A COTS-based Lightweight, Low-power and Versatile Companion Computer for Nano UAVs

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Publicado en:Sensors & Transducers vol. 268, no. 1 (Apr 2025), p. 37
Autor principal: Okudera, Rintaro
Otros Autores: Li, Yixiao, Matsubara, Yutaka, Takada, Hiroaki
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IFSA Publishing, S.L.
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100 1 |a Okudera, Rintaro  |u Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan 
245 1 |a A COTS-based Lightweight, Low-power and Versatile Companion Computer for Nano UAVs 
260 |b IFSA Publishing, S.L.  |c Apr 2025 
513 |a Journal Article 
520 3 |a Recent advances in Unmanned Aerial Vehicles (UAVs) have driven a rapid expansion in their application domains, ranging from logistics and surveillance to disaster relief and entertainment. In particular, Nano UAVs (weight: under 100 g) offer significant advantages in cost, safety, and regulatory compliance. However, their limited payload capacity and the high-power consumption of conventional control systems severely constrain flight time. Traditional companion computers designed for computationally intensive tasks, such as image processing, often require 2-5 times more power and nearly double the weight of the onboard flight controller, further exacerbating these limitations. In this study, we present a novel companion computer based on Sonys Spresense platform that is lightweight, low-power, and highly versatile. Weighing only 7 grams and featuring a POSIX [compliant RTOS alongside a multi core architecture, the proposed solution is optimally tailored for Nano UAV applications. Extensive Hardware Jin the (Loop (HITL) evaluations demonstrate that our Spresense based system consumes only 1/18 the power of the Raspberry Pi 4 Model B while delivering comparable functionality. Moreover, real Nano UAV tests using the Nano Mind 110 (weight: 36 g) confirm that integrating our companion computer results in only a modest increase in overall power consumption, thus preserving flight time. Compared to current state ofl the Jart approaches, our design effectively addresses challenges in availability, extensibility, and ease of development, offering a cost effective and practical alternative for Nano UAV systems. Future work will extend these findings through further real world validations, including advanced navigation, obstacle avoidance, and ROSfbased applications, to confirm the robustness and scalability of our approach. 
610 4 |a Raspberry Pi Ltd 
653 |a Software 
653 |a Cameras 
653 |a Computers 
653 |a Control algorithms 
653 |a Disaster relief 
653 |a Flight time 
653 |a Flight control systems 
653 |a Unmanned aerial vehicles 
653 |a Sensors 
653 |a Controllers 
653 |a Consumption 
653 |a Design 
653 |a Drones 
653 |a Power consumption 
653 |a Image processing 
653 |a Obstacle avoidance 
653 |a Linux 
653 |a Control systems 
653 |a Logistics 
653 |a Image processing systems 
700 1 |a Li, Yixiao  |u Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan 
700 1 |a Matsubara, Yutaka  |u Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan 
700 1 |a Takada, Hiroaki  |u Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan 
773 0 |t Sensors & Transducers  |g vol. 268, no. 1 (Apr 2025), p. 37 
786 0 |d ProQuest  |t Science Database 
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