Drones, typically unmanned aerial vehicles (UAVs), have many purposes. However, simultaneous operation of multiple drones is challenging, considering the real-time interactions and the environment; the drone must avoid collision with the other drones or obstacles. The proposed Advanced TD3 model performs energy-efficient path planning at the edge-level drone. We modify the twin-delayed deep deterministic policy gradient (TD3), which is the state-of-the-art policy gradient reinforcement learning. The frame stacking technique considers the continuous action space of the drone to the TD3 model. During the training, we gradually increase the observation range of agents for fast and stable convergence. We train the modified TD3 model through Offline RL to reduce the overhead for the RL model training. Drones mount the converged RL model on their onboard computer. The Advanced TD3 model in the drones selects an energy-efficient path without the overhead of the training process of the RL model in real-time, considering external factors such as wind or another drone. The total energy consumption of drones in flight along with online path planning is approximately 106% of the total energy consumption of drones that follow offline path planning, even though the trained TD3 model does not require complex computations for real-time execution.

Rotary unmanned aerial vehicles (UAVs), also known as drones, have various advantages, yet their actual applications are limited owing to their flight range. However, increasing the flight range by enhancing the hardware is a challenging task. In this study, we introduce the first step of systematic drone low-power optimization based on the framework of electronic design automation (EDA). We attempt drone power management without in-depth knowledge of aerodynamics and control theory. Instead, we introduce a novel power model of drones using physical parameters that can affect power consumption, such as the three-axis velocity and acceleration, drone height, wind velocity, and the payload's weight and volume. We detail the experimental setup, power modeling, accuracy verification, and optimization for minimum energy paths. We achieved over 90% accuracy in power modeling without depending on aerodynamics. The proposed approach shows the feasibility of energy-aware rotary UAV flight trajectory optimization considering the external forces affecting drones such as wind. The proposed method presents up to 24.01% energy saving through path changes considering external forces.

Drones have been attracting the attention of diverse industries thanks to their superior maneuverability. Logistics companies especially keep trying to utilize drones for fast delivery following the growing market size of e-commerce. Accordingly, methods for safely operating multi-drone have been researched, and many researchers have proposed various optimal or near-optimal routing methods. However, such methods have some problems that cause routing failures or huge routing computation time in a drone-dense space due to many collisions. In this paper, we propose a centralized framework that deals with enormous collisions and obtains collision-free paths rapidly. We first build a drone energy consumption model with a data-driven method using flight experiment data of a commercial drone to estimate the drone battery state-of-charge (SoC). Then, we develop a novel routing method that generates collision-free paths by considering both the congestion of the space and the SoC of each drone. The proposed method is inspired by the VLSI circuit routing method that connects all signal nets among thousands of logic components. Through numerous delivery routing simulations, we confirm that the proposed method achieves a maximum of 6 times higher routing success rate with a 10x faster runtime compared with the state-of-the-art optimal method. In addition, we validate that the proposed method is applicable to delivery routing problems with various drone battery capacities.

A thermoelectric generator (TEG) is a clean electricity generator from a heat source, usually waste heat. However, it is not as widely utilized as other electricity generators due to low conversion efficiency from heat to electricity. One approach is a system-level net power optimization for a TEG system composed of TEGs, heat sink, and fans. In this paper, we propose airflow reuse after cooling preceding TEGs to maximize system net power. For the accurate system net power, we model the TEG system, air, and heat source with proper dimension and material characteristics, and simulate with a computational fluid dynamics program. Next, the TEG power generation and the fan power consumption are calculated in consideration of the Seebeck coefficient and internal electrical resistance varying with hot and cold side temperatures. Finally, we find the optimal number of TEGs and fan speed generating the most efficient system net power in various TEG systems. The results show that the system with a side fan with a specific number of TEGs provides a system net power up to 58.6% higher than when with a top fan. The most efficient system net power with the side fan increases up to four TEGs generating 1.907 W at 13,000 RPM.

Metal gate technology is one of the most important methods used to increase the low on-current of tunnel field-effect transistors (TFETs). However, metal gates have different work-functions for each grain during the deposition process, resulting in work-function variation (WFV) effects, which means that the electrical characteristics vary from device to device. The WFV of a planar TFET, double-gate (DG) TFET, and electron-hole bilayer TFET (EHBTFET) were examined by technology computer-aided design (TCAD) simulations to analyze the influences of device structure and to find strategies for suppressing the WFV effects in TFET. Comparing the WFV effects through the turn-on voltage (V_turn-on) distribution, the planar TFET showed the largest standard deviation (σV_turn-on) of 20.1 mV, and it was reduced by −26.4% for the DG TFET and −80.1% for the EHBTFET. Based on the analyses regarding metal grain distribution and energy band diagrams, the WFV of TFETs was determined by the number of metal grains involved in the tunneling current. Therefore, the EHBTFET, which can determine the tunneling current by all of the metal grains where the main gate and the sub gate overlap, is considered to be a promising structure that can reduce the WFV effect of TFETs.