A hybrid underwater robot for multidisciplinary investigation of the ocean twilight zone

Dana R. Yoerger; Annette F. Govindarajan; Jonathan C. Howland; Joel K. Llopiz; Peter H. Wiebe; Molly Curran; Justin Fujii; Daniel Gomez-Ibanez; Kakani Katija; Bruce H. Robison; Brett W. Hobson; Michael Risi; Stephen M. Rock

doi:10.1126/scirobotics.abe1901

A hybrid underwater robot for multidisciplinary investigation of the ocean twilight zone

Science Robotics ◽

10.1126/scirobotics.abe1901 ◽

2021 ◽

Vol 6 (55) ◽

pp. eabe1901 ◽

Cited By ~ 1

Author(s):

Dana R. Yoerger ◽

Annette F. Govindarajan ◽

Jonathan C. Howland ◽

Joel K. Llopiz ◽

Peter H. Wiebe ◽

...

Keyword(s):

Autonomous Vehicles ◽

Unmet Needs ◽

Autonomous Underwater Vehicle ◽

Sensor Data ◽

Monterey Bay ◽

Environmental Disturbance ◽

Acoustic Sensors ◽

Free Swimming ◽

Behavioral Studies ◽

Slow Moving

Mesobot, an autonomous underwater vehicle, addresses specific unmet needs for observing and sampling a variety of phenomena in the ocean’s midwaters. The midwater hosts a vast biomass, has a role in regulating climate, and may soon be exploited commercially, yet our scientific understanding of it is incomplete. Mesobot has the ability to survey and track slow-moving animals and to correlate the animals’ movements with critical environmental measurements. Mesobot will complement existing oceanographic assets such as towed, remotely operated, and autonomous vehicles; shipboard acoustic sensors; and net tows. Its potential to perform behavioral studies unobtrusively over long periods with substantial autonomy provides a capability that is not presently available to midwater researchers. The 250-kilogram marine robot can be teleoperated through a lightweight fiber optic tether and can also operate untethered with full autonomy while minimizing environmental disturbance. We present recent results illustrating the vehicle’s ability to automatically track free-swimming hydromedusae (Solmissus sp.) and larvaceans (Bathochordaeus stygius) at depths of 200 meters in Monterey Bay, USA. In addition to these tracking missions, the vehicle can execute preprogrammed missions collecting image and sensor data while also carrying substantial auxiliary payloads such as cameras, sonars, and samplers.

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Complete Coverage Path Planning in an Unknown Underwater Environment Based on D-S Data Fusion Real-Time Map Building

International Journal of Distributed Sensor Networks ◽

10.1155/2012/567959 ◽

2012 ◽

Vol 8 (10) ◽

pp. 567959 ◽

Cited By ~ 7

Author(s):

Mingzhong Yan ◽

Daqi Zhu ◽

Simon X. Yang

Keyword(s):

Path Planning ◽

Real Time ◽

Autonomous Underwater Vehicle ◽

Evidence Theory ◽

Sensor Data ◽

Map Building ◽

Complete Coverage ◽

Coverage Path Planning ◽

Underwater Environment ◽

Time Map

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

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AUV-Based Plume Tracking: A Simulation Study

Journal of Control Science and Engineering ◽

10.1155/2016/1764527 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Awantha Jayasiri ◽

Raymond G. Gosine ◽

George K. I. Mann ◽

Peter McGuire

Keyword(s):

Simulation Study ◽

Navigation System ◽

Autonomous Underwater Vehicle ◽

Unscented Kalman Filter ◽

High Rate ◽

Sensor Data ◽

Boundary Tracking ◽

Plume Tracking ◽

Gradient Based ◽

Rate Sensor

This paper presents a simulation study of an autonomous underwater vehicle (AUV) navigation system operating in a GPS-denied environment. The AUV navigation method makes use of underwater transponder positioning and requires only one transponder. A multirate unscented Kalman filter is used to determine the AUV orientation and position by fusing high-rate sensor data and low-rate information. The paper also proposes a gradient-based, efficient, and adaptive novel algorithm for plume boundary tracking missions. The algorithm follows a centralized approach and it includes path optimization features based on gradient information. The proposed algorithm is implemented in simulation on the AUV-based navigation system and successful boundary tracking results are obtained.

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Joint Chilean and US mobility testing in extreme environments

10.21079/11681/42362 ◽

2021 ◽

Author(s):

Michael Parker ◽

Alex Stott ◽

Brian Quinn ◽

Bruce Elder ◽

Tate Meehan ◽

...

Keyword(s):

Autonomous Vehicles ◽

Extreme Environments ◽

Sensor Data ◽

Unmanned Aerial Systems ◽

Area Network ◽

The Us ◽

Vehicle Mobility ◽

Data Collections ◽

Aerial Systems ◽

Vehicle Sensor

Vehicle mobility in cold and challenging terrains is of interest to both the US and Chilean Armies. Mobility in winter conditions is highly vehicle dependent with autonomous vehicles experiencing additional challenges over manned vehicles. They lack the ability to make informed decisions based on what they are “seeing” and instead need to rely on input from sensors on the vehicle, or from Unmanned Aerial Systems (UAS) or satellite data collections. This work focuses on onboard vehicle Controller Area Network (CAN) Bus sensors, driver input sensors, and some externally mounted sensors to assist with terrain identification and overall vehicle mobility. Analysis of winter vehicle/sensor data collected in collaboration with the Chilean Army in Lonquimay, Chile during July and August 2019 will be discussed in this report.

