Project Title: CAREER: Generalizations in Obstacle Avoidance Theory
Investigator: Animesh Chakravarthy (PI)
Funding Agency: National Science Foundation (NSF), Award Number 1351677
Funding Division and Program: Intelligent Information Systems (IIS) – Robust Intelligence
(RI)
Funded Amount: $ 450,000 (This includes a $50,000 supplemental grant awarded in addition
to the original $400,000).
Project Duration: March 2014 – February 2019.
Abstract for main grant: This project develops a theoretical framework that enables
an analytical characterization of guidance laws for obstacle avoidance, accompanied
by an experimental validation of these laws. This has significant implications since
the obstacle avoidance problem is an important component of the path planning problem,
which appears in several diverse fields including robotics, autonomous air, ground
and underwater vehicles, computer animation, molecular motion, autonomous wheelchairs,
spacecraft avoiding space debris, robotic surgery, assistance aids for the blind,
etc. The guidance laws designed are particularly applicable for real-time implementation
of precise path planning in cluttered dynamic environments such as those containing
robot manipulators, humanoid robots, vehicles flying in formation and other high-dimensional
spaces wherein the agents have no a priori information about their environment. A
robustness analysis of the designed guidance laws to various uncertainties such as
sensor noise, data delays and data dropouts is performed, followed by an experimental
validation wherein the guidance laws are coded on microcontroller platforms in a resource-efficient
manner and implemented on small-scale robotic ground and air vehicles. The expected
results include guidance laws suitable for collision avoidance of obstacles of various,
possibly time-varying, shapes moving in high-dimensional stochastic environments,
along with a postulation of the safety guarantees of these guidance laws. This project
also performs multiple outreach activities and introduces new curriculum that promote
the education and applications of robotics, and these activities are conducted in
levels starting from K-12 all the way through undergraduate and graduate level engineering
education.
Abstract for supplemental grant: The objective of the supplemental grant is to initiate
a new collaboration with the University of Exeter, UK to develop Partial Differential
Equation (PDE)-based models of multi-vehicle systems such as swarms of flying/underwater
vehicles. Such swarms can be used to monitor various physical phenomena that evolve
in a spatio-temporal fashion, such as the dispersion of volcanic ash in the air, oil
spills in the ocean, forest fires and so on. Such physical phenomena are themselves
modeled by PDEs, and the development of PDE-based models of vehicle swarms can greatly
benefit the design of efficient, distributed control laws which ensure that the vehicle
swarm moves appropriately to track and monitor the physical phenomena.
