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

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

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

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

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

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

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

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

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

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

343m/s 3X10 8 m/s

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

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

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

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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