Single-Hydrophone Low-Cost Underwater Vehicle Swarming

Fischell, Erin M.; Kroo, Anne R.; O’Neill, Brendan W.

doi:10.1109/lra.2019.2958774

Cited by 31 publications

(18 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This assessment is based on a low-cost underwater vehicle being in the $10k range and the CSAC accounting for up to $5,500 of that amount. These cost estimates rely on previous work by Fischell et al in [14] and common low-cost underwater vehicles such as described in [28] and [29]. Two-way travel time (TWTT) systems reduce the need for closely synchronized clocks but require vehicles that can receive and respond to incoming signals, usually via an acoustic modem [13].…”

Section: Current Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Signal Absorption-Based Range Estimator for Undersea Swarms

O’Neill¹

2020

View full text Add to dashboard Cite

Robotic swarms are becoming increasingly complex on the surface and in air due to highspeed and reliable communication links, Global Positioning Satellites (GPS), and visual support to relative navigation. However, the limited propagation of these signals in the ocean has impacted similar advances in undersea robotics. Autonomous underwater vehicles (AUVs) often rely on acoustics to inform navigation solutions; however, this approach presents challenges for scalable robotic swarms. Acoustic navigation is a means to inform range and bearing to a target. Many methods for range and bearing estimation, including current low-cost solutions, rely on precision time synchronization or two-way communication to compute ranges as part of a full navigation solution. The high cost of reliable Chip-scale atomic clocks (CSACs) and acoustic modems relative to other vehicle components limits large-scale swarms due to the associated cost-per-vehicle and communications infrastructure. We propose a single, high-cost vehicle with a reliable navigation solution as a "leader" for a scalable swarm of lower-cost vehicles that receive acoustic signals from a source onboard the lead vehicle using a single hydrophone. These lower-cost "followers" navigate relative to the leader according to the preferred behavioral pattern, but for simplicity, we will refer to a simple following behavior in this work. This thesis outlines a method to obtain range estimates to sound sources in which the signal content, including frequency and power at its origin, can be reasonably approximated. Total transmission loss is calculated based on empirical equations for the absorption of sound in seawater and combined with geometric spreading loss from environmental models to estimate range to a source based on the loss at differential frequencies. We refer to this calculation as the signal absorption-based range estimator (SABRE). This method for obtaining range combines with Doppler-shift methods for target bearing based on the maximum frequency detected within a banded limit around a known source frequency. A primary objective for SABRE is to address techniques that support low-cost options for undersea swarming. This thesis's contributions include a novel method for range estimation onboard underwater autonomous vehicles that supports navigation relative to a known source when combined with Doppler-shift methods for target bearing. This thesis seeks to develop the theory, algorithms, and analytical tools required and apply those tools to real-world data sets to investigate the feasibility, sources of error, and accuracy of this new approach to range estimation for underwater swarms.

show abstract

Section: Current Methodsmentioning

confidence: 99%

“…Fischell, et al describe this feedback control method in detail in [14] and the estimate for target bearing provides half the required information for localization. SABRE provides the second half with an estimate for range to accompany bearing to target.…”

Section: Doppler Shift For Bearing Estimatesmentioning

confidence: 99%

Signal Absorption-Based Range Estimator for Undersea Swarms

O’Neill¹

2020

View full text Add to dashboard Cite

show abstract

“…A swarm drone network (SDN) is expected to be largely implemented and deployed, per in mind that all MD drones are non-stationary once they are deployed, which means that the process needs to be repeated periodically and the topology would be rapidly changing. Each MD drone can get the estimations of neighbor drones' positions employing any positioning techniques i.e., angle-of-arrival (AoA), time-of-arrival (ToA) or swarm drones' control center (SDCC) [55], or simply can exchange their GPS parameters.…”

Section: Swarm Cluster Inter-cluster Synchronizationmentioning

confidence: 99%

“…However, the security issues are out of the paper scope. More details about these issues can be found in [46][47][48][49][50][51][52][53][54][55][56][57].…”

Section: Repeatmentioning

confidence: 99%

Development of Self-Synchronized Drones’ Network Using Cluster-Based Swarm Intelligence Approach

et al. 2021

View full text Add to dashboard Cite

Timing synchronization has a vital role in swarm drones' network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones' network, master drones' (MD) neighbor drone's timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intraswarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.INDEX TERMS Drones' Network, Timing synchronization, unmanned aerial vehicle (UAV), Cluster, Swarm.

show abstract

“…This allows for complete localization using only range measurements and depending on the operational requirements can enable localization in which a small number of AUVs use GNSS fixes to maintain absolute localization for the entire network. Such an approach allows use of recently developed low-cost ranging technology [5] and absolves need for high-cost inertial navigation solutions or fixed acoustic beacons. Many distributed and centralized SNL techniques have been developed to robustly perform localization in the presence of noisy range measurements [6,7].…”

Section: Introductionmentioning

confidence: 99%

Network Localization Based Planning for Autonomous Underwater Vehicles with Inter-Vehicle Ranging

Papalia¹,

Leonard²

2020

Preprint

View full text Add to dashboard Cite

Localization between a swarm of AUVs can be entirely estimated through the use of range measurements between neighboring AUVs via a class of techniques commonly referred to as sensor network localization. However, the localization quality depends on network topology, with degenerate topologies, referred to as low-rigidity configurations, leading to ambiguous or highly uncertain localization results. This paper presents tools for rigidity-based analysis, planning, and control of a multi-AUV network which account for sensor noise and limited sensing range. We evaluate our long-term planning framework in several two-dimensional simulated environments and show we are able to generate paths in feasible time and guarantee a minimum network rigidity over the full course of the paths.

show abstract

Single-Hydrophone Low-Cost Underwater Vehicle Swarming

Cited by 31 publications

References 32 publications

Signal Absorption-Based Range Estimator for Undersea Swarms

Signal Absorption-Based Range Estimator for Undersea Swarms

Development of Self-Synchronized Drones’ Network Using Cluster-Based Swarm Intelligence Approach

Network Localization Based Planning for Autonomous Underwater Vehicles with Inter-Vehicle Ranging

Contact Info

Product

Resources

About