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.
