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.

The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential not only to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (terrestrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this paper, we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle, and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks.

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.

Finding the cost-optimal battery size in the context of parcel delivery with Electric Vehicles (EVs) requires solving a tradeoff between using the largest possible battery (so as to maximize the number of deliveries over a given time) and the relative costs (initial investment plus the unnecessary increase of the truck weight during delivery). In this paper, we propose a framework for the optimal battery sizing for parcel delivery with an electric truck; we implement an electric truck simulator including a nonlinear battery model to evaluate revenue, battery cost, charging cost, and overall profit for annual delivery. Our framework finds the cost-optimal battery size for different parcel weight distributions and customer location distributions. We analyze the effect of battery sizing on the profit, which is up to 56%.