Scalable Event-Triggered Data Fusion for Autonomous Cooperative Swarm Localization

Loefgren, Ian; Ahmed, Nisar; Frew, Eric W.; Heckman, Christoffer; Humbert, Sean

doi:10.23919/fusion43075.2019.9011247

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…We adopt the same notation as in [14]. Let a quantity, S, pertaining to an agent, i, at time k be S i k , and let j's (j ̸ = i) estimate of that same quantity be S j,i k .…”

Section: Problem Definitionmentioning

confidence: 99%

“…ET methods have also been proposed that can greatly improve upon the conservativeness of solely DDF approaches. approach for the heterogeneous states case to increase scalability [14]. While all of the above approaches are able to balance localization accuracy with communication cost and maintain cross correlations between states, they are not natively applicable to the subsea environment due to their asynchronous, low latency communication assumptions.…”

Section: Related Workmentioning

confidence: 99%

“…The decentralized ET approaches in [14,16,17] made two assumptions that do not hold up well in the underwater environment with typical AM systems. The first is the notion of asynchronous communication.…”

Section: Acoustic Modem Propertiesmentioning

confidence: 99%

“…Event-triggered (ET) Kalman filtering is an approach to cooperative localization that utilizes intelligent measurement sharing [14,16,20]. The core idea is that data is only exchanged between communicating agents if some mutually known event threshold is satisfied at the transmitter.…”

Section: Introductionmentioning

confidence: 99%

“…Fig.2.3 highlights the subset of overlapping states between the tracking filter and the navigation filter for an agent i. By utilizing crosscorrelations in the estimate, non-overlapping states that were not subject to direct fusion updates are subsequently updated by each filter via conditional Gaussian updating for Kalman filters, as done in the partial-state covariance intersection (PSCI) approach[1,14].…”

mentioning

confidence: 99%

See 4 more Smart Citations

Distributed Event-Triggered Localization for High Latency Communication

Barbier

Morrissey

Ahmed

et al. 2022

2022 American Control Conference (ACC)

Self Cite

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Thank you to Prof. Nisar Ahmed for your guidance, insight, and feedback and, most of all, providing me with a job I could grow with for 5 years.Thank you to Orbit Logic team: Ken Center, Robert Glissmann, James Smith and Evan Sneath for their collaboration on the MinAu project and all of the great experiences had at Cherry Creek and San Diego. Thank you to Luke Morrissey for being a team player through all of the ups and downs of hardware deployment. Thank you to Jeremy Muesing for being a supportive friend, offering guidance and connecting me with the job at COHRINT. Thank you to Luke Burks for being an understanding boss and offering helpful guidance. Thank you to Jeff Venicx for inspiring in me a passion for underwater robotics and helping to connect me with the job at COHRINT. Thank you to the Naval Information Warface Center folks for their support in hardware deployment.

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“…We adopt the same notation as in [14]. Let a quantity, S, pertaining to an agent, i, at time k be S i k , and let j's (j ̸ = i) estimate of that same quantity be S j,i k .…”

Section: Problem Definitionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Acoustic Modem Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Distributed Event-Triggered Localization for High Latency Communication

Barbier

Morrissey

Ahmed

et al. 2022

2022 American Control Conference (ACC)

Self Cite

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Cooperative Localization and Mapping with Robotic Swarms

Pires

Rezeck

Chaves

et al. 2021

J Intell Robot Syst

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A Concurrent Mission-Planning Methodology for Robotic Swarms Using Collaborative Motion-Control Strategies

Eshaghi

Nejat

Benhabib

2023

J Intell Robot Syst

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Swarm robotic systems comprising members with limited onboard localization capabilities rely on employing collaborative motion-control strategies to successfully carry out multi-task missions. Such strategies impose constraints on the trajectories of the swarm and require the swarm to be divided into worker robots that accomplish the tasks at hand, and support robots that facilitate the movement of the worker robots. The consideration of the constraints imposed by these strategies is essential for optimal mission-planning. Existing works have focused on swarms that use leader-based collaborative motion-control strategies for mission execution and are divided into worker and support robots prior to mission-planning. These works optimize the plan of the worker robots and, then, use a rule-based approach to select the plan of the support robots for movement facilitation – resulting in a sub-optimal plan for the swarm. Herein, we present a mission-planning methodology that concurrently optimizes the plan of the worker and support robots by dividing the mission-planning problem into five stages: division-of-labor, task-allocation of worker robots, worker robot path-planning, movement-concurrency, and movement-allocation. The proposed methodology concurrently searches for the optimal value of the variables of all stages. The proposed methodology is novel as it (1) incorporates the division-of-labor of the swarm into worker and support robots into the mission-planning problem, (2) plans the paths of the swarm robots to allow for concurrent facilitation of multiple independent worker robot group movements, and (3) is applicable to any collaborative swarm motion-control strategy that utilizes support robots. A unique pre-implementation estimator, for determining the possible improvement in mission execution performance that can achieved through the proposed methodology was also developed to allow the user to justify the additional computational resources required by it. The estimator uses a machine learning model and estimates this improvement based on the parameters of the mission at hand. Extensive simulated experiments showed that the proposed concurrent methodology improves the mission execution performance of the swarm by almost 40% compared to the competing sequential methodology that optimizes the plan of the worker robots first and, then, the plan of the support robots. The developed pre-implementation estimator was shown to achieve an estimation error of less than 5%.

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Scalable Event-Triggered Data Fusion for Autonomous Cooperative Swarm Localization

Cited by 9 publications

References 14 publications

Distributed Event-Triggered Localization for High Latency Communication

Distributed Event-Triggered Localization for High Latency Communication

Cooperative Localization and Mapping with Robotic Swarms

A Concurrent Mission-Planning Methodology for Robotic Swarms Using Collaborative Motion-Control Strategies

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