Dhanushka Kularatne

Time and Energy Optimal Path Planning in General Flows

Autonomous surface and underwater vehicles (ASVs and AUVs) are increasingly being used for persistent monitoring of ocean phenomena. Typically, these vehicles are deployed for long periods of time and must operate with limited energy budgets. As a result, there is increased interest in recent years on developing energy efficient motion plans for these vehicles that leverage the dynamics of the surrounding flow field. In this paper, we present a graph search based method to plan time and energy optimal paths in a flow field where the kinematic actuation constraints on the vehicles are captured in our cost functions. We also use tools from topological path planning to generate optimal paths in different homotopy classes, which facilitates simultaneous exploration of the environment. The proposed strategy is validated using analytical flow models for large scale ocean circulation and in experiments using an indoor laboratory testbed capable of creating flows with ocean-like features. We also present a Riemannian metric based approximation for these cost functions which provides an alternative method for computing time and energy optimal paths. The Riemannian approximation results in smoother trajectories in contrast to the graph based approach while requiring less computational time.

Tracking Attracting Lagrangian Coherent Structures in Flows

This paper presents a collaborative control strategy designed to enable a team of robots to track attracting Lagrangian coherent structures (LCS) and unstable manifolds in two-dimensional flows. Tracking LCS in dynamical systems is important for many applications such as planning energy optimal paths in the ocean and predicting various physical and biological processes in the ocean. Similar to existing approaches, the proposed strategy does not require global information about the dynamics of the surrounding flow, and is based on local sensing, prediction, and correction. Different from existing approaches, the proposed strategy has the ability to track attracting LCS and unstable manifolds in real-time through direct computation of the local finite time Lyapunov exponent field. The collaborative control strategy is implemented on a team of robots and the theoretical guarantees of the tracking strategy is briefly discussed. We demonstrate the tracking strategy in simulation using static and time dependent flows and experimentally validate the strategy using a team of micro autonomous surface vehicles (mASVs) in an actual fluid environment.

Zig-zag wanderer: Towards adaptive tracking of time-varying coherent structures in the ocean

Similar to the atmosphere, coherent structures, e.g., fronts, exist in the ocean. These frontal structures are known to be highly productive, supporting the whole spectrum of marine life. Ocean fronts are dynamic in time and space, and are a key component to a comprehensive knowledge of ocean dynamics and aquatic ecosystems in relation to climate change. However, dynamic features such as fronts are difficult to study through conventional oceanographic techniques. In this paper, we build upon our previous work in sampling and tracking an ocean front based on predictions and/or priors. Specifically, given a prior (that may not be accurate or up-to-date) we present and experimentally validate a method for an autonomous surface or underwater vehicle to plan a mission and adapt this mission on-the-go to track a dynamic, but coherent, structure. Experimental results using a novel indoor testbed, capable of creating controllable fluidic features in an indoor laboratory setting, are presented. These results demonstrate that the vehicle is able to adapt its path to follow a desired, time-varying contour.

Optimal Path Planning in Time-Varying Flows Using Adaptive Discretization

Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this letter, we present a graph-search-based method to compute energy optimal paths for AMVs in 2-D time-varying flows. The novelty of the proposed algorithm lies in the use of an adaptive discretization scheme to construct the search graph. We demonstrate the proposed algorithm by computing optimal energy paths using an analytical time-varying flow model and using time-varying ocean flow data provided by the Regional Ocean Model System. We compare the output paths with those computed via an optimal control formulation and numerically demonstrate that the proposed method can overcome problems inherent in existing fixed discretization schemes.

Small and Adrift with Self-Control: Using the Environment to Improve Autonomy

We present information theoretic search strategies for single and multi-robot teams to localize the source of a chemical spill in turbulent flows. In this work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies. Using the spatial distribution of the sensor readings, robots construct a belief distribution for the source location. Motion strategies are designed to maximize the change in entropy of this belief distribution. In addition, we show how a geophysical description of the environmental dynamics can improve existing motion control strategies. This is especially true when process and vehicle dynamics are intricately coupled with the environmental dynamics. We conclude with a summary of current efforts in robotic tracking of coherent structures in geophysical flows. Since coherent structures enables the prediction and estimation of the environmental dynamics, we discuss how this geophysical perspective can result in improved control strategies for autonomous systems.

Going with the flow: a graph based approach to optimal path planning in general flows

Autonomous surface and underwater vehicles (ASVs and AUVs) used for ocean monitoring are typically deployed for long periods of time and must operate with limited energy budgets. Coupled with the increased accessibility to ocean flow data, there has been a significant interest in developing energy efficient motion plans for these vehicles that leverage the dynamics of the surrounding flow. In this paper, we present a graph search based method to plan time and energy optimal paths in static and time-varying flow fields. We also use tools from topological path planning to generate optimal paths in different homotopy classes to facilitate simultaneous exploration of the environment by multi-robot teams. The proposed strategy is validated using analytical flow models, actual ocean data, and in experiments using an indoor laboratory testbed capable of creating flows with ocean-like features. We also present an alternative approach using a Riemannian metric based approximation for the cost functions in the static flow case for computing time and energy optimal paths. The Riemannian approximation results in smoother trajectories in contrast to the graph based strategy while requiring less computational time.

Tracking attracting manifolds in flows

This paper presents a collaborative control strategy designed to enable a team of robots to track attracting Lagrangian coherent structures (LCS) and unstable manifolds in two-dimensional flows. Tracking LCS in flows is important for many applications such as planning energy optimal paths in the ocean and for predicting the evolution of various physical and biological processes in the ocean. The proposed strategy which tracks attracting LCS and unstable manifolds in real-time through direct computation of the local finite time Lyapunov exponent field, does not require global information about the dynamics of the surrounding flow, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented on a team of robots and theoretical guarantees for the tracking and formation keeping strategies are presented. We demonstrate the performance of the tracking strategy in simulation using actual ocean flow data and experimental flow data generated in a tank. The strategy is validated experimentally using a team of micro autonomous surface vehicles in an actual fluid environment.

Design and validation of a micro-AUV for 3-D sampling of coherent ocean features

The ocean, as vast as it is complex, has a plethora of phenomena that are of legitimate scientific interest, e.g., ocean fronts and Lagrangian Coherent Structures. These coherent ocean features occur from tidal mixing and ocean circulation, and are generally characterized with narrow bands of locally intensive physical gradients with enhanced circulation, biological productivity, and optimal transport phenomena. Spatial extents of these phenomena can be on the order of 10s of km2, and episodic events can last from hours to weeks. These ocean features are 3-dimensional, where to date, most research has focused on examining only their 2-dimensional expression. These coherent features cannot be thoroughly studied through traditional sampling involving random and/or discrete sampling approaches, moreover it is not cost-effective to validate new sampling methodologies in the field. Additionally, operating a single robotic platform in the ocean is hard, and coordinating a team of robots presents challenges in communication on top of dealing with navigation and complex ocean dynamics. To this end, in this paper we present the development and validation of a micro Autonomous Underwater Vehicle for deployment in a laboratory testing tank able to accurately simulate large-scale ocean dynamics. The goal is to provide a laboratory-scale, underwater vehicle for validating and testing algorithms and strategies to sample the 3-dimensional structure that exists in coherent ocean features, e.g., ocean fronts, eddys and Lagrangian Coherent Structures, for the purpose of developing better physical and biological models to aid autonomous ocean research. We provide a detailed description of the vehicle and present multiple results from lab experiments.