Thomas Wettergren | University of Rhode Island (original) (raw)
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Papers by Thomas Wettergren
arXiv (Cornell University), Sep 9, 2019
Proceedings of SPIE, Apr 27, 2007
IEEE transactions on cybernetics, Jun 1, 2018
Applied Mathematics and Computation, Dec 1, 2023
Institution of Engineering and Technology eBooks, Jul 8, 2020
We describe an approach for accomplishing the high-level mission planning required for a heteroge... more We describe an approach for accomplishing the high-level mission planning required for a heterogeneous team of autonomous vehicles performing surveys in multiple areas, such as required for mine countermeasure (MCM) missions. The high-level mission scheduling and waterspace management require sequencing the order and location of lower-level tasks to be completed by each vehicle in the heterogeneous team: unmanned surface vessels (USVs) and unmanned underwater vehicles (UUVs). In this context, the USVs serve as transport vehicles while the UUVs perform the actual surveys and execute any other local actions. We develop a solution to this complex sequencing operation by leveraging unique information processing, communication, refueling, and planning windows that form constraints within the system within a formal scheduling optimization framework. We use mixed-integer linear programming (MILP) as a solution method for the associated optimization problem. By using such a standard optimization approach, we both take advantage of optimality guarantees on the solution and extensive commercial numerical solvers. In addition to developing the numerical optimization problem, we also include methods to account for risks due to schedule slip for the individual tasks. We conclude with an example of joint USV/UUV planning using the optimization algorithm
arXiv (Cornell University), Sep 9, 2019
OCEANS 2022, Hampton Roads, Oct 17, 2022
ACM Transactions on Sensor Networks, Nov 25, 2020
OCEANS 2022, Hampton Roads
OCEANS 2022, Hampton Roads
OCEANS 2015 - MTS/IEEE Washington, 2015
This paper addresses search planning to find hidden objects in undersea environments where the se... more This paper addresses search planning to find hidden objects in undersea environments where the sensor detection process is subject to false alarms with a geographically varying likelihood. We develop a game-theoretic approach for maximizing the information flow that occurs as a multi-agent collaborative search is conducted over a bounded region. To accomplish this, we apply 1) a search channel formalism to the discrete search paradigm to formulate information measures as a function of searcher regional visitation and 2) a Receiver Operator Characteristic (ROC) analysis to map search strategies to ROC operating points which, in turn, define the properties of the search channel. This enables us to formulate a search game where the cost is driven by expended search effort and the payout is in terms of the information collected. We describe a new algorithmic capability for solving the search allocation problem of multiple unmanned undersea vehicles that can collaborate in their efforts to resolve false alarm outcomes. We discuss the properties of the information measure developed for a repeated look over the search channel and examine the impact of setting ROC operating points to their game equilibrium values. Doing so enables the regional visitation to be determined via a greedy scheduling solution. Results are presented to show the dominance of this approach over alternative ROC strategies and to demonstrate the capability to take advantage of known non-homogeneity in the search environment.
IEEE Transactions on Control of Network Systems, 2021
Future applications of autonomous systems promise to involve increasingly large numbers of collab... more Future applications of autonomous systems promise to involve increasingly large numbers of collaborative robots individually equipped with onboard sensors, actuators, and wireless communications. By sharing and coordinating information, plans, and decisions, these very-large-scale robotic (VLSR) networks can dramatically improve their performance and operate over long periods of time with little or no human intervention. Controlling many collaborative agents to this day presents significant technical challenges. Besides requiring satisfactory communications, the amount of computation associated with most coordinated control algorithms increases with the number of agents. It is well-known, for example, that the optimal control of N collaborative agents for path planning and obstacle avoidance is a PSPACE-hard problem. Also, while necessary for performing basic tasks such as localization and mapping, many sensing and estimation approaches suffer from the curse of dimensionality, and their performance may degrade as uncertainties from disparate sources propagate through the network.
OCEANS 2017 – Anchorage, 2017
This paper discusses the construction of search games as a planning method to optimize the effect... more This paper discusses the construction of search games as a planning method to optimize the effectiveness of UUV search agents. In our previous work, we applied a receiver operator characteristic (or ROC curve) to model the detection performance of search agents operating in adverse environments where false alarms are prevalent. The area search game was developed to maximize the information collected during the search operation. This was achieved for fixed search horizon planning cycles by jointly optimizing the distribution of search effort over the region and the setting of ROC operating points affecting the characteristics of the local search channels. In this paper, we discuss extensions to this game to address the determination of search horizon. We articulate the expanded strategy space that occurs in multi-agent problems where the fusion of information is explicitly included within the search paradigm. The information content of search outcomes are examined and an information ...
