An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

Walls, Jeffrey M.; Eustice, Ryan M.

doi:10.15607/rss.2013.ix.010

Cited by 13 publications

(8 citation statements)

References 30 publications

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“…A hierarchical approach sidesteps this problem by restricting communication and relative ranging to be one-way. For example, [24], where one or more support vehicles are referred to as communications and navigation aids (CNA), and [25], [16] where vehicles are separated into "servers" and "clients". The necessity to transmit a full joint covariance matrix can also be avoided through the interleaved update approach in [26], however the estimates from this approach are overly conservative.…”

Section: A the Underwater Casementioning

confidence: 99%

“…In the case that the receiving vehicle already has some of the information contained within the factor that is transmitted, then a new correct and consistent factor can be generated through local subtraction. This is related to the "origin-state" method proposed in [16], but extends it by removing the need for relative measurements/communications to be unidirectional.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Paull

Seto²,

Leonard

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.

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Section: A the Underwater Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Paull

Seto²,

Leonard

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…An alternative approach is to restrict underwater communications to be one-way to avoid information double-counting. For example, survey vs. aid teams [14], or client and server in the origin state method of Walls and Eustice [15]. Here, we allow fully bi-direction communication where every vehicle has equal access to the communications channel.…”

Section: Related Workmentioning

confidence: 99%

Communication-constrained multi-AUV cooperative SLAM

Paull¹,

Huang

Seto³

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.

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“…This measurement also doubles as the communication channel which transmits the broadcast shared information. An extension to handle lost packets was shown in [10]. It is not clear if agents can retask and periodically change the direction of information flow if the disparity of information changes or if the system would need to restart and discard old data.…”

Section: Related Workmentioning

confidence: 99%

Multi-vehicle localisation with additive compressed factor graphs

Toohey

Pizarro

Williams

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper introduces a distributed and decentralised method to solve the cooperative localisation problem utilising the factor graph framework. This method enables vehicles to create compact packets of their own sensor information between their involvement in intervehicle measurements. The packets are limited in size, with this size dependent on the size of the state space alone. The number of packets generated is also limited at two packets (one per vehicle) created per intervehicle measurement. The packets and measurements can be shared and propagated to all vehicles not just direct neighbours or to vehicles involved in the measurements. Vehicles maintain a local solver that incorporates all local sensor information and motion updates in full nonlinear form and includes the fixed linearised packets from other vehicles. This local solver is able to update local state variables and relinearise local and intervehicle factors but has to hold remote state variables and packet data at a fixed linearisation point. We show the estimated solution is of a similar quality to a state of the art centralised relinearising estimator iSAM2 and superior to a fixed linearisation filtering solution via comparing RMS error in position estimation in simulation. Consistency of the method is also shown via the NEES metric. Communication requirements for each of the competing methods are shown, with compaction of packets being more useful the larger the ratio between intervehicle and local measurement intervals. The technique is validated using multivehicle simulation and real datasets.

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An Exact Decentralized Cooperative Navigation Algorithm for Acoustically Networked Underwater Vehicles with Robustness to Faulty Communication: Theory and Experiment

Cited by 13 publications

References 30 publications

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Communication-constrained multi-AUV cooperative SLAM

Multi-vehicle localisation with additive compressed factor graphs

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