Evolving Trends in Marine Robotics and the Role of Artificial Intelligence Techniques
Kanna Rajan
Monterey Bay Aquarium Research Institute Moss Landing, California, United States Introduction
The worlds oceans have recently been the focus of substantial coverage and public interest as reflected in media coverage of the ongoing climate change debate. While it is clear that climactic conditions have changed and still changing, the arguments have moved from a position of considerable skepticism to one of attempting to understand the actual rate of change so mitigation strategies can ostensibly be put in place. Usually these strategies rely on sparse measurements across vast spatial and temporal scales, with the resulting hypothesis that address macro-scale changes, but often miss out those at finer levels.
It is also clear that the scientific community is coming to the realization that cost and persistent measurements are no longer to be ignored in the face of tighter fiscal constraints and competitive pressures on funding resources (Rudnick and Perry, 2003). What is left out, is the constant and continued discussion needed on methods to observe the global oceans robustly and continuously in the harshest conditions and across large stretches of the water column. Traditional ship-based methods with humans-in-the-loop while still important, are clearly not scalable towards the needs to tackle the complexity of large-scale environmental monitoring. Robotic vehicles are one answer to augment such observation strategies. Robotic platforms can make measurements in rough conditions, provide continuous surveys, can be reconfigured in hardware or software relatively quickly and as such have become a handy extension of the human senses in sampling the oceans (Bellingham and Rajan, 2007).
Yet significant challenges persist. Marine robots are still specialized hardware and require trained operators for their control in the water often for pre-scripted surveys in a well defined area of study. Interpretation of their data is still a manual task; yet archiving, calibrating and visualizing the data continues to be an onerous human task. More importantly, comparing and contrasting data sets which is critical to understanding climatic change over time, is laborious. The importance of human-in-the-loop will likely continue; yet there is a difference between the human being in the robots control-loop (for navigation and control) versus being in charge of the overall observational strategy which is at a higher level of abstraction. While costs of each individual vehicle are still marginally high in dissuading organizations from deploying large numbers (tens or more), the ongoing hardware and software revolution driven by community driven Open Source collaborations is making a significant dent in pricing. Put together, we are in a unique moment in time for the ocean sciences when low(er) cost platforms can (and should) be brought to bear in understanding the global climate. The case will be made in this chapter, that hardware advances have left software tools and methods in robotic platform command and control as a key challenge that need to be addressed; one that can harness the ongoing frenetic pace of change in the technology often driven by commercial markets. The advent of the smartphone market in particular, has driven down the price and accessibility of sensors, lowered
the cost of production and made for a more egalitarian approach in Robotics to what was, barely a few years ago, available only in elite research labs.
The principal challenge, it will be argued here, is then how to harness the hardware and low-level of abstraction in command and control, to deploy, operate and sustain robots for ocean observation, whether they be underwater, on the surface or in the air.
This is precisely the challenge that the Artificial Intelligence (AI) community has been attempting to address for more than four decades. While AI is a conglomeration of techniques, the most relevant towards the key goal of ocean
exploration, observation and monitoring are, we believe, Automated Planning and Execution, Machine Learning, Autonomous Agents and Diagnosis. A larger set of methods within AI tied to ocean observing is shown in Fig 1. Lack of space inhibits detailed discussion of all for this chapter. Together, these methods form the critical backbone on which substantial capabilities can be brought to bear to provide ocean science with the much needed tools they need to impact our collective understanding of the changing climate.
