Distance and Steering Heuristics for Streamline-Based Flow Field
\n Planning

摘要

Motion planning for vehicles under the influence of flow fields can benefit
\nfrom the idea of streamline-based planning, which exploits ideas from fluid
\ndynamics to achieve computational efficiency. Important to such planners is an
\nefficient means of computing the travel distance and direction between two
\npoints in free space, but this is difficult to achieve in strong incompressible
\nflows such as ocean currents. We propose two useful distance functions in
\nanalytical form that combine Euclidean distance with values of the stream
\nfunction associated with a flow field, and with an estimation of the strength
\nof the opposing flow between two points. Further, we propose steering
\nheuristics that are useful for steering towards a sampled point. We evaluate
\nthese ideas by integrating them with RRT* and comparing the algorithm's
\nperformance with state-of-the-art methods in an artificial flow field and in
\nactual ocean prediction data in the region of the dominant East Australian
\nCurrent between Sydney and Brisbane. Results demonstrate the method's
\ncomputational efficiency and ability to find high-quality paths outperforming
\nstate-of-the-art methods, and show promise for practical use with autonomous
\nmarine robots.