Kanna Rajan

AIJ Journal 2017 Journal Article

Towards a science of integrated AI and Robotics

• Kanna Rajan
• Alessandro Saffiotti

The early promise of the impact of machine intelligence did not involve the partitioning of the nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded intelligence as being conjoined between perception, reasoning and actuation. Yet over the years the fields of AI and Robotics drifted apart. Practitioners of AI focused on problems and algorithms abstracted from the real world. Roboticists, generally with a background in mechanical and electrical engineering, concentrated on sensori-motor functions. That divergence is slowly being bridged with the maturity of both fields and with the growing interest in autonomous systems. This special issue brings together the state of the art and practice of the emergent field of integrated AI and Robotics, and highlights the key areas along which this current evolution of machine intelligence is heading.

IROS Conference 2015 Conference Paper

On mixed-initiative planning and control for Autonomous underwater vehicles

• Lukás Chrpa
• José Pinto 0001
• Manuel A. Ribeiro
• Frederic Py
• João B. Sousa
• Kanna Rajan

Supervision and control of Autonomous underwater vehicles (AUVs) has traditionally been focused on an operator determining a priori the sequence of waypoints of a single vehicle for a mission. As AUVs become more ubiquitous as a scientific tool, we envision the need for controlling multiple vehicles which would impose less cognitive burden on the operator with a more abstract form of human-in-the-loop control. Such mixed-initiative methods in goal-oriented commanding are new for the oceanographic domain and we describe the motivations and preliminary experiments with multiple vehicles operating simultaneously in the water, using a shore-based automated planner.

ICRA Conference 2014 Conference Paper

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

• Margarida Faria
• José Pinto 0001
• Frederic Py
• João Fortuna
• Hugo Dias
• Ricardo Martins
• Frederik Leira
• Tor Arne Johansen

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.

ICRA Conference 2013 Conference Paper

Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena

• Jnaneshwar Das
• Julio B. J. Harvey
• Frederic Py
• Harshvardhan Vathsangam
• Rishi Graham
• Kanna Rajan
• Gaurav S. Sukhatme

Marine phenomena such as algal blooms can be detected using in situ measurements onboard autonomous underwater vehicles (AUVs), but understanding plankton ecology and community structure requires retrieval and analysis of water specimens. This process requires shipboard or manual sample collection, followed by onshore lab analysis which is time-consuming. Better understanding of the relationship between the observable environmental features and organism abundance would allow more precisely targeted sampling and thereby save time. In this work, we present an approach to learn and improve models that predict this relationship. Coupled with recent advances in AUV technology allowing selective retrieval of water samples, this constitutes a new paradigm in biological sampling. We use organism abundance models along with spatial models of environmental features learned immediately after AUV deployments to compute spatial distributions of organisms in the coastal ocean purely from in situ AUV data. We use Gaussian process regression along with the unscented transform to fuse the two models, obtaining both the mean and variance of the organism abundance estimates. The uncertainty in organism abundance predictions is used in a sampling strategy to selectively acquire new water specimens that improves the organism abundance models. Simulation results are presented demonstrating the advantage of performing hierarchical probabilistic regression. After the validation through simulation, we show predictions of organism abundance from models learned on lab-analyzed water sample data, and AUV survey data.

IROS Conference 2013 Conference Paper

Learning-based event response for marine robotics

• Matthew Bernstein
• Rishi Graham
• Danelle Cline
• John M. Dolan
• Kanna Rajan

Robotic vehicles have become a critical tool for studying the under-sampled coastal ocean. This has led to new paradigms in scientific discovery. The combination of agility, reactivity, and persistent presence makes autonomous robots ideal for targeted sampling of elusive, episodic events such as algal blooms. In order to achieve this goal, they need to be deployed at the right place and time. To that end, we have designed and will soon deploy a shore-based event recognition technology to continuously monitor remote sensing imagery for algal blooms as targets for robotic field experiments. A Support Vector Machine underlies a field-tested decision support system which scientists will consult prior to deploying robots in the coastal ocean. Our aim is to target oceanographic field experiments for evaluation and verification.

ICRA Conference 2012 Conference Paper

An experimental momentum-based front detection method for autonomous underwater vehicles

• Jeremy Gottlieb
• Rishi Graham
• Thom Maughan
• Frederic Py
• Gabriel Elkaim
• Kanna Rajan

Fronts have been recognized as hotspots of intense biological activity and are important targets for observation to understand coastal ecology and transport in a changing ocean. With high spatial and temporal variability, detection and event response for frontal zones is challenging for robotic platforms like autonomous underwater vehicles (AUVs). These vehicles have shown their versatility and cost-effectiveness in using automated approaches to detect a range of features. Targeting them for in-situ observation and sampling capabilities for frontal zones then provides an important tool for characterizing rapid and episodic changes. We introduce a novel momentum-based front detection (MBFD) algorithm which utilizes a Kalman filter and a momentum accumulator function to identify significant temperature gradients associated with upwelling fronts. MBFD is designed to work at a number of levels including onboard an AUV, on-shore with a sparse real-time data stream and post-experiment on a full resolution data set gathered by a vehicle. Such a multi-layered approach plays an important role in mixed human-robot decision making for oceanographers making coordinated sampling and asset allocation strategies in large multi-robot field experiments in the coastal ocean.

