Simulation Framework for Addressing Challenges in Path Planning Evaluation for an Autonomous Surface Vehicle

Abstract

An efficient algorithm for path planning is crucial for guiding autonomous surface vehicles (ASVs) through designated waypoints. However, current evaluations of ASV path planning mainly focus on comparing total path lengths, using temporal models to estimate travel time, idealized integration of global and local motion planners, and omission of external environmental disturbances. These rudimentary criteria cannot adequately capture real-world operations. To address these shortcomings, this study introduces a simulation framework for evaluating navigation modules designed for ASVs. The proposed framework is implemented on a prototype ASV using the Robot Operating System (ROS) and the Gazebo simulation platform. The implementation processes replicated satellite images with the extended Kalman filter technique to acquire localized location data. Cost minimization for global trajectories is achieved through the application of Dijkstra and A* algorithms, while local obstacle avoidance is managed by the dynamic window approach algorithm. The results demonstrate the distinctions and intricacies of the metrics provided by the proposed simulation framework compared with the rudimentary criteria commonly utilized in conventional path planning works.

Keywords

Autonomous surface vehicle / Global path planner / Local path planner / Simulation / Robot operating system / Gazebo

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