Relevant Methods in AI
Artificial Intelligence (Russell and Norvig, 2003) is a collection of computational methods that center around decision-making. Simply said, the core of the discipline revolves around search; that is to make choices that move the computation (and potentially a robot within which it is embedded) towards a goal. To an external entity, this often results in a perception of a robotic agent possessing ‘intelligence’. While AI has often been in the public imagination and associated with robotic platforms, including those in space (Rajan et.al 2000 & M. Ai-Chang et.al 2004), the field’s impact on real world problems especially in robotics has, till recently, been relegated to interesting laboratory methods that do not scale to real world environments. This is changing, with sophisticated top-down abstractions in AI meeting the bottom-up methods coming from Robotics. In the following, we offer some insight in the techniques mentioned above and how they are likely to change the science of observing our ocean; note that, our focus is exclusively towards upper water column measurements:
• Automated Planning and Execution: Planning (Ghallab, Nau and Traverso, 2004) is the projection of the state of a robot in space and time, to achieve a set of goals starting. Planning is necessary to balance future mission goals with current opportunities a robot has with resources available at its disposal. Planning is also involved in rebalancing (or rejecting) activities, when appropriate, in using consumables such as onboard energy and data. Planning tasks associated with a single robot based on the environmental context requires that plan execution occurs so as to provide feedback on how the world (or the internal state and external environment associated with the robot) has evolved in response to the robots previous actions. This virtuous sense-plan-act cycle defines the transition of the robot from Figure 1: Some of the methods within the field of Artificial Intelligence that will likely have an impact on ocean observation.
an initial state to the expected goal(s) it is trying to satisfy. In the context of planning, the system then has to choose between possible alternatives (e.g. to achieve a proscribed survey pattern, should the robot generate a ‘left’ or ‘right’ turn? Should it take a water sample now given information derived from its sensors? etc). Without Planning, these activities have to be scripted in advance, and in doing so the operator has to make assumptions about what s/he expects to find in the water column at any point of time. Such an approach removes any element of pursuing opportunistic goals, whether those be science driven obtained from the sensor stream, or those associated with contingent events during vehicle operations. Automated Planning and execution ensures that the system can be responsive to such events recover from off-nominal events and re-plan the robots activities given exogenous environmental conditions or endogenous failures. Current methodologies in scripted operations are not an option that is neither sustainable nor viable for the vision of persistent robotic operations in the 21st century.
• Multi-vehicle Operation and Control: In large spatial (and temporal) scales, a single robot is akin to a single set of measurements much like what traditional oceanographic methods have been using. Multiple robots in the water column performing complementary and coordinated activities, as an ensemble, is the most viable approach to study macro (and micro)-scale processes related to our changing environment. While control of a single robot is well understood and has been well studied, coordinating multiple robots to observe oceanographic phenomenon, however, is a challenge. Coordination strategies being studied vary from tightly coupled formations (J. Soares et.al 2013), to loosely formed coalitions; yet significant gaps remain due to tightly bound assumptions of observability, control, communications and the operational environment. Abstraction in control becomes even more complex when multiple vehicle deployments are to take place, especially when the robotic elements are heterogeneous and display diversity in operational capacity and sensor payload. The need for human-in-the-loop control (J. Das et.al 2011) comes down to keeping the entire ensemble of vehicles from performing a goal level task, rather than focus on low-level detail of sensor readings or navigational control. Multi-vehicle (and distributed) plan synthesis and execution is therefore a necessity.
• Diagnosis: Failure is an integral part of operating a robot in any environment; it is critical still in the context of marine robotics where vehicles are often not observable and not trivial to recover for debugging. Typically this means marine robots in encountering any form of (exogenous or endogenous) failure are expected to come to the surface where they can be recovered, retrieved and debugged. This might be appropriate in the event of serious failure where the vehicle goes into a fail-safe mode. Yet more often, recovery is inherently possible with continued operation, should the vehicle be configured for fail-operational operation, when alternative control strategies can be executed by the onboard controller to diagnose, isolate and recover from failure. For instance it is possible that a noisy sensor may be the cause of a ‘stuck right’ signal rather than the actual control surface being jammed. In such a case, online diagnosis (e.g. using multiple sensory cues) can formulate a work-around in-situ to isolate the sensor and recover from the failure condition and continue executing the operational plan. Diagnosis is often considered a form of automated Planning; however the methods and representations necessary for diagnosis are substantially different from planning at the more abstract mission or task level as well as its lack of proximity to the actual vehicle
hardware. Reasoning in Diagnosis, has to be rapid (in milliseconds or less) and recovery methods adequate for vehicular safety. Diagnosis when intertwined with abstract automated Planning then, allows a vehicle to reformulate actions in the light of unanticipated events such as failures or opportunistic science. And in doing so, it allows for the vehicle to be persistent in the water column while engaging in different tasks and dealing with unanticipated situations.
