9 research outputs found
3D ensemble-based online oceanic flow field estimation for underwater glider path planning
Estimating ocean flow fields in 3D is a critical step in enabling the
reliable operation of underwater gliders and other small, low-powered
autonomous marine vehicles. Existing methods produce depth-averaged 2D layers
arranged at discrete vertical intervals, but this type of estimation can lead
to severe navigation errors. Based on the observation that real-world ocean
currents exhibit relatively low velocity vertical components, we propose an
accurate 3D estimator that extends our previous work in estimating 2D flow
fields as a linear combination of basis flows. The proposed algorithm uses data
from ensemble forecasting to build a set of 3D basis flows, and then
iteratively updates basis coefficients using point measurements of underwater
currents. We report results from experiments using actual ensemble forecasts
and synthetic measurements to compare the performance of our method to the
direct 3D extension of the previous work. These results show that our method
produces estimates with dramatically lower error metrics, with and without
measurement noise.Comment: Submitted to IROS 202
Minimally Invasive Social Navigation
Integrating mobile robots into human society involves the fundamental problem of navigation in crowds. This problem has been studied by considering the behaviour of humans at the level of individuals, but this representation limits the computational efficiency of motion planning algorithms. We explore the idea of representing a crowd as a flow field, and propose a formal definition of path quality based on the concept of invasiveness; a robot should attempt to navigate in a way that is minimally invasive to humans in its environment. We develop an algorithmic framework for path planning based on this definition and present experimental results that indicate its effectiveness. These results open new algorithmic questions motivated by the flow field representation of crowds and are a necessary step on the path to end-to-end implementations
Distance and Steering Heuristics for Streamline-Based Flow Field Planning
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