Towards Advancing Diver-Robot Interaction Capabilities

Nađ, Ðula; Walker, Christopher; Kvasić, Igor; Antillon, Derek Orbaugh; Mišković, Nikola; Anderson, Iain A.; Lončar, Ivan

doi:10.1016/j.ifacol.2019.12.307

Cited by 16 publications

(11 citation statements)

References 13 publications

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“…On Earth, DETs have proven to be excellent candidates for actuation, strain-sensing (the capacitance of DETs increases with strain), and energy generation ( Anderson et al, 2012 ). For example, as strain sensors they have been used to monitor the human body in motion-capture gloves ( StretchSense, 2020 ) and diver body kinematics with gesture recognition ( Walker and Anderson, 2017 ; Nad et al, 2019 ). DETs have also been produced that can sense compression, and shear forces which have potential applications in fields as varied as health care and agriculture ( Mahmoudinezhad et al, 2020 ; Kim et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

One Soft Step: Bio-Inspired Artificial Muscle Mechanisms for Space Applications

et al. 2022

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Soft robots, devices with deformable bodies and powered by soft actuators, may fill a hitherto unexplored niche in outer space. All space-bound payloads are heavily limited in terms of mass and volume, due to the cost of launch and the size of spacecraft. Being constructed from stretchable materials allows many possibilities for compacting soft robots for launch and later deploying into a much larger volume, through folding, rolling, and inflation. This morphability can also be beneficial for adapting to operation in different environments, providing versatility, and robustness. To be truly soft, a robot must be powered by soft actuators. Dielectric elastomer transducers (DETs) offer many advantages as artificial muscles. They are lightweight, have a high work density, and are capable of artificial proprioception. Taking inspiration from nature, in particular the starfish podia, we present here bio-inspired inflatable DET actuators powering low-mass robots capable of performing complex motion that can be compacted to a fraction of their operating size.

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Section: Introductionmentioning

confidence: 99%

One Soft Step: Bio-Inspired Artificial Muscle Mechanisms for Space Applications

et al. 2022

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“…Recent research in this area has utilized specially designed AUVs [13] [14]. However, these AUVs are not widely available, and their designs limit their capabilities outside the humanrobot relationship.…”

Section: Motivationmentioning

confidence: 99%

“…Two significant research efforts into robotic dive buddies over the last decade are Cognitive Autonomous Diving Buddy (CADDY) [13] and Advancing Diver-Robot Interaction Capabilities (ADRIATIC) [14]. Since 2014, the CADDY project has improved an AUV's ability to track a diver.…”

Section: Diver Navigation and Underwater Human-robot Teamingmentioning

confidence: 99%

“…ADRIATIC applied the concept of wearable sensors to gloves, which can acoustically send hand signals to the AUV using strain sensors and an IMU. While water visibility and the necessity for both divers to be looking decreases hand signals' effectiveness, this sensing glove recognizes the hand gesture and acoustically sends it to the AUV [14]. Therefore, they ensure that the diver can communicate with the AUV even when visibility is low and diver-AUV orientation is not ideal.…”

Section: Diver Navigation and Underwater Human-robot Teamingmentioning

confidence: 99%

“…Like CADDY, the ADRIATIC project uses a novel over-actuated AUV explicitly designed to assist the diver with tasks and safety monitoring. Their dive buddy AUV concept is a handheld dive tablet attached to propulsion skids [14]. The tablet is a Kenautics D2 Diving Navigation System that includes a DVL, microelectromechanical systems (MEMS) attitude and heading reference system (AHRS), pressure sensor, FLS system, low-light video camera, and GPS [44].…”

Section: Caddian Increases the Depth Of Communication Possible Betwee...mentioning

confidence: 99%

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Human-autonomy teaming for improved diver navigation

Pelletier¹

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Diving operations are inherently complex due to navigation and communication limitations. Until recently, fixed-beacon acoustic localization techniques have served as the primary means of improving diver navigation. However, modern artificial intelligence and acoustic modem technologies have enabled accurate relative navigation methods between a diver and an autonomous vehicle. Human-robot collaboration takes advantage of each member’s strengths to create the most effective team. This concept proves especially advantageous within the ocean domain, where humans are naturally deficient navigators. Yet humans serve as the team’s creative spirit, offering the critical thinking and flexibility needed to succeed in an unpredictable and dynamic environment. Recent underwater human-robot cooperative navigation systems typically rely on autonomous surface vehicles (ASVs), specially designed underwater vehicles, or stereo cameras. This thesis proposes a diver navigation method exhibiting significantly improved accuracy over dead reckoning without relying on a surface presence, cameras, or fixed acoustic beacons. Specifically, we develop and evaluate the communication architecture and autonomous behaviors required to guide a diver to a target location using subsurface humanautonomous underwater vehicle (AUV) teaming with no requirement for ocean current data or exact diver speeds. By depending on acoustic communication and commercial AUV navigation capabilities, our method has increased accessibility, applicability, and robustness over former techniques. We utilize the Woods Hole Oceanographic Institution (WHOI) Micromodem 2’s twoway-travel-time (TWTT) capability to enable range-only single-beacon navigation between two kayaks serving as proxies for the diver and Remote Environmental Monitoring Units (REMUS) 100 AUV. During processing, a nonlinear least-squares (NLS) method, called incremental smoothing and mapping 2 (iSAM2), utilizes odometry and range measurements to provide real-time diver position estimates given unknown ocean currents. Field experiments demonstrate an average online endpoint error of 4.53 meters after transits four hundred meters long. Additionally, simulations test our method’s performance in more challenging situations than those experienced in the field. Overall, this research progresses the interoperability of divers and AUVs.

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