![]() |
|
Publication: Ehsan Adel Rastkhiz, Howard Schwartz and Ioannis Lambadaris
"A Fuzzy Set-based Methodology for Autonomous Navigation" |
Abstract:
This paper presents a fuzzy set-based methodology to achieve simultaneous path adherence and
local collision avoidance in autonomous navigation. It targets scenarios where a global path to
the vehicle's destination is already planned but with imperfect knowledge of the obstacles in the
environment and their dynamics. Assuming that the vehicle can localize itself and the obstacles
around it, the proposed methodology incorporates the global path and the acquired real-time
knowledge of the local obstacles by the vehicle to create a comprehensive fuzzy representation
of the environment. This fuzzy representation is then utilized to assess the desirability of the
vehicle states within an optimization framework that balances global path adherence and ob-
stacle avoidance objectives. The proposed gradient-based solution to this optimization problem
navigates the vehicle such that it maintains its global course toward the designated destination
while avoiding collision with local obstacles. Extensive simulations on a mobile robot validate
the method's efficacy in guiding vehicles along desired paths, maneuvering around obstacles
with minimal deviation from the route, and negotiating local minima without oscillations.
Additionally, simulation results highlight the proposed fuzzy set-based methodology's low
computational complexity, real-time operation capability, and adaptability in handling complex
geometries of paths and obstacles.
Keywords: Autonomous navigation, Path-planning, Collision avoidance, Fuzzy obstacle. |