@article{ref:Hindes2021,
author = {Jason Hindes, Victoria Edwards, M. Ani Hsieh, Ira B. Schwartz},
title = {Critical transition for colliding swarms},
year = {2021},
eprint = {2101.01577},
archivePrefix={arXiv},
primaryClass={nlin.PS}
}
@article{ref:Schwartz2020,
author = {Schwartz, Ira B and Edwards, Victoria and Kamimoto, Sayomi and Kasraie, Klimka and Ani Hsieh, M and Triandaf, Ioana and Hindes, Jason },
title = {Torus bifurcations of large-scale swarms having range dependent communication delay},
journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
volume = {30},
number = {5},
pages = {051106},
year = {2020},
doi = {10.1063/5.0006540},
URL = {https://doi.org/10.1063/5.0006540},
eprint = {https://doi.org/10.1063/5.0006540}
}
@article{ref:Edwards2020,
author = {Edwards, Victoria and deZonia, Philip and Hsieh, M Ani and Hindes, Jason and Triandaf, Ioana and Schwartz, Ira B},
title = {Delay induced swarm pattern bifurcations in mixed reality experiments},
journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
volume = {30},
number = {7},
pages = {073126},
year = {2020},
doi = {10.1063/1.5142849},
URL = {https://doi.org/10.1063/1.5142849},
eprint = {https://doi.org/10.1063/1.5142849}
}
@article{ref:Kularatne2019,
author = {Kularatne,Dhanushka and Forgoston,Eric and Hsieh,M. Ani },
title = {Using control to shape stochastic escape and switching dynamics},
journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
volume = {29},
number = {5},
pages = {053128},
year = {2019},
doi = {10.1063/1.5090113},
URL = { https://doi.org/10.1063/1.5090113},
eprint = { https://doi.org/10.1063/1.5090113}
}
@article{ref:Digani2018,
author = {V. Digani and M. A. Hsieh and L. Sabattini and C. Secchi},
title = {Coordination of multiple AGVs: a Quadratic Optimization Method},
journal = {Autonomous Robots: Special Issue on Distributed Robots: From Fundamentals to
Applications},
year = {2017},
number = {},
pages = {},
month = {},
note = {Under Review},
volume = {},
}
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@article{ref:Kularatne2018b,
author = {D. Kularatne and S. Bhattacharya and M. A. Hsieh},
title = {Going With The Flow: A Graph Based Approach to Optimal
Path Planning in General Flows},
journal = {Autonomous Robots: RSS 2016 Special Issue},
year = {2017},
number = {},
pages = {},
month = {},
note = {Under Review},
volume = {},
}
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@article{ref:Kennedy2018,
author = {M. Kennedy III and D. Thakur and M. A. Hsieh and S. Bhattacharya and V. Kumar},
title = {Optimal Paths for Polygonal Robots in SE(2)},
journal = {Journal of Mechanisms and Robotics},
year = {2017},
number = {},
pages = {},
month = {},
note = {Accepted for Publication},
volume = {},
}
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@ARTICLE{ref:Kularatne2018a,
author={D. Kularatne and S. Bhattacharya and M. A. Hsieh},
journal={IEEE Robotics and Automation Letters},
title={Optimal Path Planning in Time-Varying Flows Using Adaptive Discretization},
year={2018},
volume={3},
number={1},
pages={458-465},
keywords={graph theory;marine vehicles;mobile robots;optimal control;optimisation;path planning;search problems;time-varying systems;2D time-varying flows;AMV;Regional Ocean Model System;adaptive discretization scheme;analytical time-varying flow model;autonomous marine vehicles;energy budgets;fixed discretization schemes;graph-search-based method;motion plans;optimal control formulation;optimal energy paths;optimal path planning;search graph;time-varying ocean flow data;Cost function;Drag;Level set;Marine vehicles;Oceans;Path planning;Vehicle dynamics;Field robots;graph-based planning;marine robotics;motion and path planning;optimization in time-varying flows},
doi={10.1109/LRA.2017.2761939},
ISSN={},
month={Jan},
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@Article{ref:Kularatne2017,
author=”Kularatne, Dhanushka and Hsieh, M. Ani”,
title=”Tracking attracting manifolds in flows”,
