No abstract

We present the design and validation of the multi-robot coherent structure testbed (mCoSTe). The mCoSTe is an experimental testbed that is used to evaluate the performance of manifold and coherent structure tracking strategies by a team of autonomous surface vehicles in two-dimensional flows. It consists of a fleet of micro-autonomous surface vehicles (mASVs) equipped with onboard flow sensors and three experimental flow tanks: a Low Reynolds number (LoRe) Tank, a High Reynolds number (HiRe) Tank, and a Multi-Robot (MR) Tank. Each of the flow tanks are capable of producing controllable ocean-like flows in a laboratory setting. Flows in the HiRe and MR tanks are generated using a grid of independently controlled vortex driving cylinders. We show how the HiRe tank is capable of producing repeatable and controllable coherent structures in 2D by analyzing the surface flows using a a combination of Finite-Time Lyapunov Exponents (FTLE) and Dynamic Mode Decomposition (DMD). Using these results, a scaled flow is replicated in the MR Tank for experimental validation of robotic tracking strategies. Building upon our existing work, robotic tracking of manifolds and coherent structures in 2D flows is achieved through local sampling of the flow field using each vehicles onboard flow sensors. We describe the design and development of the mASVs and the onboard flow sensor and present experimental results to show the validity of our designs.

In this paper, we describe the development of an experimental testbed capable of producing controllable ocean-like flows in a laboratory setting. The objective is to develop a testbed to evaluate multi-robot strategies for tracking manifolds and Lagrangian coherent structures (LCS) in the ocean. Recent theoretical results have shown that LCS coincide with minimum energy and minimum time optimal paths for autonomous vehicles in the ocean. Furthermore, knowledge of these structures enables the prediction and estimation of the underlying fluid dynamics. The testbed is a scaled flow tank capable of generating complex and controlled quasi-2D flow fields that exhibit wind-driven double-gyre flows. Particle image velocimetry (PIV) is used to extract the 2D surface velocities and the data is then processed to verify the existence of manifolds and Lagrangian coherent structures in the flow. The velocity data is then used to evaluate our previously proposed multi-robot LCS tracking strategy in simulation.

In this paper, we present a first attempt toward experimental validation of a multi-robot strategy for tracking manifolds and Lagrangian coherent structures (LCS) in flows. LCS exist in natural fluid flows at various scales, and they are time-varying extensions of stable and unstable manifolds of time invariant dynamical systems. In this work, we present the first steps toward experimentally validating our previously proposed real-time manifold and LCS tracking strategy that relies solely on local measurements. Although we have validated the strategy in simulations using analytical flow models, experimental flow data, and actual ocean data, the strategy has never been implemented on an actual robotic platform. We demonstrate the tracking strategy using a team of micro autonomous surface vehicles (mASVs) in our laboratory testbed and investigate the feasibility of the strategy with vehicles operating in an actual fluid environment. Our experimental results show that the team of mASVs can successfully track LCS using a simulated velocity field, and we present preliminary results showing the feasibility of a team of mASVs tracking manifolds in real flows using only local measurements obtained from their onboard flow sensors.

We address the development of a distributed control strategy for tracking Lagrangian coherent structures (LCS) in a geophysical fluid environment like the ocean. LCS are time-dependent structures that divide the flow into dynamically distinct regions and are important because they enable the estimation of the underlying geophysical fluid dynamics. In this work, we present a distributed formation control strategy designed to track stable and unstable manifolds. We build on our existing work and present an N-robot leader-follower tracking strategy that relies solely on local sensing, prediction, and correction. Our approach treats the N-robot team as a deformable body where distributed formation control for tracking coherent structures and manifolds is achieved using a sequence of homogeneous maps. We discuss the theoretical guarantees of the proposed strategy and validate it in simulation on static flows as well as the time-dependent model of a winddriven double-gyre often seen in the ocean.