Author name cluster

Michael R. Benjamin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

13 papers

1 author row

IROS Conference 2025 Conference Paper

ASV-Aided AUV Navigation: A Field Study on Nonlinear Estimation for Localization of Low-Cost, Scalable Systems

Raymond Turrisi
Daniel A Duecker
John Morrison
Fabian Steinmetz
Michael R. Benjamin

This work investigates the use of multiple Autonomous Surface Vehicles (ASVs) as Communication/Navigation Aids (CNAs) to enhance the navigation and state estimation of an Autonomous Underwater Vehicle (AUV). Our approach builds on recent advancements in low-cost sensors and platforms, which enable novel AUV applications across fundamental science, commercial industries, and defense. We consider six different combinations of Kalman Filter and Factor Graph localization solutions on three datasets, covering 53 minutes and 3. 1 kilometers of operation. We first present the solution using the measurements from all three ASVs, before occluding measurements from two of the ASVs to assess the effect of reduced observability on localization performance.

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IROS Conference 2025 Conference Paper

Decentralized Declustering of Multiple Underactuated Autonomous Surface Vehicles: Managing Robot Swarms in the Field

Filip Traasdahl Strømstad
Michael R. Benjamin

The task of deploying a large number of autonomous vehicles is challenging, risky and often overlooked in the literature. These vehicles are typically deployed from a single location, and their underactuated nature, close proximity, and susceptibility to external disturbances make it difficult to achieve a mission-ready configuration without collisions. In this paper, we address the problem of transitioning a set of underactuated Autonomous Surface Vehicles (ASVs) from arbitrary and inconvenient initial conditions, to a deconflicted set of deployed vehicles. We propose a decentralized and scalable method that assigns the vehicles to their target positions, generates optimal paths given minimum turning radii and assures collision avoidance between the vehicles. Performance is verified through simulation and extensive field trials. Results demonstrate that our approach improves the time to decluster with 58% compared to the current manual method. By improving efficiency and robustness while eliminating human involvement, this work streamlines ASV fleet deployments, enabling more effective multi-agent field operations.

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ICRA Conference 2025 Conference Paper

Hybrid State Estimation and Mode Identification of an Amphibious Robot

Herman B. Amundsen
Supun Randeni
Russell C. Bingham
Carles Civit
B. Pietro Filardo
Martin Føre
Eleni Kelasidi
Michael R. Benjamin

C-Ray is an amphibious robot that is capable of swimming in water and crawling on land using its undulating fins, enabling operations in a wide range of environments. The robot can be modeled as a hybrid dynamical system whose dynamics and propulsion change when the robot transitions between water and land. Most importantly, the direction of wave travel in the robot's fins is reversed between its swimming and crawling locomotion styles. To operate autonomously, C-Ray requires both accurate identification of when transitions between water and land occur and robust state estimation in littoral environments where the transition dynamics are highly discontinuous and transient. This paper presents a hybrid observer for estimating continuous states and identifying state-driven mode switches for C-Ray, enabling autonomous water/land-transitions. The proposed observer is a combination of the multiplicative extended Kalman filter (MEKF) and the salted Kalman filter, a newly proposed Kalman filter for mapping state uncertainty during hybrid transitions. We also propose an altitude and sea floor geometry observer and incorporate this directly into the MEKF. The performance is evaluated in simulations.

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ICRA Conference 2024 Conference Paper

A Model for Multi-Agent Autonomy That Uses Opinion Dynamics and Multi-Objective Behavior Optimization

Tyler M. Paine
Michael R. Benjamin

This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretical results, we show it is possible to design the behavior of the MRS by the selection of a relatively small set of parameters. The resulting behavior - both collective actions and individual actions - can be understood intuitively. The approach is entirely decentralized and the communication cost scales by the number of group options, not agents. We demonstrated the effectiveness of this approach using a hypothetical ‘explore-exploit-migrate’ scenario in a two hour field demonstration with eight unmanned surface vessels (USVs). The results from our preliminary field experiment show the collective behavior is robust even with time-varying network topology and agent dropouts.