journal=”Autonomous Robots”,
year=”2017″,
month=”Dec”,
day=”01″,
volume=”41″,
number=”8″,
pages=”1575–1588″,
abstract=”This paper presents a collaborative control strategy designed to enable a team of robots to track attracting Lagrangian coherent structures (LCS) and unstable manifolds in two-dimensional flows. Tracking LCS in flows is important for many applications such as planning energy optimal paths in the ocean and for predicting the evolution of various physical and biological processes in the ocean. The proposed strategy which tracks attracting LCS and unstable manifolds in real-time through direct computation of the local finite time Lyapunov exponent field, does not require global information about the dynamics of the surrounding flow, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented on a team of robots and theoretical guarantees for the tracking and formation keeping strategies are presented. We demonstrate the performance of the tracking strategy in simulation using actual ocean flow data and experimental flow data generated in a tank. The strategy is validated experimentally using a team of micro autonomous surface vehicles in an actual fluid environment.”,
issn=”1573-7527″,
doi=”10.1007/s10514-017-9628-y”,
url=”https://doi.org/10.1007/s10514-017-9628-y”
}
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@Article{ref:Hajieghrary2017,
AUTHOR = {Hajieghrary, Hadi and Mox, Daniel and Hsieh, M. Ani},
TITLE = {Information Theoretic Source Seeking Strategies for Multiagent Plume Tracking in Turbulent Fields},
JOURNAL = {Journal of Marine Science and Engineering},
VOLUME = {5},
YEAR = {2017},
NUMBER = {1},
ARTICLE NUMBER = {3},
URL = {http://www.mdpi.com/2077-1312/5/1/3},
ISSN = {2077-1312},
ABSTRACT = {We present information theoretic search strategies for single and multi-robot teams to localize the source of biochemical contaminants in turbulent flows. The robots synthesize the information provided by sporadic and intermittent sensor readings to optimize their exploration strategy. By leveraging the spatio-temporal sensing capabilities of a mobile sensing network, our strategies result in control actions that maximize the information gained by the team while minimizing the time spent localizing the biochemical source. By leveraging the team’s ability to obtain simultaneous measurements at different locations, we show how a multi-robot team is able to speed up the search process resulting in a collaborative information theoretic search strategy. We validate our proposed strategies in both simulations and experiments.},
DOI = {10.3390/jmse5010003}
}
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@ARTICLE{ref:Prorok2017,
author={A. Prorok and M. A. Hsieh and V. Kumar},
journal={IEEE Transactions on Robotics},
title={The Impact of Diversity on Optimal Control Policies for Heterogeneous Robot Swarms},
year={2017},
volume={33},
number={2},
pages={346-358},
keywords={decentralised control;distributed control;multi-robot systems;optimal control;optimisation;decentralized controller;decentralized methods;distribution problem;heterogeneous robot swarms;optimal control policies;real-time optimization;robot distribution;robot species;swarm heterogeneity;task topology;transition rates;Measurement;Optimization;Real-time systems;Resource management;Robot sensing systems;Switches;Heterogeneous multirobot systems;stochastic systems;swarm robotics;task allocation},
doi={10.1109/TRO.2016.2631593},
ISSN={1552-3098},
month={April},
}
Back
@article{HAJIEGHRARY2016b,
title = “Multi-agent search for source localization in a turbulent medium”,
journal = “Physics Letters A”,
volume = “380”,
number = “20”,
pages = “1698 – 1705”,
year = “2016”,
issn = “0375-9601”,
doi = “https://doi.org/10.1016/j.physleta.2016.03.013”,
url = “http://www.sciencedirect.com/science/article/pii/S0375960116002425”,
author = “Hadi Hajieghrary and M. Ani Hsieh and Ira B. Schwartz”,
keywords = “Multi-agent systems, Information theory, Distributed control”
