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1. UAV-based Networked Airborne Computing Simulator and Testbed Design and Implementation

   https://doi.org/10.1109/ICUAS57906.2023.10156100  

   Wang, Baoqian; Xie, Junfei; Ma, Ke; Wan, Yan (June 2023, 2023 International Conference on Unmanned Aircraft Systems (ICUAS))

   The integration of onboard computing capabilities with unmanned aerial vehicles (UAV) has gained significant attention in recent years as part of mobile computing paradigms such as mobile edge computing (MEC), fog computing, and mobile cloud computing. To enhance the performance of airborne computing, networked airborne computing (NAC) aims to interconnect UAVs through direct flight-to-flight links, with UAVs sharing resources with each other. However, despite the growing interest in NAC and UAV-based computing, existing studies rely heavily on numerical simulations for performance evaluation and lack realistic simulators and hardware testbeds. To fill this gap, this paper presents the development of two NAC platforms: a realistic simulator based on ROS and Gazebo, and a hardware testbed with multiple UAVs communicating and sharing computing resources. Through simulation and real flight tests with two computation applications, we evaluate the platforms and examine the impact of mobility on NAC performance. Our findings offer valuable insights into NAC and provide guidance for future advancements. 

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2. Collaborative multi-robot multi-human teams in search and rescue.

   Williams, R. (April 2020, Proceedings of the International ISCRAM Conference)

   Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy. 

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3. Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing

   https://doi.org/10.1145/3453142.3491419  

   Pinyarash Pinyoanuntapong; Tagore Pothuneedi; Ravikumar Balakrishnan; Minwoo Lee; Chen Chen; Pu Wang (February 2022, 2021 IEEE/ACM Symposium on Edge Computing (SEC))

   Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents FedEdge simulator, a high-fidelity Linux-based simulator, which enables fast prototyping, sim-to-real code, and knowledge transfer for multi-hop FL systems. FedEdge simulator is built on top of the hardware-oriented FedEdge experimental framework with a new extension of the realistic physical layer emulator. This emulator exploits trace-based channel modeling and dynamic link scheduling to minimize the reality gap between the simulator and the physical testbed. Our initial experiments demonstrate the high fidelity of the FedEdge simulator and its superior performance on sim-to-real knowledge transfer in reinforcement learning -optimized multi-hop FL 

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4. Interference Analysis and Mitigation for UAV Communications in Drone Corridors

   https://doi.org/10.1109/WPMC59531.2023.10338970  

   Ellis, Cameron; Zhang, Minglong; Marojevic, Vuk (November 2023, IEEE)

   Unmanned aerial vehicles (UAVs) have witnessed widespread adoption in the modern world, with their development set to continue into the future. As UAV technology and applications advance, it becomes imperative to understand their communication capabilities. UAVs experience distinct radio propagation conditions compared to ground-based radio nodes, necessitating a critical investigation into aerial radio node performance. This paper analyzes interference in UAV-to-UAV (U2U) communications within drone corridors and proposes an interference mitigation strategy utilizing millimeter wave (mmWave) beamforming. Employing a semi-persistent scheduling approach from the Third Generation Partnership Project (3GPP) sidelink communications for low altitude aerial nodes in drone corridors, the study primarily examines interference from drone clusters within designated air corridors. To assess U2U communication performance, a 3GPP standard-compliant cross-layer simulator is developed. Simulation results demonstrate that employing mmWave beamforming instead of isotropic transmission substantially reduces interference, leading to higher communications reliability and enabling more UAVs to occupy and communicate in the airspace. 

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5. uavEE: A Modular, Power-Aware Emulation Environment for Rapid Prototyping and Testing of UAV

   M. Theile, O. Dantsker (January 2018, Proceedings of IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA))

   State of the art design and testing of avionics for unmanned aircraft is an iterative process that involves many test flights, interleaved with multiple revisions of the flight management software and hardware. To significantly reduce flight test time and software development costs, we have developed a real-time UAV Emulation Environment (uavEE) using ROS that interfaces with high fidelity simulators to simulate the flight behavior of the aircraft. Our uavEE emulates the avionics hardware by interfacing directly with the embedded hardware used in real flight. The modularity of uavEE allows the integration of countless test scenarios and applications. Furthermore, we present an accurate data driven approach for modeling of propulsion power of fixed-wing UAVs, which is integrated into uavEE. Finally, uavEE and the proposed UAV Power Model have been experimentally validated using a fixed-wing UAV testbed. 

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