Kanna Rajan

Robotics in Remote and Hostile Environments

In our continuing quest for knowledge, robots are powerful tools for accessing environments too dangerous or too remote for human exploration. Early systems functioned under close human supervision, effectively limited to executing preprogrammed tasks. However, as exploration moves to regions where communication is ineffective or unviable, robots will need to carry out complex tasks without human supervision. To enable such capabilities, robots are being enhanced by advances ranging from new sensor development to automated mission planning software, distributed robotic control, and more efficient power systems. As robotics technology becomes simultaneously more capable and economically viable, individual robots operated at large expense by teams of experts are increasingly supplemented by teams of robots used cooperatively under minimal human supervision.

A deliberative architecture for AUV control

Autonomous Underwater Vehicles (AUVs) are an increasingly important tool for oceanographic research demonstrating their capabilities to sample the water column in depths far beyond what humans are capable of visiting, and doing so routinely and cost-effectively. However, control of these platforms to date has relied on fixed sequences for execution of pre-planned actions limiting their effectiveness for measuring dynamic and episodic ocean phenomenon. In this paper we present an agent architecture developed to overcome this limitation through on-board planning using Constraint- based Reasoning. Preliminary versions of the architecture have been integrated and tested in simulation and at sea.

Coordinated sampling of dynamic oceanographic features with underwater vehicles and drifters

We extend existing oceanographic sampling methodologies to sample an advecting feature of interest using autonomous robotic platforms. GPS-tracked Lagrangian drifters are used to tag and track a water patch of interest with position updates provided periodically to an autonomous underwater vehicle (AUV) for surveys around the drifter as it moves with ocean currents. Autonomous sampling methods currently rely on geographic waypoint track-line surveys that are suitable for static or slowly changing features. When studying dynamic, rapidly evolving oceanographic features, such methods at best introduce error through insufficient spatial and temporal resolution, and at worst, completely miss the spatial and temporal domain of interest. We demonstrate two approaches for tracking and sampling of advecting oceanographic features. The first relies on extending static-plan AUV surveys (the current state-of-the-art) to sample advecting features. The second approach involves planning of surveys in the drifter or patch frame of reference. We derive a quantitative envelope on patch speeds that can be tracked autonomously by AUVs and drifters and show results from a multi-day off-shore field trial. The results from the trial demonstrate the applicability of our approach to long-term tracking and sampling of advecting features. Additionally, we analyze the data from the trial to identify the sources of error that affect the quality of the surveys carried out. Our work presents the first set of experiments to autonomously observe advecting oceanographic features in the open ocean.

Towards Deliberative Control in Marine Robotics

We describe a general purpose artificial-intelligence-based control architecture that incorporates in situ decision making for autonomous underwater vehicles (AUVs). The Teleo-reactive executive (T-REX) framework deliberates about future states, plans for actions, and executes generated activities while monitoring plans for anomalous conditions. Plans are no longer scripted a priori but synthesized onboard with high-level directives instead of low-level commands. Further, the architecture uses multiple control loops for a “divide-and-conquer” problem-solving strategy allowing for incremental computational model building, robust and focused failure recovery, ease of software development, and ability to use legacy or nonnative computational paradigms. Vehicle adaptation and sampling occurs in situ with additional modules which can be selectively used depending on the application in focus. Abstraction in problem solving allows different applications to be programmed relatively easily, with little to no changes to the core search engine, thereby making software engineering sustainable. The representational ability to deal with time and resources coupled with Machine Learning techniques for event detection allows balancing shorter term benefits with longer term needs, an important need as AUV hardware becomes more robust allowing persistent ocean sampling and observation. T-REX is in regular operational use at MBARI, providing scientists a new tool to sample and observe the dynamic coastal ocean.

Data-driven robotic sampling for marine ecosystem monitoring

Robotic sampling is attractive in many field robotics applications that require persistent collection of physical samples for ex-situ analysis. Examples abound in the earth sciences in studies involving the collection of rock, soil, and water samples for laboratory analysis. In our test domain, marine ecosystem monitoring, detailed understanding of plankton ecology requires laboratory analysis of water samples, but predictions using physical and chemical properties measured in real-time by sensors aboard an autonomous underwater vehicle (AUV) can guide sample collection decisions. In this paper, we present a data-driven and opportunistic sampling strategy to minimize cumulative regret for batches of plankton samples acquired by an AUV over multiple surveys. Samples are labeled at the end of each survey, and used to update a probabilistic model that guides sampling during subsequent surveys. During a survey, the AUV makes irrevocable sample collection decisions online for a sequential stream of candidates, with no knowledge of the quality of future samples. In addition to extensive simulations using historical field data, we present results from a one-day field trial where beginning with a prior model learned from data collected and labeled in an earlier campaign, the AUV collected water samples with a high abundance of a pre-specified planktonic target. This is the first time such a field experiment has been carried out in its entirety in a data-driven fashion, in effect “closing the loop” on a significant and relevant ecosystem monitoring problem while allowing domain experts (marine ecologists) to specify the mission at a relatively high level.