OCEANS 2017 – Anchorage, 2017
This paper develops a distributed sensor scheduling methodology that utilizes target classificati... more This paper develops a distributed sensor scheduling methodology that utilizes target classification decisions to govern the number of active sensors selected around the target in the deployment region. This approach utilizes a distributed supervisor on each sensor node to control the multi-modal operating state of the node. A distributed sensor selection method is proposed that dynamically adjusts the number of active sensor nodes based on the classification decision. The proposed method is simulated and validated to show that incorporating classification into the control loop significantly conserves energy reserves while still allowing for accurate target estimation.
Applied Mathematics and Computation, 2021
Abstract We consider a version of the N -player snowdrift game in which the payoffs obtained by t... more Abstract We consider a version of the N -player snowdrift game in which the payoffs obtained by the players are delayed. The delay in payoffs is shown to lead to a Hopf bifurcation with an associated critical value of the time delay in the replicator dynamics. For time delays larger than the critical time delay, the replicator dynamics oscillate around the equilibrium instead of asymptotically approaching it. The dependence of this critical time delay on the parameters of the game is determined. After developing the analysis for the 2-player game, the same methodology is applied to the N -player version of the game to show how the results change as a function of N , concluding with an analysis of the limiting case of large numbers of players.
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016
This paper addresses selected computational aspects of collaborative search planning when multipl... more This paper addresses selected computational aspects of collaborative search planning when multiple search agents seek to find hidden objects (i.e. mines) in operating environments where the detection process is prone to false alarms. A Receiver Operator Characteristic (ROC) analysis is applied to construct a Bayesian cost objective function that weighs and combines missed detection and false alarm probabilities. It is shown that for fixed ROC operating points and a validation criterion consisting of a prerequisite number of detection outcomes, an interval exists in the number of conducted search passes over which the risk objective function is supermodular. We show that this property is not retained beyond validation criterion boundaries. We investigate the use of greedy algorithms for distributing search effort and, in particular, examine the double greedy algorithm for its applicability under conditions of varying criteria. Numerical results are provided to demonstrate the effectiveness of the approach.
IEEE Transactions on Control of Network Systems, 2019
Global Oceans 2020: Singapore – U.S. Gulf Coast
arXiv (Cornell University), Sep 9, 2019
Proceedings of SPIE, Apr 27, 2007
IEEE transactions on cybernetics, Jun 1, 2018
Applied Mathematics and Computation, Dec 1, 2023
Institution of Engineering and Technology eBooks, Jul 8, 2020
We describe an approach for accomplishing the high-level mission planning required for a heteroge... more We describe an approach for accomplishing the high-level mission planning required for a heterogeneous team of autonomous vehicles performing surveys in multiple areas, such as required for mine countermeasure (MCM) missions. The high-level mission scheduling and waterspace management require sequencing the order and location of lower-level tasks to be completed by each vehicle in the heterogeneous team: unmanned surface vessels (USVs) and unmanned underwater vehicles (UUVs). In this context, the USVs serve as transport vehicles while the UUVs perform the actual surveys and execute any other local actions. We develop a solution to this complex sequencing operation by leveraging unique information processing, communication, refueling, and planning windows that form constraints within the system within a formal scheduling optimization framework. We use mixed-integer linear programming (MILP) as a solution method for the associated optimization problem. By using such a standard optimization approach, we both take advantage of optimality guarantees on the solution and extensive commercial numerical solvers. In addition to developing the numerical optimization problem, we also include methods to account for risks due to schedule slip for the individual tasks. We conclude with an example of joint USV/UUV planning using the optimization algorithm
arXiv (Cornell University), Sep 9, 2019
OCEANS 2022, Hampton Roads, Oct 17, 2022
ACM Transactions on Sensor Networks, Nov 25, 2020
OCEANS 2022, Hampton Roads
OCEANS 2022, Hampton Roads
OCEANS 2015 - MTS/IEEE Washington, 2015
This paper addresses search planning to find hidden objects in undersea environments where the se... more This paper addresses search planning to find hidden objects in undersea environments where the sensor detection process is subject to false alarms with a geographically varying likelihood. We develop a game-theoretic approach for maximizing the information flow that occurs as a multi-agent collaborative search is conducted over a bounded region. To accomplish this, we apply 1) a search channel formalism to the discrete search paradigm to formulate information measures as a function of searcher regional visitation and 2) a Receiver Operator Characteristic (ROC) analysis to map search strategies to ROC operating points which, in turn, define the properties of the search channel. This enables us to formulate a search game where the cost is driven by expended search effort and the payout is in terms of the information collected. We describe a new algorithmic capability for solving the search allocation problem of multiple unmanned undersea vehicles that can collaborate in their efforts to resolve false alarm outcomes. We discuss the properties of the information measure developed for a repeated look over the search channel and examine the impact of setting ROC operating points to their game equilibrium values. Doing so enables the regional visitation to be determined via a greedy scheduling solution. Results are presented to show the dominance of this approach over alternative ROC strategies and to demonstrate the capability to take advantage of known non-homogeneity in the search environment.
