Distance and Steering Heuristics for Streamline-Based Flow Field Planning
This work addresses motion planning for autonomous marine robots in challenging ocean currents, representing an incremental improvement with specific domain applications.
The paper tackled the problem of efficiently computing travel distance and direction for motion planning in strong incompressible flows like ocean currents by proposing analytical distance functions and steering heuristics. Results showed the method integrated with RRT* outperformed state-of-the-art methods in computational efficiency and path quality in both artificial and real ocean data.
Motion planning for vehicles under the influence of flow fields can benefit from the idea of streamline-based planning, which exploits ideas from fluid dynamics to achieve computational efficiency. Important to such planners is an efficient means of computing the travel distance and direction between two points in free space, but this is difficult to achieve in strong incompressible flows such as ocean currents. We propose two useful distance functions in analytical form that combine Euclidean distance with values of the stream function associated with a flow field, and with an estimation of the strength of the opposing flow between two points. Further, we propose steering heuristics that are useful for steering towards a sampled point. We evaluate these ideas by integrating them with RRT* and comparing the algorithm's performance with state-of-the-art methods in an artificial flow field and in actual ocean prediction data in the region of the dominant East Australian Current between Sydney and Brisbane. Results demonstrate the method's computational efficiency and ability to find high-quality paths outperforming state-of-the-art methods, and show promise for practical use with autonomous marine robots.