}
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@article{ref:Szwaykowska2016,
title = {Collective motion patterns of swarms with delay coupling: Theory and experiment},
author = {Szwaykowska, Klementyna and Schwartz, Ira B. and Mier-y-Teran Romero, Luis and Heckman, Christoffer R. and Mox, Dan and Hsieh, M. Ani},
journal = {Phys. Rev. E},
volume = {93},
issue = {3},
pages = {032307},
numpages = {11},
year = {2016},
month = {Mar},
publisher = {American Physical Society},
doi = {10.1103/PhysRevE.93.032307},
url = {https://link.aps.org/doi/10.1103/PhysRevE.93.032307}
}
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@article{ref:Hajieghrary2016a,
title = {Dynamic Adaptive Robust Backstepping Control Design for an Uncertain Linear System},
author = {Hajieghrary H, Ani Hsieh M.},
journal = {ASME. J. Dyn. Sys., Meas., Control},
volume = {138},
issue = {7},
pages = {071004-071004-8},
numpages = {},
year = {2016},
month = {},
publisher = {},
doi = {10.1115/1.4033019},
url = {}
}
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@article{ref:Prorok2016,
title = {Adaptive Distribution of a Swarm of Heterogeneous Robots},
author = {A. Prorok and M. A. Hsieh, and V. Kumar},
journal = {Acta Polytechnica},
volume = {56},
issue = {1},
pages = {},
numpages = {},
year = {2016},
month = {},
publisher = {},
doi = {doi.org/10.14311/APP.2016.56.0067},
url = {https://ojs.cvut.cz/ojs/index.php/ap/article/view/3438}
}
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@article{ref:Edwards2016,
title = {Segregation of heterogeneous robotics swarms via convex optimization},
author = {Edwards, Victoria and Rezeck, Paulo and Chaimowicz, Luiz and Hsieh, M Ani},
journal = {Dynamic Systems and Control Conference},
volume = {50695},
issue = {},
pages = {V001T03A001},
numpages = {},
year = {2016},
month = {},
organization = {American Society of Mechanical Engineers},
doi = {10.1115/DSCC2016-9653},
url = {https://doi.org/10.1115/DSCC2016-9653}
}
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@article{ref:Heckman2015,
author = {Christoffer R. Heckman and Ira B. Schwartz and M. Ani Hsieh},
title ={Toward efficient navigation in uncertain gyre-like flows},
journal = {The International Journal of Robotics Research},
volume = {34},
number = {13},
pages = {1590-1603},
year = {2015},
doi = {10.1177/0278364915585396},
URL = {https://doi.org/10.1177/0278364915585396},
eprint = {https://doi.org/10.1177/0278364915585396},
abstract = { We present the development and experimental validation of an autonomous surface/underwater vehicle control strategy that leverages the environmental dynamics and uncertainty to navigate in a stochastic fluidic environment. We assume that the workspace is composed of the union of a collection of disjoint regions, each bounded by Lagrangian coherent structures (LCSs). LCSs are dynamical features in the flow field that behave like invariant manifolds in general time-invariant dynamical systems and delineate the boundaries of attraction basins. We analyze a passive particle’s noise-induced transition between adjacent LCS-bounded regions and show how most probable escape trajectories with respect to the transition probability between adjacent LCS-bounded regions can be determined. Additionally, we show how the likelihood of transition can be controlled through minimal actuation. The result is an energy efficient navigation strategy that leverages the inherent dynamics of the surrounding flow field for mobile sensors operating in a noisy fluidic environment. We experimentally validate the proposed vehicle control strategy and analyze its theoretical properties. Our results show that the single vehicle control parameter exhibits a predictable exponential scaling with respect to the escape times and is effective even in situations where the structure of the flow is not fully known and control effort is costly. }
}
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@article{ref:Heckman2014,
