Current Projects:  

Motion Planning for Unmanned Vehicles (UxV):  The main objective of this project is to develop real-time motion planning algorithms for autonomous vehicles in dynamic and uncertain environment.The problem of operation in a cluttered urban environment is an especially difficult one due to limited work space available for maneuvering. Buildings and other obstructions often limit the vehicle’s line of sight, and can also hinder other required functions such as visual assessments. The key for success in real-time motion planning is their ability to recognize obstacles in real-time and take affirmative actions as quickly as possible and viable to vehicle dynamics. As a consequence, the success of such missions is highly correlated to the robustness of the algorithms that navigate these vehicles in an uncertain environment. The main focus of our work is the development of localization & motion planning algorithms enabling automated navigation of vehicles in unknown environment while creating a map of the neighboring environment from sensed information.

Uncertainty Propagation through Nonlinear Dynamical Systems: This problem involves the study of the time evolution of the state probability density function corresponding to the state of a dynamical system using measurements from multiple sources. Mathematically, it is a formidable problem to solve because of the issues like positivity, normality, discretization, and most importantly, the dimensionality of the system.  Conventional approaches such as Kalman filters, ensemble filters, and particle filters work well when measurement updates are available frequently, however; there is no way of updating the probability density function (characterizing uncertainty) weights during propagation. The objective of this project is to develop novel analytical and computational tools for efficient propagation of uncertainties through nonlinear dynamical systems while using Fokker Plank Equation error as feedback to update the weights in the absence of measurement data. Applications of interest include tracking of a space object for the determination of its orbit, diffusion of Chem-Bio Radioactive Nuclear (CBRN) material and mobility prediction for mobile robots in uncertain environment.

Image-Guided Tracking of Internal Organ and Tumor Motion: This research work deals with the development of safe and effective “Adaptive Conformal Radiation Therapy” for cancer treatment while minimizing the relapse rate of tumor and side effects of the lethal radiation dose. The main objective of this research work is to design and test a novel framework for accurate estimation of 7-D (position + orientation) tumor target dynamics based on the correlation of real-time imagery data from external and internal fiducial markers. The core tool at the heart of our approach is recently developed adaptive multi-resolution system identification algorithm, which makes use of recent advances in Neural Networks, Finite Element Methods and Nonlinear Adaptive Control. An important aspect of our work is to adapt respiratory models in real-time which helps us in addressing many critical issues specific to image-guided radiation therapy such as distinguishing between patient movement and respiratory motion, signal deficiencies, time latency, frequency of the measurement data and uncertainty in the breathing models currently in use.

Last Updated: Wed, August 22, 2007 20:54