• Machine Learning (ML): With increasing rates of deployment of sensors, whether on mobile or immobile robotic platforms, has come the deluge of data. Sifting through the data to understand recurring patterns in space and time is something computational methods are substantially better at than most humans, even if understanding the implication of the derived information continues to require human cognition. Statistical patterns can be gleaned by computation, but its interpretation is, to date, something only humans can deal with. Time series measurements are ideal candidates for demonstrating this capability, as also water column properties. Typically this means that correlations are found across different variables such as temperature, salinity, nitrate, dissolved oxygen with the actual property being studied. Current ML techniques can look for specific target patterns; yet it a far more powerful field, one that can be applied towards ecological transport, climate variability, genomics and the general field of Adaptive Sampling (i.e. where and when should a robot sample a potentially variable field such as the water column). Such macro-scale studies can be targeted towards large scale datasets already in possession of various laboratories.
Challenges and Future Opportunities
With the methods highlighted above, immediate near term benefits can be realizable in coordinated upper water-column observations using symbolic AI methods, with heterogeneous robots as shown in Fig. 2. A range of applications from observing ocean fronts, anoxic zones, algal blooms, plumes (anthropomorphic or natural) are viable. The figure shows aerial unmanned autonomous vehicles (UAVs), powered autonomous underwater vehicles (AUVs), unpowered ocean gliders and autonomous surface vehicles (ASVs), which are simultaneously making measurements in the meso-scale (less than 50 Sq. Km). While AUVs and ASVs have been routinely deployed for scientific experiments, UAVs have only recently come onto the scene. In doing so, they have provided a much needed boost for synoptic views (Hovstein et.al 2014) in the upper water-column. With their higher speeds, relatively simple launch and recovery methods and ability to carry a range of small payloads, UAVs hold promise in changing how large swaths of the ocean are observed systematically and cost-effectively.
The backend of this heterogeneous robotic ensemble will require a systematically engineered decision-support system that can obtain the raw data from commercially available sensors on these platforms, to process, archive and analyze the data co-temporally with the incoming stream. The system on shore must also be in a position to run synthetic ocean models and provide projective means for experiment planning and control allowing shore side oceanographers to work from anywhere when connected to the Internet (Gomes et.al, 2013).
While aspects of AI involving onboard decision-making on robotic vehicles and on shore have been demonstrated (Rajan and Py, 2012, Pinto et.al 2012, Rajan and Py, 2013,), the ongoing task is to integrate these approaches that can enable seamless communication from shore to the robotic ensemble and back, in ways that sampling and control for shore-based scientists is viable. Associated with this are three principal challenges related to:
1. integrating ocean models with experiment planning and control
2. coordinated and distributed control of the entire ensemble of heterogeneous robots
3. synthesis of the data stream from different sensors on different platforms returned over a ‘thin’ data pipe in a communication constrained environment at sea
So what will the next 50 years in the 21st century look like when aided by tools, techniques and processes brought over from AI for marine robotics? Looking into a ‘crystal ball’ we believe the following outcomes will be possible for AI-aided data driven exploration:
• Platform diagnosis will be tightly coupled with automated plan execution, which can schedule and deliberate about consumable as well as replenish-able resources resulting in a fully functional autonomous platform that can reconfigure itself around off-nominal situations while dealing with scientific opportunism. This will allow scientists to deploy vehicles (underwater, surface and aerial) and decidedly provide high-level objectives with little to no supervision, expecting these robots to persist in the water-column, the air, the Figure 2. Heterogeneous multi-vehicle operations in the water column communicating over multiple communication streams to a shore-side decision-support system
surface and the air/sea interface. Inferential capabilities of these robots will be such that they will be able to decide what data would likely be of immediate interest to scientists on shore, and push that data out alerting them of a possible event-response scenario unfolding in near real-time.
• With the tight integration of AI Planning with Diagnosis, vehicles could then persist in the water column for days and weeks and perhaps more. While battery technology could have progressed to provide high(er) energy density than what is available now, it is likely that in coastal regions, inexpensive underwater docking stations connected to shore will be used to provide energy needs as well as data download capability. ASVs will increasingly be able to tow such docking stations and AUVs will be able to charge while moving slowly in the water-column.