title = {Going With the Flow: Enhancing Stochastic Switching Rates in Multigyre Systems},
author = {Christoffer R. Heckman and M. Ani Hsieh and Ira B. Schwartz},
journal = {ASME. J. Dyn. Sys., Meas., Control},
volume = {137},
issue = {3},
pages = {031006-031006-6},
numpages = {},
year = {2014},
month = {},
publisher = {},
doi = {10.1115/1.4027828},
url = {}
}
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@ARTICLE{ref:Michini2014,
author={M. Michini and M. A. Hsieh and E. Forgoston and I. B. Schwartz},
journal={IEEE Transactions on Robotics},
title={Robotic Tracking of Coherent Structures in Flows},
year={2014},
volume={30},
number={3},
pages={593-603},
keywords={autonomous underwater vehicles;hydrodynamics;robot dynamics;LCSs;Lagrangian coherent structures;collaborative robotic control strategy;collaborative tracking strategy;dynamical systems;flow coherent structures;flow tank;general dynamical systems;general time-dependent systems;ocean data;ocean flows;oceanography;robotic tracking;separatrices;stable manifolds;static flow manifolds;time-dependent model;unstable manifolds;weather prediction;wind-driven double-gyre flow;Manifolds;Oceans;Robot sensing systems;Vectors;Velocity measurement;Distributed robot systems;marine robotics;networked robots},
doi={10.1109/TRO.2013.2295655},
ISSN={1552-3098},
month={June},}
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@article{ref:Mather2014,
title={Synthesis and analysis of distributed ensemble control strategies for allocation to multiple tasks},
volume={32},
DOI={10.1017/S0263574713000994},
number={2},
journal={Robotica},
publisher={Cambridge University Press},
author={Mather, T. William and Hsieh, M. Ani},
year={2014},
pages={177–192},
}
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@article{ref:Worcester2014,
author = {James Worcester and M. Ani Hsieh and Rolf Lakaemper},
title ={Distributed assembly with online workload balancing and visual error detection and correction},
journal = {The International Journal of Robotics Research},
volume = {33},
number = {4},
pages = {534-546},
year = {2014},
doi = {10.1177/0278364913509125},
URL = {https://doi.org/10.1177/0278364913509125},
eprint = {https://doi.org/10.1177/0278364913509125},
abstract = { We consider the assembly of a three-dimensional (3D) structure by a team of heterogeneous robots capable of online sensing and error correction during the assembly process. We build on our previous work and address the partitioning of the assembly task to maximize parallelization of the assembly process. Specifically, we consider 3D structures that can be assembled from a fixed collection of heterogeneous tiles that vary in shapes and sizes. Given a desired 3D structure, we first compute the partition of the assembly strategy into Na subcomponents that can be executed in parallel by a team of Na assembly robots. The assembly robots then perform online workload balancing during construction to minimize assembly time. To enable online error detection and correction during the assembly process, mobile robots equipped with visual depth sensors are tasked to scan, identify, and track the state of the structure. The result is a cooperative assembly framework where assembly robots can balance their individual workloads online by trading assembly components while scanning robots detect and reassign missing assembly components online. We present the integration of the planning, sensing, and control strategies employed in our framework and report on the experimental validation of the strategy using our multi-robot testbed. },
}
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@article{ref:Mallory2013,
author = {K. Mallory and M. A. Hsieh and E. Forgoston and I. B. Schwartz},
title ={Distributed Allocation of Mobile Sensing Swarms in Gyre Flows},
journal = {Nonlin. Processes Geophys.},
volume = {20},
number = {5},
pages = {657-668},
year = {2013},
doi = {10.1177/0278364913509125},
URL = {},
eprint = {},
abstract = {},
}
