Distributed Collective Building of Two-Dimensional Structures Using Autonomous Robots
Kjerstin Easton, Alcherio Martinoli, Rodney Goodman

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)

Optimal Task Allocation and Distributed Sensing in Collective Autonomous Robotics
William Agassounon, Alcherio Martinoli, Rodney Goodman

Our research aims at studying two particular topics within the Collective Robotics field, these are the division of labor and the dynamic task allocation. The Swarm Intelligence approach can be applied to fully distributed systems that consist of several autonomous decision making entities working together with minimal communication and local perception to complete one or several tasks. Our approach is inspired by biological systems such as colonies of social insects (ants, bees, termites, etc) in which the collective behavior often emerges from a series of local agent-to-agent and agent-to-environment interactions. We are developing response threshold-based algorithms for optimal task allocation and probabilistic models that provide accurate forecast of the resulting collective behavior. Finally, one of the main strengths of this project is the attempt to create a theoretical framework for real embedded systems provided with threshold allocation mechanisms. These systems are therefore analyzed at several implementation levels, from analytical probabilistic models to real robots experiments through embodied sensor-based simulators. (full report)

Distributed Plume Tracing
Adam T. Hayes, Alcherio Martinoli, Owen Holland, Rodney M. Goodman

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)

Evolving Robust, Collective Patrolling Behavior Using Genetic Algorithms
Joseph Chen, Alcherio Martinoli, Rodney M. Goodman

Evolution is a powerful force. Harvester ants have successfully evolved to efficiently patrol their territory for different type of events (food items, enemy intrusion, etc.). The goal of this project is to study how effective and robust patrolling behavior can be evolved first in embodied, sensor-based simulations and then in real robot experiments. We will use evolutionary techniques (Genetic Algorithms, GA) for exploring the individual control parameters that play a crucial role in the team patrolling performance. In order to better understand the required individual and group capabilties for effective patrolling, we will test the influence of individual navigation capabilities and different fitness functions. We will also note whether any interesting collective behavior develops if the robots are allowed to directly communicate at each encounter, without introducing any type of stigmergic mechanism (e.g. pheromones). (full report)

Swarm Intelligence and Traffic Safety
Philip Tsao, Alcherio Martinoli, Rodney M. Goodman

An automotive controller that complements the driving experience must work to avoid collisions, enforce a smooth trajectory, and deliver the vehicle to the intended destination as quickly as possible. Unfortunately, satisfying these requirements with traditional methods proves intractable at best and forces us to consider biologically-inspired techniques such as Swarm Intelligence. A controller is currently being designed in a robot simulation program with the goal of implementing the system in real hardware to investigate these biologically-inspired techniques and to validate the results. (full report)

Distributed Turbulent Flow Control by Neural-Networked MEMS
Zhigang Han, Qiao Lin, Xuan-Qi Wang, Fukang Jiang, Thomas Tsao, Yu-Chong Tai
Collaborators: Vincent Koosh (Caltech), Rodney Goodman (Caltech), James Lew (MAE, UCLA) , Chih-Ming Ho (MAE, UCLA)

The ultimate goal of this project is to develop fully integrated MEMS with microsensors, microactuators, and microelectronics (M³) for turbulent boundary layer control. We have developed many generations of MEMS shear-stress sensors for vortex detection. The latest one is a fully integrated shear-stress sensor using a post-IC process that is added onto foundry-processed CMOS wafers. This shear-stress sensor uses a gate-polysilicon hot-wire as the sensing element that sits on a freestanding Parylene diaphragm suspended over a cavity. A special Parylene vacuum sealing and etch back process is used to achieve better thermal isolation and overall sensitivity. Wind tunnel testing of this sensor shows a sensitivity of 30 mV/Pa and a measured bandwidth of 18 kHz. We have also performed extensive theoretical analysis of these sensors. The resulting 2D MEMS shear-stress sensor theory, which includes heat transfer effects ignored by the classical theory, is verified by experimental data. We also perform 3-D heat transfer simulation and the results agree with the testing data and support the proposed new theory. (full report)

Electronic Nose Project
Samuel Tang, Rodney Goodman

The proposed electronic nose chip is composed of four parts: sensor stage, signal processing stage, database, and classifier. (full report)

VLSI Implementation of a Neural Network
Vincent Koosh, Rodney Goodman

We are developing a single chip solution to implement a feedforward neural network and training algorithm. (full report)