IROS Conference 2011 Conference Paper

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

• Jnaneshwar Das
• Thom Maughan
• Mike McCann
• Mike Godin
• Tom O'Reilly
• Monique Messie
• Fred Bahr
• Kevin Gomes

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.

AAMAS Conference 2010 Conference Paper

A Systematic Agent Framework for Situated Autonomous Systems

• Frederic Py
• Kanna Rajan
• Conor McGann

We present a formal framework of an autonomous agent asa collection of coordinated control loops, with a recurringsense, plan, act cycle. Our framework manages the information flow within the partitioned structure to ensure consistency in order to direct the flow of goals and observations ina timely manner. The resulting control structure improvesscalability since many details of each controller can be encapsulated within a single control loop. This partitioned agentdesign promises a domain-independent, scalable and robustapproach for control of real-world autonomous robots operating in dynamic environments. We validate our frameworkwith experimental results from deployments in two differentreal-world domains.

ICRA Conference 2010 Conference Paper

Towards marine bloom trajectory prediction for AUV mission planning

• Jnaneshwar Das
• Kanna Rajan
• Sergey Frolov
• Frederic Py
• John P. Ryan 0001
• David A. Caron
• Gaurav S. Sukhatme

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.

ICRA Conference 2008 Conference Paper

A deliberative architecture for AUV control

• Conor McGann
• Frederic Py
• Kanna Rajan
• Hans Thomas
• Richard Henthorn
• Robert S. McEwen

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.

AAAI Conference 2008 Conference Paper

Adaptive Control for Autonomous Underwater Vehicles

• Conor McGann
• Kanna Rajan

We describe a novel integration of Planning with Probabilistic State Estimation and Execution. The resulting system is a unified representational and computational framework based on declarative models and constraintbased temporal plans. The work is motivated by the need to explore the oceans more cost-effectively through the use of Autonomous Underwater Vehicles (AUV), requiring them to be goal-directed, perceptive, adaptive and robust in the context of dynamic and uncertain conditions. The novelty of our approach is in integrating deliberation and reaction over different temporal and functional scopes within a single model, and in breaking new ground in oceanography by allowing for precise sampling within a feature of interest using an autonomous robot. The system is general-purpose and adaptable to other ocean going and terrestrial platforms.

ICAPS Conference 2005 Conference Paper

Activity Planning for the Mars Exploration Rovers

• John L. Bresina
• Ari K. Jónsson
• Paul H. Morris
• Kanna Rajan

Operating the Mars Exploration Rovers is a challenging, time-pressured task. Each day, the operations team must generate a new plan describing the rover activities for the next day. These plans must abide by resource limitations, safety rules, and temporal constraints. The objective is to achieve as much science as possible, choosing from a set of observation requests that oversubscribe rover resources. In order to accomplish this objective, given the short amount of planning time available, the MAPGEN (Mixed-initiative Activity Plan GENerator) system was made a mission-critical part of the ground operations system. MAPGEN is a mixed-initiative system that employs automated constraint-based planning, scheduling, and temporal reasoning to assist operations staff in generating the daily activity plans. This paper describes the adaptation of constraint-based planning and temporal reasoning to a mixed-initiative setting and the key technical solutions developed for the mission deployment of MAPGEN.

IS Journal 2004 Journal Article

MAPGEN: mixed-initiative planning and scheduling for the Mars Exploration Rover mission

• M. Ai-Chang
• J. Yglesias
• B.G. Chafin
• W.C. Dias
• P.F. Maldague
• J. Bresina
• L. Charest
• A. Chase

The Mars Exploration Rover mission is one of NASA's most ambitious science missions to date. Launched in the summer of 2003, each rover carries instruments for conducting remote and in site observations to elucidate the planet's past climate, water activity, and habitability. Science is MER's primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (mixed initiative activity plan generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool from the Jet Propulsion Laboratory and the Europa planning and scheduling system from NASA Ames Research Center. We discuss the issues arising from combining these tools in this mission's context. MAPGEN is the first AI-based system to control a space platform on another planet's surface.

ICAPS Conference 2000 Conference Paper

Planning in Interplanetary Space: Theory and Practice

• Ari K. Jónsson
• Paul H. Morris
• Nicola Muscettola
• Kanna Rajan
• Benjamin D. Smith

On May 17th 1999, NASA activated for the first time an AI-based planner/scheduler running on the flight processor of a spacecraft. This was part of the Remote Agent Experiment (RAX), a demonstration of closedloop planning and execution, and model-based state inference and failure recovery. This paper describes the RAX Planner/Scheduler (RAX-PS), both in terms of the underlying planning framework and in terms of the fielded planner. RAX-PS plans are networks of constraints, built incrementally by consulting a model of the dynamics of the spacecraft. The RAX-PS planning procedure is formally well defined and can be proved to be complete. RAX-PS generates plans that are temporally flexible, allowing the execution system to adjust to actual plan execution conditions without breaking the plan. The practical aspect, developing a mission critical application, required paying attention to important engineering issues such as the design of methods for programmable search control, knowledge acquisition and planner validation. The result was a system capable of building concurrent plans with over a hundred tasks within the performance requirements of operational, mission-critical software.

ECAI Conference 2000 Conference Paper

Remote Agent: An Autonomous Control System for the New Millennium

• Kanna Rajan
• Douglas E. Bernard
• Gregory Dorais
• Edward B. Gamble
• Bob Kanefsky
• James Kurien
• William Millar
• Nicola Muscettola