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@article{ref:Mather2011,
author = {T William Mather and M. Ani Hsieh},
title ={Macroscopic modeling of stochastic deployment policies with time delays for robot ensembles},
journal = {The International Journal of Robotics Research},
volume = {30},
number = {5},
pages = {590-600},
year = {2011},
doi = {10.1177/0278364911401442},
URL = {https://doi.org/10.1177/0278364911401442},
eprint = {https://doi.org/10.1177/0278364911401442},
abstract = { We consider the dynamic assignment and reassignment of a homogeneous robot ensemble to multiple spatially located tasks with deterministic or near-deterministic task execution times. Similar to Halasz et al. and Berman et al., we consider the development of agent-level, i.e. microscopic, stochastic control policies through the analysis of an appropriate macroscopic analytical model that describes the dynamics of the ensemble. Specifically, we present an approach to better approximate the effects of deterministic microscopic time delays at the macroscopic level based on Padé approximants. We present, analyze, and compare the frequency response of our approach to that presented by Berman et al. using different agent-based simulations. },
}
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@ARTICLE{ref:Berman2009,
author={S. Berman and A. Halasz and M. A. Hsieh and V. Kumar},
journal={IEEE Transactions on Robotics},
title={Optimized Stochastic Policies for Task Allocation in Swarms of Robots},
year={2009},
volume={25},
number={4},
pages={927-937},
keywords={Markov processes;distributed control;multi-robot systems;optimisation;stochastic systems;Markov processes;decentralized strategy;distributed control;homogeneous swarm robots;optimization problem;optimized stochastic policies;stochastic systems;task allocation;Distributed control;Markov processes;optimization;stochastic systems;swarm robotics;task allocation},
doi={10.1109/TRO.2009.2024997},
ISSN={1552-3098},
month={Aug},}
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@Article{ref:Derenick2009,
author=”Derenick, Jason
and Spletzer, John
and Hsieh, Ani”,
title=”An Optimal Approach to Collaborative Target Tracking with Performance Guarantees”,
journal=”Journal of Intelligent and Robotic Systems”,
year=”2009″,
month=”Sep”,
day=”01″,
volume=”56″,
number=”1″,
pages=”47–67″,
abstract=”In this paper, we present a discrete-time optimization framework for target tracking with multi-agent systems. The “target tracking” problem is formulated as a generic semidefinite program (SDP) that when paired with an appropriate objective yields an optimal robot configuration over a given time step. The framework affords impressive performance guarantees to include full target coverage (i.e. each target is tracked by at least a single team member) as well as maintenance of network connectivity across the formation. Key to this work is the result from spectral graph theory that states the second-smallest eigenvalue—$\lambda$ 2—of a weighted graph’s Laplacian (i.e. its inter-connectivity matrix) is a measure of connectivity for the associated graph. Our approach allows us to articulate agent-target coverage and inter-agent communication constraints as linear-matrix inequalities (LMIs). Additionally, we present two key extensions to the framework by considering alternate tracking problem formulations. The first allows us to guarantee k-coverage of targets, where each target is tracked by k or more agents. In the second, we consider a relaxed formulation for the case when network connectivity constraints are superfluous. The problem is modeled as a second-order cone program (SOCP) that can be solved significantly more efficiently than its SDP counterpart—making it suitable for large-scale teams (e.g. 100’s of nodes in real-time). Methods for enforcing inter-agent proximity constraints for collision avoidance are also presented as well as simulation results for multi-agent systems tracking mobile targets in both ℝ2 and ℝ3.”,
