Michael R. Benjamin

Nested autonomy for unmanned marine vehicles with MOOS-IvP

This document describes the MOOS-IvP autonomy software for unmanned marine vehicles and its use in large-scale ocean sensing systems. MOOS-IvP is composed of two open-source software projects funded by the Office of Naval Research. MOOS provides a core autonomy middleware capability, and the MOOS project additionally provides a set of ubiquitous infrastructure utilities. The IvP Helm is the primary component of an additional set of capabilities implemented to form a full marine autonomy suite known as MOOS-IvP. This software and architecture are platform and mission agnostic and allow for a scalable nesting of unmanned vehicle nodes to form large-scale, long-endurance ocean sensing systems composed of heterogeneous platform types with varying degrees of communications connectivity, bandwidth, and latency.

Navigation of unmanned marine vehicles in accordance with the rules of the road

This paper is concerned with the in-field autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the coast guard collision regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture the flexibility exploited by humans, this work applies a novel method of multi-objective optimization, interval programming, in a behavior-based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the first in-field demonstration of multiobjective optimization applied to autonomous COLREGS-based marine vehicle navigation

A method for protocol-based collision avoidance between autonomous marine surface craft

This paper is concerned with the in-field autonomous operation of unmanned marine vehicles in accordance with convention for safe and proper collision avoidance as prescribed by the Coast Guard Collision Regulations (COLREGS). These rules are written to train and guide safe human operation of marine vehicles and are heavily dependent on human common sense in determining rule applicability as well as rule execution, especially when multiple rules apply simultaneously. To capture, the flexibility exploited by humans, this work applies a novel method of multiobjective optimization, interval programming, in a behavior-based control framework for representing the navigation rules, as well as task behaviors, in a way that achieves simultaneous optimal satisfaction. We present experimental validation of this approach using multiple autonomous surface craft. This work represents the first in-field demonstration of multiobjective optimization applied to autonomous COLREGS-based marine vehicle navigation.

Autonomous Underwater Vehicles: Trends and Transformations

Three examples of inter-agency cooperation utilizing current generation, individual Autonomous Underwater Vehicles (AUVs) are described consistent with recent recommendations of the U.S. Commission on Ocean Policy. The first steps in transforming individual AUVs into adaptive, networked systems are underway. To realize an affordable and deployable system, a network-class AUV must be designed with cost–size constraints not necessarily applied in developing solo AUVs. Vehicle types are suggested based on function and ocean operating regime: surface layer, interior and bottom layer. Implications for platform, navigation and control subsystems are explored and practical formulations for autonomy and intelligence are postulated for comparing performance and judging behavior. Laws and conventions governing intelligent maritime navigation are reviewed and an autonomous controller with conventional collision avoidance behavior is described. Network-class cost constraints can be achieved through economies of scale. Productivity and efficiency in AUV manufacturing will increase if constructive competition is maintained. Constructive strategies include interface and operating standards. Professional societies and industry trade groups have a leadership role to play in establishing public, open standards. cations are described at many conferences and in an expanding literature of journal publications. Griffiths (2003) provides recent developments in AUV design, construction and operation. A number of commercial manufacturers have emerged to supply the growing market. Clearly, individual AUVs are evolving into useful tools that extend current measurement methods. Three examples involving current generation, individual AUVs will serve to illustrate trends in inter-agency cooperation utilizing this technology. Following these examples, we examine factors that will transform current measurement methods. Network externalities associated with interagency cooperation will play a role in driving this transformation. Recently the Navy joined with the U. S. Environmental Protection Agency, the Narragansett Bay Estuary Program, and the Autonomous Undersea Systems Institute to demonstrate the effectiveness of using a Solar Powered Autonomous Underwater Vehicle (SAUV II) to measure dissolved oxgyen concentrations in Greenwich Bay, Rhode Island. Utilization of an AUV to rapidly move con

Adaptive Control of Heterogeneous Marine Sensor Platforms in an Autonomous Sensor Network