IEEE Transactions on Control of Network Systems, 2021
Future applications of autonomous systems promise to involve increasingly large numbers of collab... more Future applications of autonomous systems promise to involve increasingly large numbers of collaborative robots individually equipped with onboard sensors, actuators, and wireless communications. By sharing and coordinating information, plans, and decisions, these very-large-scale robotic (VLSR) networks can dramatically improve their performance and operate over long periods of time with little or no human intervention. Controlling many collaborative agents to this day presents significant technical challenges. Besides requiring satisfactory communications, the amount of computation associated with most coordinated control algorithms increases with the number of agents. It is well-known, for example, that the optimal control of N collaborative agents for path planning and obstacle avoidance is a PSPACE-hard problem. Also, while necessary for performing basic tasks such as localization and mapping, many sensing and estimation approaches suffer from the curse of dimensionality, and their performance may degrade as uncertainties from disparate sources propagate through the network.
OCEANS 2017 – Anchorage, 2017
This paper discusses the construction of search games as a planning method to optimize the effect... more This paper discusses the construction of search games as a planning method to optimize the effectiveness of UUV search agents. In our previous work, we applied a receiver operator characteristic (or ROC curve) to model the detection performance of search agents operating in adverse environments where false alarms are prevalent. The area search game was developed to maximize the information collected during the search operation. This was achieved for fixed search horizon planning cycles by jointly optimizing the distribution of search effort over the region and the setting of ROC operating points affecting the characteristics of the local search channels. In this paper, we discuss extensions to this game to address the determination of search horizon. We articulate the expanded strategy space that occurs in multi-agent problems where the fusion of information is explicitly included within the search paradigm. The information content of search outcomes are examined and an information ...
OCEANS 2017 – Anchorage, 2017
This paper develops a distributed sensor scheduling methodology that utilizes target classificati... more This paper develops a distributed sensor scheduling methodology that utilizes target classification decisions to govern the number of active sensors selected around the target in the deployment region. This approach utilizes a distributed supervisor on each sensor node to control the multi-modal operating state of the node. A distributed sensor selection method is proposed that dynamically adjusts the number of active sensor nodes based on the classification decision. The proposed method is simulated and validated to show that incorporating classification into the control loop significantly conserves energy reserves while still allowing for accurate target estimation.
Applied Mathematics and Computation, 2021
Abstract We consider a version of the N -player snowdrift game in which the payoffs obtained by t... more Abstract We consider a version of the N -player snowdrift game in which the payoffs obtained by the players are delayed. The delay in payoffs is shown to lead to a Hopf bifurcation with an associated critical value of the time delay in the replicator dynamics. For time delays larger than the critical time delay, the replicator dynamics oscillate around the equilibrium instead of asymptotically approaching it. The dependence of this critical time delay on the parameters of the game is determined. After developing the analysis for the 2-player game, the same methodology is applied to the N -player version of the game to show how the results change as a function of N , concluding with an analysis of the limiting case of large numbers of players.
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016
This paper addresses selected computational aspects of collaborative search planning when multipl... more This paper addresses selected computational aspects of collaborative search planning when multiple search agents seek to find hidden objects (i.e. mines) in operating environments where the detection process is prone to false alarms. A Receiver Operator Characteristic (ROC) analysis is applied to construct a Bayesian cost objective function that weighs and combines missed detection and false alarm probabilities. It is shown that for fixed ROC operating points and a validation criterion consisting of a prerequisite number of detection outcomes, an interval exists in the number of conducted search passes over which the risk objective function is supermodular. We show that this property is not retained beyond validation criterion boundaries. We investigate the use of greedy algorithms for distributing search effort and, in particular, examine the double greedy algorithm for its applicability under conditions of varying criteria. Numerical results are provided to demonstrate the effectiveness of the approach.
IEEE Transactions on Control of Network Systems, 2019
Global Oceans 2020: Singapore – U.S. Gulf Coast