• Machine Learning tools on shore will reshape how we derive information from raw data. Vast stores of data can already be assimilated (Bernstein et.al 2013) to provide scientists with deep causal knowledge linking cause and effect for phenomenon at least at a level currently not available. This would allow both fine scale modeling of ecological transport (why organisms populate certain stretches of the ocean and why and when they migrate in the water-column) and also predictive methods for bulk phenomenon such as blooms and plumes. Such methods could also provide partial event-response possibilities taking into account atmospheric and oceanographic conditions along with data from instrumented sensors to make oceanographers aware of coastal conditions. Such a capability could be of potential interest to retarget assets for more ground truth-ed analysis using ships and other robotic platforms.
• ML attempts to do online learning in robotic vehicles in the water-column will likely be mixed, given less than stellar conditions of environmental sensor noise, highly non-linear nature of currents in the water-column and no matter how many sensors and platforms, inadequacy of near real-time data. Such a capability will require AUVs to become akin to ‘sniffer dogs’ that can dynamically detect and chase evolving conditions in the water, based on uncertain a-priori knowledge in models of features of interest. Dynamic vehicle planning, routing and path following will likely allow a vehicle to return to an approximate location to resume surveys interrupted by opportunistic science as determined by the robot, with no human guidance. With online ML, vehicles will couple their diagnostic/self-healing capability to learning patterns on the fly, to be able to concerted judgments on when to adaptively sample.
• UAVs, AUVs and ASVs will be able to loosely coordinate their sampling and observation in the upper water-column to find, tag and track (and sample) dynamic events such as fronts and the air/sea interface (M. Faria et.al, 2014). Biological estimates of primary productivity can then be made by sampling flux across this air/sea boundary using sensitive sensors for outgassed elements. Such networked robotic system of systems while well engineered, will still prone to failure given the harsh operating conditions. Such observations will likely be possible in the Polar Regions, albeit not as robustly and persistently as in calmer, protected waters closer to shore. The southern ocean for example, will continue to be a harsh and
unforgiving environment for such systems to be deployed on a routine basis, even if short-duration experimentation are possible.
None of these challenges are major impediments to advancing the state of the practice of ocean observation using such robotic tools. More than likely, they are near-term goals of most major groups in the field to target and deploy for scientific experimentation. One prime reason for such a ‘modest’ prediction in innovation in techniques for ocean observing is the direct correlation to the modest investment in Marine Robotics now and likely in the near future, as a field. Scientists need to be provided incentives to reach out to develop novel platforms integrated with novel sensors using computational methods outlined here. Correspondingly, the robotics community which has faced substantial barriers-to-entry into the oceanographic domain (infrastructure) and also found it convenient to work in other domains (notably terrestrial) to achieve short-term (and stove-piped) academic goals necessary for career advancement, needs to look longer term to make the environment an important factor in their research. So this look at the crystal ball is predicated with the assumption that some modest changes in how academic and scientific research will be substantially more “inter” and “cross” disciplinary than it is now. The hope is that the reality and acknowledgement of change related to ongoing near term weather across the globe, might spark some of the push necessary to make different communities to work and share ideas and innovations together.
Conclusions and impact to Portugal
The Portuguese Republic is in a unique position, one would argue, to cut across existing trends and ‘punch above its weight’ to provide a much needed fillip to ocean science and engineering. Here is why:
• It already is at the forefront of Marine Robotics research with strong academic groups working in various aspects of environmental monitoring.
• It has a cognizant and supporting administration and policy makers who understand the implication of the nation’s seaward facing geography, its access to mid-Atlantic islands in the Azores and Madeira and the rich yet fragile ecosystems associated with a number of Marine Protected Areas within their waters.
• The ongoing technical revolution in the field of robotics has engaged and retained a critical mass of young researchers across the nation to push the state of the practice. This is a valuable and critical need to sustained research growth in the field. In other words, the nation has the critical intellectual mass to make an inroad in the fields of AI and Robotics.
• The Portuguese understand their rich legacy of ocean exploration coming from the Age of Discoveries. That legacy and the nations geography collude in ensuring that the oceans will continue to dominate its collective cultural and existential ethos.
Together, when tapped effectively Portugal can and must continue to be at the center of innovation that will address the critical needs of the global community and its changing climate.
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