Cooperative Vehicle Environmental Monitoring

Abstract

Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

Collaborative Automation and IoT Technologies for Coastal Ocean Observing Systems

“…The Balearic Islands Coastal Ocean Observing and Forecasting System (SOCIB, Tintoré et al, 2013) provides operational solutions for a multi-platform, integrated and multidisciplinary observing system which is able to leverage system's diversity and automation to better respond to end-user needs (Heslop et al, 2019). To achieve integration in such heterogenous system communication and coordination among nodes is paramount (Leonard, 2016;Thompson and Guihen, 2019) and semi-autonomous planning and monitoring tools should evolve to integrate numerical ocean and atmospheric models as well as in situ data in order to coordinate and optimize usage of the individual nodes. Underlying all of these attributes should be the adoption of standard methods and best practices to create a foundation for the desired interoperability (Pearlman et al, 2019).…”

“…In these cases, decision-making and m2m communication focus on enforcing a control system that governs each participating platform. Ocean survey missions have particularly benefited from cooperative control methods resulting in solutions that are effective for monitoring over extended periods (Leonard, 2016; Ocean Infinity, 2020 1 ; Simetti et al, 2020). -Collaborative solutions focus on complex missions that have a "deep" sequence of dependent and interdependent tasks.…”