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IROS Conference 2024 Conference Paper

Decentralized Linear Convoying for Underactuated Surface Craft with Partial State Coupling

Raymond Turrisi
Michael R. Benjamin

This work introduces a novel decentralized algorithm and control law for stable linear convoying using a layered control approach. This algorithm was implemented in MOOS-IvP, using an abstraction layer that models a virtual system decoupled from the system’s actual dynamics. This makes the algorithm platform-agnostic and able to be combined with other behaviors such as collision avoidance and operating region behaviors. A trajectory defined by a lead agent is discretized and embedded with the leader’s dynamics and propagated to all following agents. We first demonstrate that this approach when paired with a simple PD controller prevents accumulated errors and improves trajectory tracking for follower agents. Thereafter we demonstrate how virtually coupling a subset of agent states improves the overall cohesiveness of the convoy. Improvements are demonstrated in both simulations and field trials using five autonomous surface vehicles.

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ICRA Conference 2024 Conference Paper

Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances

Nicholas Rober
Karan Mahesh
Tyler M. Paine
Max L. Greene
Steven Lee
Sildomar T. Monteiro
Michael R. Benjamin
Jonathan P. How

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach’s ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures.

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IROS Conference 2023 Conference Paper

An Ensemble of Online Estimation Methods for One Degree-of-Freedom Models of Unmanned Surface Vehicles: Applied Theory and Preliminary Field Results with Eight Vehicles

Tyler M. Paine
Michael R. Benjamin

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing.

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IROS Conference 2023 Conference Paper

Perseus AUV: Towards Linear Convoying of Agile A-Sized AUVs Through Acoustic Track-and-Trail

Nicholas R. Rypkema
Supun Randeni
Michael Sacarny
Michael R. Benjamin
Michael S. Triantafyllou

We present the Perseus autonomous underwater vehicle (AUV) - an A-sized a a A-size stands for the standard sonobuoy [2] form factor, with a maximum diameter of 124 mm and a length of around 0. 9 m, ensuring the ability to launch from standard sonobuoy launchers onboard a wide array of fixed wing and rotary wing air crafts, surface ships and submarines [3] micro AUV, outfitted with a low-cost passive inverted ultra-short baseline (piUSBL) acoustic reception system, which allows it to acoustically track-and-trail a leader vehicle that carries an acoustic transmission source. With a long-term goal of linear convoying of multiple A-sized AUVs, in this work, we used an unmanned surface vehicle (USV) towing an acoustic source, as a proxy for a lead AUV, demonstrating that the Perseus AUV is able to successfully track-and-trail a leader vehicle. The AUV was also outfitted with a tuna-inspired morphing fin mechanism that allowed the vehicle to achieve good directional stability as well as good maneuverability; properties that are useful for linear convoying AUVs, but are presently difficult to achieve because they impose contradictory requirements. We demonstrated this system with real-world, in-water experiments in the Charles river, Massachusetts, USA.

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ICRA Conference 2007 Conference Paper

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

Michael R. Benjamin
David Battle
Donald P. Eickstedt
Henrik Schmidt
Arjuna P. Balasuriya

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.

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ICRA Conference 2007 Conference Paper

Behavior Based Adaptive Control for Autonomous Oceanographic Sampling

Donald P. Eickstedt
Michael R. Benjamin
Ding Wang
Joseph A. Curcio
Henrik Schmidt

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.

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IROS Conference 2006 Conference Paper

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

Donald P. Eickstedt
Michael R. Benjamin
Henrik Schmidt
John J. Leonard

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

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ICRA Conference 2006 Conference Paper

Multi-objective Optimization of Sensor Quality with Efficient Marine Vehicle Task Execution

Michael R. Benjamin
Matthew Grund
Paul Newman 0001

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

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ICRA Conference 2006 Conference Paper

Navigation of Unmanned Marine Vehicles in Accordance with the Rules of the Road

Michael R. Benjamin
Joseph A. Curcio
John J. Leonard
Paul Newman 0001

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

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