This paper describes an investigation into the control of autonomous mobile sensor platforms in a marine sensor network used to provide monitoring of transitory phenomenon over a wide area. A distributed network of small, inexpensive vehicles with heterogeneous sensors allows us to build a robust monitoring network capable of real-time response to rapidly changing sensor data. The major objective of this paper is to describe a framework for adaptive and cooperative control of the autonomous sensor platforms in such a network. This framework has two major components, a sensor that provides high-level state information to a behavior-based autonomous vehicle control system and a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allow reactive control in complex environments with multiple constraints. Experimental results are presented for a 2-D target tracking application using a network of autonomous surface craft in which one platform with a simulated bearing sensor tracks a moving target and relays the target state information to a second vehicle that is moving in a classification mode. From these results, it is readily seen that there is the potential for potent synergy from the cooperation of multiple sensor platforms which can each view an event of interest from a different vantage point

Autonomous Control of an Autonomous Underwater Vehicle Towing a Vector Sensor Array

This paper is about the autonomous control of an autonomous underwater vehicle (AUV), and the particular considerations required to allow proper control while towing a 100-meter vector sensor array. Mission related objectives are tempered by the need to consider the effect of a sequence of maneuvers on the motion of the towed array which is thought not to tolerate sharp bends or twists in sensitive material. We describe and motivate an architecture for autonomy structured on the behavior-based control model augmented with a novel approach for performing behavior coordination using multi-objective optimization. We provide detailed in-field experimental results from recent exercises with two 21-inch AUVs in Monterey Bay California.

Multi-objective optimization of sensor quality with efficient marine vehicle task execution

This paper describes the in-field operation of two interacting autonomous marine vehicles to demonstrate the suitability of interval programming (IvP), a novel mathematical model for multiple-objective optimization. Broadly speaking, IvP coordinates competing control needs such as primary task execution that depends on a sufficient position estimate, and vehicle maneuvers that will improve that position estimate. In this work, vehicles cooperate to improve their position estimates using a sequence of vehicle-to-vehicle range estimates from acoustic modems. Coordinating primary task execution and sensor quality maintenance is a ubiquitous problem, especially in underwater marine vehicles. This work represents the first use of multiobjective optimization in a behavior-based architecture to address this problem

Autonomous cooperation of heterogeneous platforms for sea-based search tasks

Many current methods of search using autonomous marine vehicles do not adapt to changes in mission objectives or the environment. A cellular-decomposition-based framework for cooperative, adaptive search is proposed that allows multiple search platforms to adapt to changes in both mission objectives and environmental parameters. Software modules for the autonomy framework MOOS-IvP are described that implement this framework. Simulated and experimental results show that it is feasible to combine both pre-planned and adaptive behaviors to effectively search a target area.

Behavior Based Adaptive Control for Autonomous Oceanographic Sampling

This paper describes an investigation into the adaptive control of autonomous mobile sensor platforms for providing oceanographic sampling. Mobile sensor platforms provide an ability to rapidly sample oceanographic data of interest for real-time input into ocean environmental models with the goal of reducing the modeling uncertainty by introducing selected sampled data. The major objective of this paper is to describe the autonomy architecture developed to support adaptive sampling. This architecture consists of an open-source distributed autonomy architecture and an approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints. Experimental results are provided for an adaptive ocean thermal gradient tracking application performed by an autonomous surface craft in Monterey Bay. These results highlight not only the suitability of autonomous sensor platforms for providing adaptive sampling of the ocean environment but, also, the suitability of our behavior-based autonomy approach and distributed autonomy architecture for providing a simple, flexible, and scalable method for autonomous sensor platform control. The paper concludes with an overview of future adaptive sampling experiments planned with autonomous underwater sensor platforms using the same methodology.

Cooperative Target Tracking in a Distributed Autonomous Sensor Network

This paper describes an investigation into the control of multiple, cooperating autonomous sensor platforms operating in a marine sensor network. Distributed sensors allow us to view phenomena of interest from multiple, simultaneous vantage points, creating significant processing gain from the spatial diversity. The major objective of this paper is to describe a framework for adaptive and cooperative control of the autonomous sensor platforms in such a network. This framework has two major components, an intelligent sensor that provides high-level state information to a behavior-based autonomous vehicle control system and a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints. Experimental results are presented for a 2-D target tracking application in which a pair of fully autonomous surface craft using simulated bearing sensors acquire and track a moving target. From these results, it is readily seen that there is the potential for potent synergy from the cooperation of multiple sensor platforms