A Rapid Situational Awareness Development Framework for Heterogeneous Manned-Unmanned Teams

Das, Avishek; Kol, Patrik; Lundberg, Cody Lee; Doelling, Kris; Sevil, Hakkı Erhan; Lewis, Frank L.

doi:10.1109/naecon.2018.8556769

Cited by 15 publications

(9 citation statements)

References 41 publications

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“…Ding, et al [123] developed an IA to accelerate human-agent collaboration in tactical and strategic battlefield decision-making by improving the SA of the pilot. Das, et al [124] developed a framework for the rapid development of SA in unknown and/or dynamic environments. Besides the agent's awareness, successful operations require human members to have SA of both the changing situation and the IAs' decision-making processes [125].…”

Section: Transactive Memory Systemmentioning

confidence: 99%

Human–Agent Team Dynamics: A Review and Future Research Opportunities

Iftikhar,

Chiu,

Khan

et al. 2024

IEEE Trans. Eng. Manage.

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Humans teaming with intelligent autonomous agents are becoming indispensable in work environments.However, human-agent teams pose significant challenges as team dynamics are complex arising from the task and social aspects of human-agent interactions. To improve our understanding of human-agent team dynamics, we conducted a systematic literature review. Drawing on Mathieu et al.'s (2019) teamwork model developed for all-human teams, we map the landscape of research to human-agent team dynamics, including structural features, compositional features, mediating mechanisms, and the interplay of the above features and mechanisms. We reveal that the development of human-agent team dynamics is still nascent, with a particular focus on information sharing, trust development, agents' human likeness behaviors, shared cognitions, situation awareness, and function allocation. Gaps remain in many areas of team dynamics, such as team processes, adaptability, shared leadership, and team diversity. We offer various interdisciplinary pathways to advance research on human-agent teams.

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Section: Transactive Memory Systemmentioning

confidence: 99%

Human–Agent Team Dynamics: A Review and Future Research Opportunities

Iftikhar,

Chiu,

Khan

et al. 2024

IEEE Trans. Eng. Manage.

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“…As table I shows, micro-size drones integrate embedded computers, while standard-sized drones can even accommodate desktop processors. Nevertheless, embedded processors can nowadays deliver performance in the order of hundreds of TOp/s and hundreds of GFLOp/s, which has proven to be sufficient to support the full flight stack for mission control, both for micro [17] and standard-size UAVs [22].…”

Section: A Socs For Standard and Micro-sized Uavsmentioning

confidence: 99%

Shaheen: An Open, Secure, and Scalable RV64 SoC for Autonomous Nano-UAVs

Valente,

Veeran,

Sinigaglia

et al. 2023

2023 IEEE Hot Chips 35 Symposium (HCS)

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The rapid advancement of energy-efficient parallel ultra-low-power (ULP) µcontrollers units (MCUs) is enabling the development of autonomous nano-sized unmanned aerial vehicles (nano-UAVs). These sub-10cm drones represent the next generation of unobtrusive robotic helpers and ubiquitous smart sensors. However, nano-UAVs face significant power and payload constraints while requiring advanced computing capabilities akin to standard drones, including real-time Machine Learning (ML) performance and the safe co-existence of general-purpose and real-time OSs. Although some advanced parallel ULP MCUs offer the necessary ML computing capabilities within the prescribed power limits, they rely on small main memories (<1MB) and µcontroller-class CPUs with no virtualization or security features, and hence only support simple bare-metal runtimes. In this work, we present Shaheen, a 9mm 2 200mW SoC implemented in 22nm FDX technology. Differently from state-of-the-art MCUs, Shaheen integrates a Linux-capable RV64 core, compliant with the v1.0 ratified Hypervisor extension and equipped with timing channel protection, along with a low-cost and low-power memory controller exposing up to 512MB of off-chip low-cost low-power HyperRAM directly to the CPU. At the same time, it integrates a fully programmable energy-and area-efficient multi-core cluster of RV32 cores optimized for general-purpose DSP as well as reduced-and mixed-precision ML. To the best of the authors' knowledge, it is the first silicon prototype of a ULP SoC coupling the RV64 and RV32 cores in a heterogeneous host+accelerator architecture fully based on the RISC-V ISA. We demonstrate the capabilities of the proposed SoC on a wide range of benchmarks relevant to nano-UAV applications including general-purpose DSP as well as inference and online learning of quantized DNNs. The cluster can deliver up to 90GOp/s and up to 1.8TOp/s/W on 2-bit integer kernels and up to 7.9GFLOp/s and up to 150GFLOp/s/W on 16-bit FP kernels.

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“…This approach works for homogeneous and non-homogeneous systems alike, because it generalizes the model as a system of particles. Each agent has a case dependent variable, X, so that the probability density function is calculated using [25,[49][50][51][52]:…”

Section: Distributed Behavior Modelmentioning

confidence: 99%

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Smith¹,

Sevil²

2022

Robotics

Self Cite

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The aim of this research effort was to develop a framework for a structure from motion (SfM)-based 3D reconstruction approach with a team of autonomous small unmanned aerial systems (sUASs) using a distributed behavior model. The framework is composed of two major goals to accomplish this: a distributed behavior model for a team of sUASs and a SfM-based 3D reconstruction using team of sUASs. The developed distributed behavior model is based on the entropy of the system, and when the entropy of the system is high, the sUASs get closer to reducing the overall entropy. This is called the grouping phase. If the entropy is less than the predefined threshold, then the sUASs switch to the 3D reconstruction phase. The novel part of the framework is that sUASs are only given the object of interest to reconstruct the 3D model, and they use the developed distributed behavior to coordinate their motion for that goal. A comprehensive parameter analysis was performed, and optimum sets of parameters were selected for each sub-system. Finally, optimum parameters for two sub-systems were combined in a simulation to demonstrate the framework’s operability and evaluate the completeness and speed of the reconstructed model. The simulation results show that the framework operates successfully and is capable of generating complete models as desired, autonomously.

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A Rapid Situational Awareness Development Framework for Heterogeneous Manned-Unmanned Teams

Cited by 15 publications

References 41 publications

Human–Agent Team Dynamics: A Review and Future Research Opportunities

Human–Agent Team Dynamics: A Review and Future Research Opportunities

Shaheen: An Open, Secure, and Scalable RV64 SoC for Autonomous Nano-UAVs

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

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