Vehicle Motion Planning Using Stream Functions

Borrowing a concept from hydrodynamic analysis, this paper presents stream functions which satisfy Laplace's equation as a local-minima free method for producing potential-field based navigation functions in two dimensions. These functions generate smoother paths (i.e. more suited to aircraft-like vehicles) than previous methods. A method is developed for constructing analytic stream functions to produce arbitrary vehicle behaviors while avoiding obstacles, and an exact solution for the case of a single uniformly moving obstacle is presented. The effects of introducing multiple obstacles are discussed and current work in this direction is detailed. Experimental results generated on the Cornell RoboFlag testbed are presented and discussed, as well as related work applying these methods to path planning for unmanned air vehicles

Verification of an Autonomous Reliable Wingman using CCL

We present a system of two aircraft, one human-piloted and one autonomous, that must coordinate to achieve tasks. The vehicles communicate over two data channels, one high rate link for state data transfer and one low rate link for command messages. We analyze the operation of the system when the high rate link fails and the aircraft must use the low rate link to execute a safe "lost wingman" procedure to increase separation and re-acquire contact. In particular, the protocol is encoded in CCL, the Computation and Control Language, and analyzed using temporal logic. A portion of the verified code is then used to command the unmanned aircraft, while on the human-piloted craft the protocol takes the form of detailed flight procedures. An overview of the implementation for a June, 2004 flight test is also presented

Unsupervised Learning of Individuals and Categories from Images

Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data

Unsupervised Category Discovery in Images Using Sparse Neural Coding

We present an unsupervised method for learning and recognizing object categories from unlabeled images. Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we apply a sparse generative model to the outputs of a biologically faithful model of the primate ventral visual system. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. In recognition, this model is used in a maximum-likelihood manner to classify unseen images, and we find units emerging from learning that respond selectively to specific image categories. A significant advantage of this approach is that there is no need to specify the number of categories present in the training set. We present classification accuracy using three different evaluation metrics.

Feedback Controlled Software Systems

Software systems generally suffer from a certain fragility in the face of disturbances such as bugs, unforeseen user input, unmodeled interactions with other software components, and so on. A single such disturbance can make the machine on which the software is executing hang or crash. We postulate that what is required to address this fragility is a general means of using feedback to stabilize these systems. In this paper we develop a preliminary dynamical systems model of an arbitrary iterative software process along with the conceptual framework for stabilizing it in the presence of disturbances. To keep the computational requirements of the controllers low, randomization and approximation are used. We describe our initial attempts to apply the model to a faulty list sorter, using feedback to improve its performance. Methods by which software robustness can be enhanced by distributing a task between nodes each of which are capable of selecting the best input to process are also examined, and the particular case of a sorting system consisting of a network of partial sorters, some of which may be buggy or even malicious, is examined

A new approach to teaching feedback

The Control and Dynamical Systems (CDS) Department at the California Institute of Technology (Caltech) has revised its entry-level curriculum in dynamics, feedback, and control with the goals of updating the subject matter to include modern tools and making control tools accessible to a nontraditional audience. One of the approaches made was to divide the introductory control theory class into two tracks, with a conceptual track geared toward students who need only a conceptual overview of control tools and an analytical track providing a more detailed mathematical treatment of feedback. The conceptual track, CDS 101, which is mainly discussed in the paper, is intended for advanced students in science and engineering who can benefit from an overview of control techniques but who might not have the need for the mathematical depth underlying the material. Special attention is paid to ensuring that the course is accessible to students from biological, physical, and information sciences, using examples from these domains to illustrate concepts. The goal of the course is to enable students to use the principles and tools of feedback in their research activities

Sparse Representation in the Human Medial Temporal Lobe

Recent experiments characterized individual neurons in the human medial temporal lobe with remarkably selective, invariant, and explicit responses to images of famous individuals or landmark buildings. Here, we used a probabilistic analysis to show that these data are consistent with a sparse code in which neurons respond in a selective manner to a small fraction of stimuli

Biologically Inspired Feedback Design for Drosophila Flight

We use a biologically motivated model of the Drosophila's flight mechanics and sensor processing to design a feedback control scheme to regulate forward flight. The model used for insect flight is the grand unified fly (GUF) [3] simulation consisting of rigid body kinematics, aerodynamic forces and moments, sensory systems, and a 3D environment model. We seek to design a control algorithm that will convert the sensory signals into proper wing beat commands to regulate forward flight. Modulating the wing beat frequency and mean stroke angle produces changes in the flight envelope. The sensory signals consist of estimates of rotational velocity from the haltere organs and translational velocity estimates from visual elementary motion detectors (EMD's) and matched retinal velocity filters. The controller is designed based on a longitudinal model of the flight dynamics. Feedforward commands are generated based on a desired forward velocity. The dynamics are linearized around this operating point and a feedback controller designed to correct deviations from the operating point. The control algorithm is implemented in the GUF simulator and achieves the desired tracking of the forward reference velocities and exhibits biologically realistic responses

Explicit Object Representation by Sparse Neural Codes

iii Despite having only a single name on the cover, this thesis represents the work of a great many people. Some have provided mentorship and guidance, some have directly contributed to the work and the writing, and many more have helped me to become who I am as a researcher and as a person. I have been exceedingly fortunate in the friends and colleagues I have amassed over the years, and without them none of this would have been possible. Richard Murray has been my advisor since my first days at Caltech, and has been an invaluable mentor as I have made the transition from coursework to independent research. Richard is a continual fountain of enthusiasm no matter the subject area, and has encouraged all of my explorations into a wide variety of subject areas as I sought a suitable area for thesis research. Always available to help me make progress, not with an answer but by finding the right question to ask, Richard has been a great teacher and friend. Christof Koch supervised the years of work that went into creating this thesis.