than in an open space  Confidence varies according to an estimation of obstacle density

12 Webots Implementation - action selection - influence of behavioral constants

13 Interaction between behaviors Video: obstacle avoidance, prey and predator action selection

14 Influence of behavioral constants  When both a prey and a predator are detected Fear and Daring affect robot behavior

15 Visual System - Distance Measures Analysis - Prey and Predator Tracking

16 Input Mapping (1) 1…m 1 … … n Input: m x n distance grid Output: Polar distance map. Sectors distance estimation: minima between the cells of every column (pessimist approach) min(col 1 )min(…)min(col m )

17 Input Mapping (2)  Issue: Filmed area depends on robot‘s head position  Solution: Knowing Cam Angle and Angular Speed (depending on Turn and Drive): Map Camera Field on Visual Field

18 Input Mapping (3) Video: example of depth Map generation

19 Prey and Predator Tracking (1)  Shape recognition  Prey: small circle Turn so that circle centre is set in front of the robot Stop when sufficiently close  Predator: big circle Turn away as fast as possible

20 Prey and Predator Tracking (2)  Circular Hough Transform  Left-Right Size check

21 Prey and Predator Tracking (3)  Evaluate target expected size according to distance and compare with measured size

22 Amphibot II implementation - Introduction - Battery charge influence - Obstacle avoidance: results

23 Introduction  Differences from webots: Camera‘s range: 60° instead of 120° Input: more noisy Frame rate: is smaller Drive Signal: Its relation with amplitude and frequency critically depends on the environment and the used hardware

24 Battery charge influence  Estimation or measure of battery charge impossible, world rotation phase in mapping must be skipped

25 Results Video: setup presentation, obstacle avoidance

26 Conclusion - Results - Further Works

27 Results  Stereo-Vision system Effective for both obstacle avoidance and target recognition  Behavior Scalable (a joystick was added as a new behavior with minimal variations) Quick, memory inexpensive „Natural“ parameters:  One architecture, many behaviors  Several parameters to trim, „aestetic“ criteria

28 Further works  Camera-to-Wold mapping can be improved?  How to define parameter values?  Possible addition of a planner?  How can the visual system cope with a water enviroment?  Robot gait may adapt to the type of surface?

29 THE END Thank you! Any question?


31 Amphibot‘s Input Mapping  Polar map containing 19 sectors  Robot kept on place while oscillating parallel to a wall

32 Obstacle Avoidance Video: Dead-end detection

33 Prey Cornering Behavior Video: obstacle is ignored in case a prey is present (behavior feedback)

34 Turning vs. Reactivity  Tracking in a webots simulation  Low Reactivity produces an unnatural behavior  High Reactivity makes the robot react too slowly

35 Turning Radius vs. Battery charge Video: turning performance along time with constant drive and turn

36 Drive Signal vs. Amplitude and Frequency

37 Drive vs. Obstacle distance

38 Bonus: Hough Transform Video: circle tracking

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