| dc.description.abstract | Motion planning for autonomous navigation in unknown environments cluttered with
obstacles is a fundamental challenge in robotics, requiring efficient, safe, and reliable
strategies for path planning. This thesis introduces two novel autonomous navigation
strategies for vehicles operating in static, unknown n-dimensional environments clut-
tered with convex obstacles. The first strategy proposes a continuous feedback controller
that steers a vehicle safely to a target destination in a quasi-optimal manner within a
“sphere world,” where each obstacle is enclosed by a sphere-shaped boundary. Under this
approach, the robot avoids obstacles by navigating along the shortest path on the sur-
face of the cone enclosing the obstacle and proceeds directly toward the target when no
obstacles obstruct the line of sight. This controller guarantees almost global asymptotic
stability in two-dimensional (2D) environments under specific obstacles configurations.
An extension of this method is also developed for real-time navigation in unknown, static
2D environments with sufficiently curved convex obstacles, maintaining the same stability
guarantees. Simulation and experimental results demonstrate the practical effectiveness
of this approach in navigating real-world environments.
While the first strategy ensures almost global asymptotic stability only under specific
conditions related to the obstacles configuration and for 2D environments, the second
strategy aims to provide a more robust solution with stronger stability guarantees. This
second strategy introduces a hybrid feedback controller designed to navigate a vehicle in
static n-dimensional Euclidean spaces cluttered with spherical obstacles. This approach
ensures safe convergence to a predefined destination from any initial position within the
obstacle-free workspace while optimizing obstacle avoidance. A novel switching mecha-
nism is proposed to alternate between two operational modes: the motion-to-destination
mode and the obstacle-avoidance mode, ensuring global asymptotic stability regardless
of the obstacles’ configuration. Numerical simulations in both known and unknown 2D
and 3D environments, along with experimental validation in a 2D setting, demonstrate
the effectiveness the proposed approach.
These strategies provide robust solutions for autonomous navigation in static, un-
known environments, contributing to the advancement of safe, efficient, and optimal
motion planning techniques for robotic systems in complex, obstacle-laden spaces. | en_US |
