1,720,975 research outputs found
Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
No full textThe use of unmanned aerial vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system based on a swarm of nano-UAVs, characterized by a limited payload of 35 g and tightly constrained onboard sensing and computing capabilities. Each nano-UAV is equipped with four 64-pixel depth sensors that measure the relative distance to obstacles in four directions. The proposed system merges the information from the swarm and generates a coherent grid map without relying on any external infrastructure. The data fusion is performed using the iterative closest point algorithm and a graph-based simultaneous localization and mapping algorithm, running entirely onboard the UAV's low-power ARM Cortex-M microcontroller with just 192 kB of memory. Field results gathered in three different mazes with a swarm of up to four nano-UAVs prove a mapping accuracy of 12 cm and demonstrate that the mapping time is inversely proportional to the number of agents. The proposed framework scales linearly in terms of communication bandwidth and onboard computational complexity, supporting communication between up to 20 nano-UAVs and mapping of areas up to 180 m2 with the chosen configuration requiring only 50 kB of memory
Structural Health Monitoring System with Narrowband IoT and MEMS Sensors
Full text linkMonitoring of civil infrastructures is critically needed to track aging, damages and ultimately to prevent severe failures which can endanger many lives. The ability to monitor in a continuous and fine-grained fashion the integrity of a wide variety of buildings, referred to as structural health monitoring, with low-cost, long-term and continuous measurements is essential from both an economic and a life-safety standpoint. To address these needs, we propose a low-cost wireless sensor node specifically designed to support modal analysis over extended periods of time with long-range connectivity at low power consumption. Our design uses very cost-effective MEMS accelerometers and exploits the Narrowband IoT protocol (NB-IoT) to establish long-distance connection with 4G infrastructure networks. Long-range wireless connectivity, cabling-free installation and multi-year lifetime are a unique combination of features, not available, to the best of our knowledge, in any commercial or research device. We discuss in detail the hardware architecture and power management of the node. Experimental tests demonstrate a lifetime of more than ten years with a 17000 mAh battery or completely energy-neutral operation with a small solar panel (60 mm × 120 mm). Further, we validate measurement accuracy and confirm the feasibility of modal analysis with the MEMS sensors: compared with a high-precision instrument based on a piezoelectric transducer, our sensor node achieves a maximum difference of 0.08% at a small fraction of the cost and power consumption
SuperBat - Advancing Obstacle Avoidance on Nano-UAVs by Fusing Ultrasonic and Laser-based Time-of-Flight Sensors
No full textNano-Unmanned Aerial Vehicles (UAVs) hold significant promise in various applications, such as exploring ducts or aiding rescue missions in proximity to humans. Their small dimensions make them ideal to operate indoor but pose challenges in terms of payload capacity and onboard computation power. Establishing a robust Obstacle Avoidance (OA) onboard algorithm, able to operate reliably across diverse environments with various materials including transparent and reflective surfaces, is still an open challenge. Recent approaches have pointed to sensor fusion as a path towards reliable OA. In this paper, we fuse a 64-pixel laser-based Time-of-Flight (ToF) sensor (VL53L5CX) and an ultrasonic sensor (ICU-30201) in our computationally lightweight navigation policy to achieve robust and efficient OA. The field tests are based on the Crazyflie 2.1 platform (CF), resulting in a total flight mass of 38 g. Experimental evaluation on the CF shows a reliability of 80 % even in an unexplored indoor environment, including transparent and reflective obstacles, while flying at a maximum speed of 1 m/s. Additionally, the CF is able to fly through a 75 cm narrow corridor with 100% reliability. These findings underscore the efficacy of the fused laser-based ToF and ultrasonic sensors, even under relatively high-speed flight conditions, enabling robust OA on miniaturized robotic platforms
An Energy Optimized JPEG Encoder for Parallel Ultra-Low-Power Processing-Platforms
No full textThe energy autonomy and the lifetime of battery-operated sensors are primary concerns in industrial, healthcare and IoT applications, in particular when a high amount of data needs to be sent wirelessly such as in Wireless Camera Sensors (WCS). Onboard real-time image compression is the appropriate solution to decrease the system’s energy. This paper proposes an optimized algorithm implementation tailored for PULP (Parallel Ultra Low Power) processors, that permits to shrink the image size and the data to transmit. Our optimized JPEG encoder based on a Fast-Discrete Cosine Transform (DCT) function is designed to achieve the best trade-off between energy consumption and image distortion. The parallel software implementation requires only 0.495 mJ per frame and can support up to 80 fps satisfying the most stringent requirements in WCSs applications without requiring a dedicated hardware accelerator
NB-IoT Versus LoRaWAN: An Experimental Evaluation for Industrial Applications
Full text linkLow power and long-range communications are crucial features of the Internet of Things (IoT) paradigm that is becoming essential even for industrial applications. Today, the most promising long-range communication technologies are LoRaWAN and Narrow Band IoT (NB-IoT), which are driving a large IoT ecosystem. In this article, we evaluate the performance of LoRaWAN and NB-IoT with accurate in-field measurements using the same application context for a fair comparison in terms of energy efficiency, lifetime, quality of service, and coverage. The NB-IoT energy transmission is scarcely dependent on the payload length. Thus applications that can tolerate buffering and caching techniques on the node are favored. On the other hand, LoRaWAN consumes 10 × lower energy compared to NB-IoT for occasional and latency-sensitive communications, for which it enables much end-device lifetime. Finally, this paper provides design guidelines for future industrial applications with stringent requirements of long-range and low power wireless connectivity
WindNode: A long-lasting and long-range bluetooth wireless sensor node for pressure and acoustic monitoring on wind turbines
No full textThis paper presents a low power, flexible and energy-efficient wireless sensor node for aerodynamic and acoustic measurements on wind turbine blades and other industrial structures. It comprises 40 high-accuracy absolute MEMS pressure sensors, ten MEMS microphones, a data processing system, a wireless transmitter based on Bluetooth Low Energy 5 tuned for long-range and high throughput while maintaining energy efficiency. The sensor node has been designed and implemented to test the range of communication, the impact on energy efficiency, the functionality, and the estimated lifetime. Experimental tests outdoor in realistic conditions revealed that the system can sustain a data rate of 850kbps over 438m. The node power consumption while streaming all measured data from a multi-MW wind turbine is only 46mW, enabling lifetimes of a full month even in the worst-case scenario of streaming all sensor data using an 8.7Ah Li-Ion battery
H-Watch: An open, connected platform for AI-enhanced CoViD19 infection symptoms monitoring and contact tracing
No full textThe novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch
Ultra-low energy pest detection for smart agriculture
Full text linkApple is one of the most produced fruits in the world because it is easy to grow, store, and transport. The most significant threat of this crop is the attack of the codling moth, a small insect capable of damaging whole orchards in a few days. To prevent this parasite and to plan effective countermeasures, we present an ultra low power smart camera capable of detecting and recognizing the pest in the field; therefore, a wireless alarm can be transmitted over a long distance. The system implements a machine learning approach based on neural networks on the camera board. The sensor is also provided with long-range radio capability and an energy harvester; it permits to operate indefinitely because of its positive energy balance when deployed in the field. Experimental tests on the proposed energy-neutral smart camera demonstrate a validation accuracy of 93% and only 3.5mJ required for image analysis and classification
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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