Opportunities to Parallelize Path Planning Algorithms for Autonomous Underwater Vehicles

TL;DR

This paper explores how to parallelize graph-based path planning algorithms for autonomous underwater vehicles, considering environment variability and inaccuracies, with plans to evaluate on multi-core systems within actual AUVs.

Contribution

It introduces parallelized path planning algorithms that incorporate robustness to forecast errors and vehicle speed variations, applicable to various autonomous systems.

Findings

Parallel algorithms enable efficient path planning in time-varying environments.

Robust algorithms account for forecast errors and speed inaccuracies.

Evaluation planned on multi-core embedded systems within AUVs.

Abstract

This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment. Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution. An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions and a different observed vehicle speed in the real mission than planned. In this context, robust path planning algorithms will be described. These algorithms are equally applicable to land based, aerial, or underwater mobile autonomous systems. The results presented here provide the basis for a future Research project in which the parallelized algorithms will be evaluated on multi and many core systems such as the dual core ARM Panda board and the 48 core Single-chip Cloud Computer (SCC). Modern multi and many core processors support a wide range of performance vs. energy tradeoffs that can be exploited in energyconstrained environments such as battery operated autonomous underwater vehicles. For this evaluation, the boards will be deployed within the Slocum glider, a commercially available, buoyancy driven autonomous underwater vehicle (AUV).