Heterogeneous Teams of Modular Robots for Mapping and Exploration by Grabowski et. al

1. Heterogeneous Teams of Modular Robots for Mapping and Explorationby Grabowski et. al

2. Abstract • Design of a team of Heterogeneous robots of various sizes and capabilities • Team collaboration to map and explore unknown environments • Focus on design and operation of Millibots

3. Advantages of a team of heterogeneous robots • Size of a robot determines its capabilities • All the robots need not have every capability with respect to sensing and communication • Less expensive robots that are easier to maintain and debug

4. The Team • All Terrain Vehicles(ATVs) • Pioneer robots • Medium-sized Tank robots • Centimeter scale Millibots

6. The Team • All Terrain Vehicles(ATVs) • Completely autonomous, range of up to 100 miles • Extensive computational power • Can act as a “mother” in a marsupial robot team • Pioneer robots • Platforms which allow the team to dynamically exchange algorithm and state information while on-line

7. The Team • Medium-sized Tank robots • Medium-sized, autonomous robots with infrared and sonar arrays and swivel mounted camera • On-board 486 computer • Capable of action as individual or as leader or coordinator of a millibot team

8. Millibots • Small and lightweight robots • Can access small closed spaces and are inconspicuous • Small size limits mobility range, communication and computation

9. Millibot Architecture - Specialization • Specialization • Every robot does not need every capability • Instead, build specialized robots for particular aspects of each task • Advantage • Reduction of power, volume, and weight of the robot • Disadvantage • Disadvantage • Sacrifices redundancy in the team

10. Millibot Architecture - Modularity • Architecture consists of number of sub-systems • Each sub-system is self-contained with processor and interface circuitry • Seven sub-systems currently included – Motor control, sonar, Infra-red, localization, communication and main processor • Sub-systems share a common bus for data and timing signals

11. Collaborative Localization • Collaboration is essential to overcome limitations imposed by size • Millibots use trilateration for localization • Each robot periodically emits radio and ultrasound pulses • Difference between arrival of the two pulses is stored by each receiver • Position of each robot is obtained using a maximum likelihood detector with computation only on a team leader

12. 343m/s 3X108m/s

13. Mapping and Exploration • Team level strategy essential for this task as sensor range is limited (~50cm) • Maintaining localization is critical • Robots rely on LOS beckoning • Team leader(or human operator) • Merges the local map information from the robots to create a global view • Can direct the robots to unexplored areas

14. Map Representation • Occupancy grid with a Bayesian update rule • Allows the combination of sensor readings from different robots and different time instances • Any sensor that can convert it’s data into a probability can be merged into the map • Occupancy value • 1: occupied by an obstacle • 0: free cell • 0.5: intial

15. Experimental Results • Task to explore and map as much area as possible before the team failed • Possible failures included • Loss of localization, loss of battery power, loss of communication • For each experiment • Three Millibots equipped with sonar arrays for collecting map information • Two Millibots equipped with camera modules to aid in obstacle identification and provide a level of fault tolerance • All equipped with localization module

16. Experimental Results • First experiment • Test and verify the team’s ability to localize and collect map data • Second experiment • Detect and avoid obstacles and remain operational for more than an hour • Loss of a camera robot but mission was continued • Third experiment • Large number of obstacles invisible to sonar • Heavy reliance on cameras reduces exploration speed

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