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)

Microbat
Nick Pornsin-Sirirak, Yu-Chong Tai
Collaborators: Hany Nassef (UCLA), Chih-Ming Ho (UCLA), Joel Grasmeyer (AeroVironment), Matt Keennon (AeroVironment)

Through the discovery of flapping-wing (unsteady-state) aerodynamics, the world's first electric-powered palm-sized ornithopter has been successfully developed and test-flown. This effort is enabled by the use of a new titanium-alloy MEMS (Micro-Electro-Mechanical Systems) airframe/wing technology to produce light but robust 3-D wings. Parylene-C is used as wing membrane. This new wing design results in a 40% wing area reduction compared to the 1st generation wing. We have built a system that includes a lightweight NiCd battery and an electrical motor, a gearbox transmission design of 22:1 gear ratio with 90% efficiency, and a DC-to-DC voltage converter. Together, it allows us to design a complete system with necessary components within the weight budget for a successful flight. So far, the best flight duration obtained by Microbat was 18 seconds. It is mainly limited by the power source. (full report)

Super Manueverable UAV Controlled by M³ System
Fukang Jiang, Charles Grosjean, Yong Xu, Yu-Chong Tai
Collaborators: Chih-Ming Ho (MAE, UCLA), Ray Morgan, Martyn Cowley, Scott Newbert (AeroVironment Inc.)

An aircraft for the future - having no tail, controlled by M³ systems, and with no traditional control surfaces - will be developed for low altitude surveillance. A new robust system of distributed microsensors and microactuators, with associated microelectronics (a M³ system) will be designed and fabricated to satisfy flight test requirements. A new aircraft will be designed from scratch to accentuate the concept of achieving aerodynamic maneuvering through a micromachine-based deformable smart surface. This new aircraft design concept can significantly reduce weight, overall power consumption and radar cross-section. (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)

Sensing and Control for Robotic Fish Locomotion
Richard Mason, Joel Burdick

We are studying issues in fluid mechanics, nonlinear control, and sensing that are necessary for the development of self-propelled robot fish. (full report)

Detection of Human Motion in a Cluttered Scene
Yang Song, Xiaolin Feng, Luis Goncalves, Pietro Perona

Detecting humans in images is a useful application of computer vision. Loose and textured clothing, occlusion and scene clutter make it a difficult problem because bottom-up segmentation and grouping do not always work. We address the problem of detecting humans from their motion pattern in monocular image sequences; extraneous motions and occlusion may be present. We assume that we may not rely on segmentation, nor grouping and that the vision front-end is limited to observing the motion of key points and textured patches in between pairs of frames. We do not assume that we are able to track features for more than two frames. Our method is based on learning an approximate probabilistic model of the joint position and velocity of different body features. Detection is performed by hypothesis testing on the maximum a posteriori estimate of the pose and motion of the body. Our experiments on a dozen of walking sequences indicate that our algorithm is accurate and efficient. (full report)

Configurable Architectures and Systems for Real-Time Low-level Vision
Arrigo Benedetti, Pietro Perona

The long-term goal of this project is to build an infrastructure for the design and implementation of real-time computer vision systems. Since vision algorithms are compute-bound we have chosen the technology of Field Programmable Gate Array (FPGAs), that allow to exploit the parallelism inherent to the first stages of low-level vision tasks. The first problem that we have considered is the real-time computation of the optical flow measured from the sequence of images captured by a video camera. We have designed, built and demonstrated a system able to select in real-time 2-D visual features on a commercially available platform. During this process we have learned that the system level architectures of commercially available configurable systems are not optimized for low level vision tasks, therefore, we have designed a novel architecture dedicated to real-time processing of video streams. A system based on this architecture has been built and is currently being tested. More recently, we have studied the problem of bit-width computation for the optimization of the data paths found in digital video signal processors. (full report)

Learning Object Class Models
Markus Weber, Max Welling, Robert Fergus, Pietro Perona