issn=”1573-0409″,
doi=”10.1007/s10846-008-9302-x”,
url=”https://doi.org/10.1007/s10846-008-9302-x”
}
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@Article{ref:Hsieh2008c,
author=”Hsieh, M. Ani
and Hal{\’a}sz, {\’A}d{\’a}m
and Berman, Spring
and Kumar, Vijay”,
title=”Biologically inspired redistribution of a swarm of robots among multiple sites”,
journal=”Swarm Intelligence”,
year=”2008″,
month=”Dec”,
day=”01″,
volume=”2″,
number=”2″,
pages=”121–141″,
abstract=”We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al., in Proceedings of the International Conference on Intelligent Robots and Systems (IROS’07), pp. 2320–2325, 2007). We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.”,
issn=”1935-3820″,
doi=”10.1007/s11721-008-0019-z”,
url=”https://doi.org/10.1007/s11721-008-0019-z”
}
Back
@article{ref:Hsieh2008b,
title={Decentralized controllers for shape generation with robotic swarms},
volume={26},
DOI={10.1017/S0263574708004323},
number={5},
journal={Robotica},
publisher={Cambridge University Press},
author={Hsieh, M. Ani and Kumar, Vijay and Chaimowicz, Luiz},
year={2008},
pages={691–701}}
Back
@article{ref:Hsieh2008a,
author = {Hsieh, M. Ani and Cowley, Anthony and Kumar, Vijay and Taylor, Camillo J.},
title = {Maintaining Network Connectivity and Performance in Robot Teams: Research Articles},
journal = {J. Field Robot.},
issue_date = {January 2008},
volume = {25},
number = {1-2},
month = jan,
year = {2008},
issn = {1556-4959},
pages = {111–131},
numpages = {21},
url = {http://dx.doi.org/10.1002/rob.v25:1/2},
doi = {10.1002/rob.v25:1/2},
acmid = {1331307},
publisher = {John Wiley and Sons Ltd.},
address = {Chichester, UK},
}
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@article {ref:Hsieh2007,
author = {Hsieh, M. Ani and Cowley, Anthony and Keller, James F. and Chaimowicz, Luiz and Grocholsky, Ben and Kumar, Vijay and Taylor, Camillo J. and Endo, Yoichiro and Arkin, Ronald C. and Jung, Boyoon and Wolf, Denis F. and Sukhatme, Gaurav S. and MacKenzie, Douglas C.},
title = {Adaptive teams of autonomous aerial and ground robots for situational awareness},
journal = {Journal of Field Robotics},
volume = {24},
number = {11-12},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
issn = {1556-4967},
url = {http://dx.doi.org/10.1002/rob.20222},
doi = {10.1002/rob.20222},
pages = {991–1014},
year = {2007},
}
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@inproceedings{CongDARS2018,
author = {Wei, Cong and Yu, Xi and Tanner, Herbert and Ani Hsieh, M},
title = {Synchronous Rendezvous for Networks of Active Drifters in Gyre Flows},
booktitle = {Proc. of the International Symposium on Distributed Autonomous Robotic Systems (DARS2018)},
year = {2018},
month = {Oct},
address = {Boulder, CO USA},
pages = {},
}
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@INPROCEEDINGS{LiuSSRR2018,
author={H. Liu and M. A. Hsieh},
booktitle={2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},
title={Neural Network Aided Information Theoretic Exploration},
year={2018},
volume={},
number={},
pages={1-7},
keywords={information theory;learning (artificial intelligence);mobile robots;neural nets;neurocontrollers;path planning;topological features;exploration frontier;information-theoretic exploration strategy;candidate frontier locations;topological knowledge;metric information;topological information;robot-only mapping;fully-connected neural network;robot-only exploration;learning;neural network offline training;Task analysis;Robot sensing systems;Mutual information;Feature extraction;Space exploration;Neural networks},
doi={10.1109/SSRR.2018.8468650},
ISSN={2475-8426},
month={Aug},}
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@inproceedings{KularatneRSS2018,
author = {Kularatne, Dhanushka and Forgoston, Eric and Ani Hsieh, M},
title = {Exploiting Stochasticity for the Control of Transitions in Gyre Flows},
booktitle = {Proc. of Robotics: Science and Systems},
