Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments

Kemna, Stephanie; Rogers, John G.; Nieto-Granda, Carlos; Young, Stuart H.; Sukhatme, Gaurav S.

doi:10.1109/icra.2017.7989245

Cited by 51 publications

(32 citation statements)

References 8 publications

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“…The authors employ a solution based on a traveling salesman problem. A recent paper by Kemna et al (2017) shows an interesting usage scenario of an underwater multi-robot team and applies a dynamic Voronoi partitioning technique for decentralized and adaptive robot interaction in unknown environments.…”

Section: Related Workmentioning

confidence: 99%

Long-term pattern formation and maintenance for battery-powered robots

Švogor

Beltrame

2019

Swarm Intell

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This paper presents a distributed, energy-aware method for the autonomous deployment and maintenance of battery-powered robots within a known or unknown region in 2D space. Our approach does not rely on a global positioning system and therefore allows for applications in GPS-denied environments such as underwater sensing or underground monitoring. After covering a region, our system maintains a formation and uses an arbitrary number of charging stations to prevent robots from fully discharging. Analyzing the topology of the network formed during robot deployment, we generate virtual recharging trees which the robots use to navigate toward a nearby charging station when needed. All robots that leave the formation are replaced by their neighbors, maximizing the effective coverage provided by the system. We demonstrate the capability of our methods using models, a physics-based simulator, and experiments with real robots.

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Section: Related Workmentioning

confidence: 99%

Long-term pattern formation and maintenance for battery-powered robots

Švogor

Beltrame

2019

Swarm Intell

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“…Collaboration between multiple feature‐detecting AUVs would greatly enhance synopticity, coverage, and endurance of automated oceanographic studies (Petillo, ; Petillo et al, ; Reed & Hover, ). The complexity and low bandwidth of the underwater acoustic communications channel poses challenges to multi‐AUV collaboration; however, those challenges can be overcome (Kemna, Caron, & Sukhatme, ; Kemna, Rogers, Nieto‐Granda, Young, & Sukhatme, ; Schmidt, Benjamin, Petillo, & Lum, ). The development of autonomous methods is further complicated by the difficulty and cost of deploying and operating underwater vehicles, leading to less testing and refinement of autonomous sampling approaches.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

“…The development of autonomous methods is further complicated by the difficulty and cost of deploying and operating underwater vehicles, leading to less testing and refinement of autonomous sampling approaches. Ocean engineers are pushing the envelope of underwater communications bandwidth and range, as well as realizing multivehicle collaboration using the available bandwidth (Kemna et al, , ; Schmidt et al, ).…”

Section: Comparison With Previous Workmentioning

confidence: 99%

Front delineation and tracking with multiple underwater vehicles

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Claus

et al. 2018

Journal of Field Robotics

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This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front.Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short-range Iver AUVs, Tethys-class long-range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles. K E Y W O R D S adaptive sampling, autonomous underwater vehicles, multiasset planning, ocean front tracking 1 | INTRODUCTION Space-based remote sensing can provide extensive information about ocean dynamics. However, remote sensing information is generally limited to measuring the ocean surface. To probe the ocean interior efficiently requires marine vehicles such as autonomous underwater vehicles (AUVs), gliders, profiling buoys, surface vehicles, and ships sampling in situ. Unfortunately, building, deploying, and operating these in situ marine robotic explorers is expensive. As a result, any actual study involves a limited number of marine vehicles, especially when compared to the vast expanse of the ocean. Determining where to deploy and operate marine assets is a challenging problem given the 4D spatiotemporal variations in oceanographic phenomena.The use of autonomous marine vehicles will increase as the size of ocean observing systems expand to study the impact of the oceans on Earth's climate and ecosystems. The day-to-day operations of these systems will become increasingly difficult if human intervention is required. To enable large observing systems to operate more efficiently, techniques for autonomous control of assets based on science goals and data sources such as in situ J Field Robotics. 2019;36:568-586. wileyonlinelibrary.com/journal/rob

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“…Two existing myopic solutions to adaptive AUV trajectory planning are described in the following. In References [ 17 , 18 ], to prevent the existence of a single point of failure and to achieve the scalability to the number of AUVs, a decentralized strategy for multi-AUV sampling and patrolling is developed. The region of interest is dynamically partitioned into multiple Voronoi cells according to the AUV locations.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Multi-AUV Adaptive Trajectory Planning for Under-Ice Field Estimation

Wang

et al. 2018

Sensors

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This work studies online learning-based trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the under-ice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyper-parameters. The AUV trajectories for sampling are determined on an epoch-by-epoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyper-parameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a long-term reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learning-based online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation results show that the proposed learning-based trajectory planning algorithm has performance similar to a benchmark method that assumes perfect knowledge of the field hyper-parameters.

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Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments

Cited by 51 publications

References 8 publications

Long-term pattern formation and maintenance for battery-powered robots

Long-term pattern formation and maintenance for battery-powered robots

Front delineation and tracking with multiple underwater vehicles

Reinforcement Learning-Based Multi-AUV Adaptive Trajectory Planning for Under-Ice Field Estimation

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