We have developed a method to automatically learn models of visual object classes from sets of unlabeled and unsegmented training images. The method has been demonstrated to work on images of cars and handwritten characters and it is being adapted to human faces. (full report)

Finding Faces in Cluttered Scenes
Markus Weber, Michael Burl, Pietro Perona

We have designed algorithms that learn a probabilistic description of human faces and other object classes. We have implemented a real-time face detection system which runs at 1Hz and demonstrates the ability to handle deformations, occlusions and background clutter. (full report)

3D Vision with Minimal Equipment
Silvio Savarese, Jean-Yves Bouguet, Pietro Perona

The aim of our work is to investigate new approaches for three-dimensional reconstruction of objects. The proposed techniques require minimal and inexpensive equipment. (full report)

Perception and 3D Reconstruction of Specular Surfaces
Silvio Savarese, Pietro Perona

The aim of our work is to investigate how the human visual system perceives specular surfaces and which cues can be used to recover the shape of such class of objects. (full report)

3D Photography on Your Desk
Jean-Yves Bouguet, Pietro Perona

We are developing a simple and inexpensive method for extracting the three-dimensional shape of objects by using weak-structured lighting. Experimental results demonstrate that the error in reconstructing the surface is less than 1%. (full report)

Visual Input for Pen-Based Computers
Mario E. Munich, Pietro Perona

Our work focuses on the development of a visual interface for pen-based computers. We are building a system that visually tracks the trajectory of a pen in real-time and recovers the handwritten strokes with sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. (full report)

Camera-Based ID Verification by Signature Tracking
Mario E. Munich, Pietro Perona

The goal of this project is to develop a vision-based biometric technique based on visual capturing of signatures and to evaluate the performance of the system. (full report)

Optically Programmable FPGA Systems
Jose Mumbru, Gan Zhou, Arrigo Benedetti, Xin An, George Panotopoulos, Fai Mok, Demetri Psaltis, Pietro Perona

Reconfigurable processors bring a new computational paradigm where the processor modifies its structure to suit a given application, rather than having to modify the application to fit the device. The Optically Programmable Gate Array (OPGA), an enhanced version of a conventional FPGA, utilizes a holographic memory accessed by an array of VCSELs to program its logic. Combining spatial and shift multiplexing to store the configuration pages in the memory, the OPGA module is very compact and has extremely short configuration time allowing for dynamic reconfiguration. The reconfiguration capability of the OPGA can be applied to solve more efficiently problems in pattern recognition and searches in databases. (full report)

Holographic Imaging of Biological Samples
Wenhai Liu, Demetri Psaltis

We are developing an imaging system with the ability of imaging a 3D object plus the color spectrum information. It makes use of the spatial and wavelength selectivity of volume holograms, which act as multiple lens and color filters to separate the 2D slices with different color from the 3D object into various detectors. It will be a powerful tool for imaging application in cell biology, biochemistry, material research and any other 3D imaging application. (full report)

Little Piece of Cortex
George Panotopoulos, Demetri Psaltis, Pietro Perona

We introduce a model of the V1 cortex. This model is composed by an initial filter stage and two interaction stages, inspired by their biological counterparts. The model produces results matching the ones obtained by physiological experiments. (full report)

Hand Gesture Biometrics
George Panotopoulos, Demetri Psaltis

We introduce a biometric measure based on hand gestures. We use simple filters to extract features from a gesture captured in the form of still frames. We then use PCA to perform classification using these features. For small databases we obtain 100% correct classification. (full report)

Divide and Conquer Strategy for Recognition
George Panotopoulos, Demetri Psaltis

We devised a classification strategy based on the division of a single complex question to more, simpler questions. We showed that this strategy corresponds to a tree structure and can be implemented by reconfigurable computers. We demonstrated the efficiency of this strategy on the problem of classification of handwritten digits. We derived analytical expressions linking the performance of the overall classifier to the performance of its parts. (full report)