Labor Division and Distributed Sensing in Swarm Systems
William Agassounon, Alcherio Martinoli
Collaborators: Robert McEliece (Caltech), Erik Antonsson (Caltech), David H. Lewis (TRW), Willy Behrens (TRW), Guy Theraulaz (CNRS, Toulouse, France), Deborah Gordon (Stanford), Jean-Louis Deneubourg (ULB, Bruxelles, Belgium).

This research project aims to devise distributed scalable control algorithms for division of labor and task allocation in mobile embedded swarm systems. Our approach is inspired by social insect societies (ants, bees, termites, etc) whose collective behavior often emerges from a series of local agent-to-agent and agent-to-environment interactions. We are currently developing response threshold-based algorithms to achieve efficient and robust division of labor, and probabilistic models that provide accurate forecast of the resulting collective behavior. These swarm systems are therefore analyzed at several implementation levels, from macroscopic and microscopic probabilistic models to real robot experiments through embodied sensor-based simulations. (full report)

Distributed Plume Tracing
Adam Hayes, Alcherio Martinoli
Collaborators: Rodney Goodman, Michael Freund, Nate Lewis
External Collaborators: Owen Holland

The objective of this project is to study biologically inspired algorithms which enable a robot or group of robots to track an odor plume to its source, with an appropriate combination of speed, efficiency, reliability, and accuracy. Research is conducted at three levels: non-embodied point simulations, embodied sensor-based simulations, and real robots. The simulations use sensors and actuators which are based on the capabilities of the real robots, and plume information is derived from empirical data files recorded from real plumes or realistic plume simulators. In simulation we explore the performance of various families of simple algorithms, as well as the potential for automated parameter tuning and on-line learning. We assess the most promising algorithms on real robots, which are equipped with Caltech olfactory sensors, anemometric devices, and simple communication systems. (full report)

Distributed Collective Building of Two-Dimensional Structures Using Autonomous Robots
Kjerstin Easton, Alcherio Martinoli
Collaborators: Joel Burdick (CNSE, Caltech), Guy Theraulaz (CNRS, Toulouse, France), Dario Floreano (EPFL, Lausanne, Switzerland), Nicolas Reeves (UQAM, Montreal, Canada)

Using autonomous robots to build three-dimensional structures is a distant goal, but the first step in approaching collective building is to construct two-dimensional architectures. Using a team of miniature Khepera robots with manipulation and vision capabilities, we will implement a building technique modeled after qualitative stigmergic construction mechanisms used by social insects. This technique will allow the robots to communicate building instructions through modifications to the local environment, avoiding dependence on explicit robot-to-robot communication and lending itself to implementation with any number of robots. (full report)

Swarm Intelligence and Traffic Safety
Yizhen Zhang, Alcherio Martinoli
Collaborators: Erik Antonsson (Caltech), Ross Olney (Delphi-Delco Automotive Systems)

A smart car that assists the driver must give warnings in dangerous situations, override the driver to avoid collisions, and help to reach the intended destination as quickly as possible. Unfortunately, satisfying these requirements and at the same time leaving the decisional autonomy at the individual level becomes an extremely hard problem to solve with traditional methods. Biologically-inspired techniques such as Swarm Intelligence and Incremental Evolution provide new promising ways to tackle the design and distributed control problems of a traffic system. In this project, solutions are developed using embodied simulations and validated with real robot experiments. (full report)