We investigate collaborative coverage with a self-organizing swarm of minimalist robots. The inspection of the compressor section of a (jet) turbine engine is a motivating case study (see
IROS'06 and
BSSAC'07 for a high-level overview).
Based on a simple, randomized solution in which robots do not collaborate (
ISER'04), we introduce local communication among the robots in order to improve dispersion in the environment and thus speed up coverage progress (
ISER'06).
As the individual robots are extremely unreliable due to sensor and actuator noise, we capture the dynamics of the swarm using probabilistic microscopic and macroscopic models (
DARS'04,
ICRA'05,
DARS'06), which can serve as a prediction and design tool (
SYROCO'06).
We contrast this approach with deliberative algorithms that theoretically provide complete coverage (
ISER'06), but decay to probabilistic completeness due to sensor and actuator noise (
ICRA'07). Currently, we are investigating collaborative approaches, where information about task progress is shared using a custom developed
radio module running
TinyOS, and offline algorithms that provided near-optimal coverage paths to the robot team.
Real robot experiments are carried out with a swarm of up to 40
Alice miniature robots, developed at the
Autonomous Systems Laboratory by
Gilles Caprari. For actual inspection and enhanced navigation/localization, we equipped
the Alice platform with a miniature camera that allows us capturing
30x30 pixels RGB image at around 2Hz (
video). We also use the realistic simulator
Webots to systematically study the impact of different hardware constraints.