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A Connected Autonomous Vehicle Testbed: Capabilities, Experimental Processes and Lessons Learned

Automation ◽

10.3390/automation1010002 ◽

2020 ◽

Vol 1 (1) ◽

pp. 17-32

Author(s):

Thomas Kent ◽

Anthony Pipe ◽

Arthur Richards ◽

Jim Hutchinson ◽

Wolfgang Schuster

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Lessons Learned ◽

Urban Setting ◽

Sensor Data ◽

Stable Set ◽

Research And Innovation ◽

Legal Frameworks ◽

High Level ◽

Key Steps

VENTURER was one of the first three UK government funded research and innovation projects on Connected Autonomous Vehicles (CAVs) and was conducted predominantly in the South West region of the country. A series of increasingly complex scenarios conducted in an urban setting were used to: (i) evaluate the technology created as a part of the project; (ii) systematically assess participant responses to CAVs and; (iii) inform the development of potential insurance models and legal frameworks. Developing this understanding contributed key steps towards facilitating the deployment of CAVs on UK roads. This paper aims to describe the VENTURER Project trials, their objectives and detail some of the key technologies used. Importantly we aim to introduce some informative challenges that were overcame and the subsequent project and technological lessons learned in a hope to help others plan and execute future CAV research. The project successfully integrated several technologies crucial to CAV development. These included, a Decision Making System using behaviour trees to make high level decisions; A pilot-control system to smoothly and comfortably turn plans into throttle and steering actuation; Sensing and perception systems to make sense of raw sensor data; Inter-CAV Wireless communication capable of demonstrating vehicle-to-vehicle communication of potential hazards. The closely coupled technology integration, testing and participant-focused trial schedule led to a greatly improved understanding of the engineering and societal barriers that CAV development faces. From a behavioural standpoint the importance of reliability and repeatability far outweighs a need for novel trajectories, while the sensor-to-perception capabilities are critical, the process of verification and validation is extremely time consuming. Additionally, the added capabilities that can be leveraged from inter-CAV communications shows the potential for improved road safety that could result. Importantly, to effectively conduct human factors experiments in the CAV sector under consistent and repeatable conditions, one needs to define a scripted and stable set of scenarios that uses reliable equipment and a controllable environmental setting. This requirement can often be at odds with making significant technology developments, and if both are part of a project’s goals then they may need to be separated from each other.

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GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion

2020 22nd International Conference on Transparent Optical Networks (ICTON) ◽

10.1109/icton51198.2020.9203087 ◽

2020 ◽

Author(s):

Nicolas Souli ◽

Christos Laoudias ◽

Panayiotis Kolios ◽

Christian Vitale ◽

Georgios Ellinas ◽

...

Keyword(s):

Data Fusion ◽

Autonomous Vehicles ◽

Sensor Data ◽

Sensor Data Fusion ◽

Location Verification

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Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics

Applied Sciences ◽

10.3390/app10186317 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6317 ◽

Cited By ~ 1

Author(s):

Wilfried Wöber ◽

Georg Novotny ◽

Lars Mehnen ◽

Cristina Olaverri-Monreal

Keyword(s):

Parameter Estimation ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

The Other ◽

Sensor Data ◽

Critical Feature ◽

Motion Data ◽

Movement Data ◽

End Use ◽

Vehicle Movement

On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.

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Grid-based Environment Estimation for Local Autonomous Vehicle Navigation

10.51202/9783186246080 ◽

2016 ◽

Author(s):

Georg Tanzmeister

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Local Environment ◽

Sensor Data ◽

Test Vehicle ◽

Safety Critical ◽

Environment Model ◽

Reliable Model ◽

Autonomous Vehicle Navigation ◽

Grid Based

This dissertation is focused on the environment model for automated vehicles. A reliable model of the local environment available in real-time is a prerequisite to enable almost any useful activity performed by a robot, such as planning motions to fulfill tasks. It is particularly important in safety critical applications, such as for autonomous vehicles in regular traffic. In this thesis, novel concepts for local mapping, tracking, the detection of principal moving directions, cost evaluations in motion planning, and road course estimation have been developed. An object- and sensor-independent grid representation forms the basis of all presented methods enabling a generic and robust estimation of the environment. All approaches have been evaluated with sensor data from real road scenarios, and their performance has been experimentally demonstrated with a test vehicle. ...

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The hydroid and early medusa stages of the deep sea jellyfish Earleria purpurea (Hydrozoa: Mitrocomidae) from the Monterey Bay Submarine Canyon, USA

Zootaxa ◽

10.11646/zootaxa.2902.1.3 ◽

2011 ◽

Vol 2902 (1) ◽

pp. 59

Author(s):

CHAD L. WIDMER

Keyword(s):

Life Cycle ◽

Deep Sea ◽

Submarine Canyon ◽

Monterey Bay ◽

Remotely Operated Vehicle ◽

Free Swimming

The hydroid and early medusa stages of the deep sea hydrozoan jellyfish Earleria purpurea (Hydrozoa: Mitrocomidae) are described. Mature medusae were collected from the Monterey Bay submarine canyon near Monterey, California, USA utilizing a remotely operated vehicle and returned to the laboratory for culturing. In vitro fertilized eggs developed into free-swimming planulae larvae that settled and metamorphosed into benthic hydroid colonies consisting of feeding hydranths and medusa producing gonangia. Newly released medusae were grown to maturity and placed on educational display at the Monterey Bay Aquarium. The hydranths and gonangia were compared and found to be distinct from those of E. corachloeae the only other member of the Genus Earleria with a described life cycle.

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