year = {2018},
month = {06},
pages = {},
doi = {10.15607/RSS.2018.XIV.060}
}
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@Inbook{MoxDARS2018,
author=”Mox, Daniel and Cowley, Anthony and Hsieh, M. Ani and Taylor, C. J.”,
editor=”Gro{\ss}, Roderich and Kolling, Andreas and Berman, Spring and Frazzoli, Emilio
and Martinoli, Alcherio and Matsuno, Fumitoshi and Gauci, Melvin”,
title=”Information Based Exploration with Panoramas and Angle Occupancy Grids”,
bookTitle=”Distributed Autonomous Robotic Systems: The 13th International Symposium”,
year=”2018″,
publisher=”Springer International Publishing”,
address=”Cham”,
pages=”45–58″,
isbn=”978-3-319-73008-0″,
doi=”10.1007/978-3-319-73008-0_4″,
url=”https://doi.org/10.1007/978-3-319-73008-0_4″
}
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@Inbook{HsiehISRR2015,
author=”Ani Hsieh, M. and Hajieghrary, Hadi and Kularatne, Dhanushka and Heckman, Christoffer R. and Forgoston, Eric and Schwartz, Ira B. and Yecko, Philip A.”,
editor=”Bicchi, Antonio and Burgard, Wolfram”,
title=”Small and Adrift with Self-Control: Using the Environment to Improve Autonomy”,
bookTitle=”Robotics Research: Volume 2″,
year=”2018″,
publisher=”Springer International Publishing”,
address=”Cham”,
pages=”387–402″,
abstract=”We present information theoretic search strategies for single and multi-robot teams to localize the source of a chemical spill in turbulent flows. In this work, robots rely on sporadic and intermittent sensor readings to synthesize information maximizing exploration strategies. Using the spatial distribution of the sensor readings, robots construct a belief distribution for the source location. Motion strategies are designed to maximize the change in entropy of this belief distribution. In addition, we show how a geophysical description of the environmental dynamics can improve existing motion control strategies. This is especially true when process and vehicle dynamics are intricately coupled with the environmental dynamics. We conclude with a summary of current efforts in robotic tracking of coherent structures in geophysical flows. Since coherent structures enables the prediction and estimation of the environmental dynamics, we discuss how this geophysical perspective can result in improved control strategies for autonomous systems.”,
isbn=”978-3-319-60916-4″,
doi=”10.1007/978-3-319-60916-4_22″,
url=”https://doi.org/10.1007/978-3-319-60916-4_22″
}
Back
@INPROCEEDINGS{DiganiCASE2015,
author={V. Digani and M. A. Hsieh and L. Sabattini and C. Secchi},
booktitle={2015 IEEE International Conference on Automation Science and Engineering (CASE)},
title={A Quadratic Programming approach for coordinating multi-AGV systems},
year={2015},
volume={},
number={},
pages={600-605},
keywords={automatic guided vehicles;decentralised control;logistics;mobile robots;quadratic programming;warehouse automation;quadratic programming;QP;multiAGV system;autonomous guided vehicle;optimization strategy;ad-hoc predefined roadmap;logistic operation;warehouse automation;decentralized coordination strategy;Vehicles;Optimization;Linear programming;Collision avoidance;Throughput;System recovery;Complexity theory},
doi={10.1109/CoASE.2015.7294144},
ISSN={2161-8070},
month={Aug},}
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@inproceedings{KularatneRSS2015,
author = {Kularatne, Dhanushka and Hsieh, M. Ani},
booktitle={Robotics: Science and Systems 2015}, year = {2015},
month = {Jul},
address={Rome, Italy},
title = {Tracking Attracting Lagrangian Coherent Structures in Flows},
doi = {10.15607/RSS.2015.XI.021},
}
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@inproceedings{ref:Chee2020,
author = {K. Y. {Chee} and M. A. {Hsieh}},
booktitle = {2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},
title = {Augmenting Coverage Control with Robot-Environment Dependency for Multi-Robot Systems},
year = {2020},
volume = {},
number = {},
pages = {260-267},
doi = {10.1109/SSRR50563.2020.9292628}}