Simultaneous Tracking and Sampling of Dynamic Oceanographic Features with Autonomous Underwater Vehicles and Lagrangian Drifters

Studying ocean processes often requires observations made in a Lagrangian frame of reference, that is, a frame of reference moving with a feature of interest [1]. Often, the only way to understand a process is to acquire measurements at sufficient spatial and temporal resolution within a specific feature while it is evolving. Examples of coastal ocean features whose study requires Lagrangian observations include concentrated patches of microscopic algae (Fig. 1) that are toxic and may have impacts on fisheries, marine life and humans, or a patch of low-oxygen water that may cause marine life mortality depending on its movement and mixing.

Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned

With the advent of Autonomous Underwater Vehicles (AUVs) and other mobile platforms, marine robotics have had substantial impact on the oceanographic sciences. These systems have allowed scientists to collect data over temporal and spatial scales that would be logistically impossible or prohibitively expensive using traditional ship-based measurement techniques. Increased dependence of scientists on such robots has permeated scientific data gathering with future field campaigns involving these platforms as well as on entire infrastructure of people, processes and software, on shore and at sea. Recent field experiments carried out with a number of surface and underwater platforms give clues to how these technologies are coalescing and need to work together. We highlight one such confluence and describe a future trajectory of needs and desires for field experiments with autonomous marine robotic platforms. Our 2010 inter-disciplinary experiment in the Monterey Bay involved multiple platforms and collaborators with diverse science goals. One important goal was to enable situational awareness, planning and collaboration before, during and after this large-scale collaborative exercise. We present the overall view of the experiment and describe an important shore-side component, the Oceanographic Decision Support System (ODSS), its impact and future directions leveraging such technologies for field experiments.

Coordinating UAVs and AUVs for oceanographic field experiments: Challenges and lessons learned

Obtaining synoptic observations of dynamic ocean phenomena such as fronts, eddies, oxygen minimum zones and blooms has been challenging primarily due to the large spatial scales involved. Traditional methods of observation with manned ships are expensive and, unless the vessel can survey at high-speed, unrealistic. Autonomous underwater vehicles (AUVs) are robotic platforms that have been making steady gains in sampling capabilities and impacting oceanographic observations especially in coastal areas. However, their reach is still limited by operating constraints related to their energy sources. Unmanned aerial vehicles (UAVs) recently introduced in coastal and polar oceanographic experiments have added to the mix in observation strategy and methods. They offer a tantalizing opportunity to bridge such scales in operational oceanography by coordinating with AUVs in the water-column to get in-situ measurements. In this paper, we articulate the principal challenges in operating UAVs with AUVs making synoptic observations for such targeted water-column sampling. We do so in the context of autonomous control and operation for networked robotics and describe novel experiments while articulating the key challenges and lessons learned.

Preliminary Results for Model-Based Adaptive Control of an Autonomous Underwater Vehicle

We discuss a novel autonomous system which integrates onboard deliberation with execution and probabilistic state estimation for an adaptive Autonomous Underwater Vehicle for deep sea exploration. The work is motivated by the need to have AUVs be goal-directed, perceptive, adaptive and robust in the context of dynamic and uncertain conditions. The challenges leading to deployment required dealing with modeling uncertainty and integrating control loops at different levels of abstraction and response for a dynamic environment. The system is general-purpose and adaptable to other ocean going and terrestrial platforms.

Integrating autonomous underwater vessels, surface vessels and aircraft as persistent surveillance components of ocean observing studies

Global initiatives are underway to establish Ocean Observing Systems (OOS) that can provide society better information on ocean conditions. These observatories include moorings, drifters, floats, and buoyancy gliders. Although gliders have long operational endurance, their reliance on batteries limits sensors payloads, thus some OOS also include autonomous underwater vehicles (AUVs) with active propulsion. In some observatories AUVs can recharge their batteries at underwater docking stations connected to shore by cables. However AUVs can also be recharged from autonomous surface vessels (ASVs) such as the WaveGlider, whose propulsion is provided by wave action, and payload power supplied by solar panels. In addition to this function, as components of OOS, ASVs can collect data and act as communication nodes for data from bottom moorings, gliders and AUVs. Unmanned air vehicle systems (UAS) may perform the same role. The problem of networking these heterogeneous systems is discussed along with tools and technologies for adaptive ocean sampling. A vision is outlined to build a portable mobile observatory for OOS which can be deployed anywhere, anytime, that relies on a mix of human-in-the-loop and fully autonomous computational technology.