Jnaneshwar Das

USC CINAPS Builds Bridges

More than 70% of our earth is covered by water, yet we have explored less than 5% of the aquatic environment. Aquatic robots, such as autonomous underwater vehicles (AUVs), and their supporting infrastructure play a major role in the collection of oceanographic data. To make new discoveries and improve our overall understanding of the ocean, scientists must make use of these platforms by implementing effective monitoring and sampling techniques to study ocean upwelling, tidal mixing, and other ocean processes. Effective observation and continual monitoring of a dynamic system as complex as the ocean cannot be done with one instrument in a fixed location. A more practical approach is to deploy a collection of static and mobile sensors, where the information gleaned from the acquired data is distributed across the network. Additionally, orchestrating a multisensor, long-term deployment with a high volume of distributed data involves a robust, rapid, and cost-effective communication network. Connecting all of these components, which form an aquatic robotic system, in synchronous operation can greatly assist the scientists in improving our overall understanding of the complex ocean environment.

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 marine bloom trajectory prediction for AUV mission planning

This paper presents an oceanographic toolchain that can be used to generate multi-vehicle robotic surveys for large-scale dynamic features in the coastal ocean. Our science application targets Harmful Algal Blooms (HABs) which have significant societal impact to coastal communities yet are poorly understood ecologically. Bloom patches can be large spatially (in kms) and unpredictable in their extent. To understand their ecology, we need to be able to bring back water samples from the ‘right’ places and times for lab analysis. In doing so, we target hotspots representative of intense biogeochemical activity for such sampling. Our approach uses remote sensing data to detect such hotspots using ocean color as a proxy, and advectively projects these patches spatio-temporally using surface current data from HF Radar stations. Experiments with satellite and Radar data sets are promising for large, coherent blooms. We show how these predictions can be used to select an appropriate sampling trajectory for an AUV.

An experimental study of station keeping on an underactuated ASV

Dynamic positioning is an important application for marine vehicles that do not have the luxury of anchoring or mooring themselves. Such vehicles are usually large and have arrays of thrusters that allow for controllability in the sway as well as the surge and yaw axes. Most smaller boats however, are underactuated and do not possess control in the sway direction. This makes the control problem significantly more challenging. We address the station keeping problem for a small autonomous surface vehicle (ASV) with significant windage. The vehicle is required to hold station at a given position. We describe the design of a weighted controller that uses wind feed-forward to complement a line-of-sight guidance controller to achieve satisfactory performance under slow-varying moderate wind conditions. We test the control system in simulation and in field trials with a twin-propeller ASV. Experiments show that the controller works very well in moderate wind conditions allowing the ASV to keep station with a position error of approximately one vehicle length.

Devices, systems, and methods for automated monitoring enabling precision agriculture

Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products - plant morphology, canopy volume, leaf area index, and fruit counts - using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves.

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.

Multi-Robot Collaboration with Range-Limited Communication: Experiments with Two Underactuated ASVs

We present a collaborative team of two under-actuated autonomous surface vessels (ASVs) that performs a cooperative navigation task while satisfying a communication constraint. Our approach is based on the use of a hierarchical control structure where a supervisory module commands each vessel to perform prioritized elementary tasks, a behavior-based controller generates motion directives to achieve the assigned tasks, and a maneuvering controller generates the actuator commands to follow the motion directives. The control technique has been tested in a mission where a set of target locations spread across a planar environment has to be visited once by either of the two ASVs while maintaining a relative separation less than a given maximum distance (to guarantee inter-ASV wireless communication). Experiments were carried out in the field with a team of two ASVs visiting 22 locations on a lake surface (approximately 30000m 2) with static obstacles. Results show a 30% improvement in mission time over the single-robot case.

An Online Utility-Based Approach for Sampling Dynamic Ocean Fields

The coastal ocean is a dynamic and complex environment due to the confluence of atmospheric, oceanographic, estuarine/riverine, and land-sea interactions. Yet it continues to be undersampled, resulting in poor understanding of dynamic, episodic, and complex phenomena such as harmful algal blooms, anoxic zones, coastal plumes, thin layers, and frontal zones. Often these phenomena have no viable biological or computational models that can provide guidance for sampling. Returning targeted water samples for analysis becomes critical for biologists to assimilate data for model synthesis. In our work, the scientific emphasis on building a species distribution model necessitates spatially distributed sample collection from within hotspots in a large volume of a dynamic field of interest. To do so, we propose an autonomous approach to sample acquisition based on an online calculation of sample utility. A series of reward functions provide a balance between temporal and spatial scales of oceanographic sampling and do so in such a way that science preferences or evolving knowledge about the feature of interest can be incorporated in the decision process. This utility calculation is undertaken onboard a powered autonomous underwater vehicle (AUV) with specialized water samplers for the upper water column. For validation, we provide experimental results using archival AUV data along with an at-sea demonstration in Monterey Bay, CA.