Drone Stations-Aided Beyond-Battery-Lifetime Flight Planning for Parcel Delivery

Abstract

This paper considers using drones to conduct the last-mile parcel delivery. To enable the beyond-battery-lifetime flight, drone stations are considered to replace or recharge the battery for drones. We focus on the flight planning problem with the goal of minimizing the total travel time from the depot to a customer, a key indicator of the quality of service. We investigate four typical ways for the drone to get extra energy at drone stations: 1) replacing the battery with a fresh one, 2) recharging the battery to the full capacity, 3) recharging the battery to the optimal level, and 4) recharging the battery to the optimal level accounting for the availability of drone stations (i.e., whether a drone station is occupied by other drones). While the first two scenarios can be formulated following the framework of integer linear programming, the last two scenarios turn into mixed-integer nonlinear programming problems. To address the later problems, we present a framework in which discretized state graphs are constructed first and then the optimal paths are found by graph searching algorithms. We propose a dynamic version of Dijkstra's algorithm to deal with the unavailability issue of drone stations. The algorithm can quickly find the optimal flight path for a drone, and extensive computer-based experimental results have been presented to demonstrate the effectiveness of the proposed method.Note to Practitioners-Multi-rotary unmanned aerial vehicles (UAVs), also known as drones, have been regarded as a promising means to reshape future logistics. To save human labour and reduce cost, many giant logistics companies have been dedicated to developing various drones to deliver light and small parcels during the past decade. However, due to the limitation of payload, the battery capacity is constrained, which prevents drones from long-distance flights. Practitioners have tried the drone-vehicle collaboration method, but this still requires human labour to participate. In this paper, we present a framework where drones autonomously conduct long-distance delivery with the assistance of drone stations. It is worth pointing out that such a framework is not to replace the ground delivery method but to serve as an alternative to the ground counterpart for small and light parcels. A particular focus is on the flight planning from the depot to a destination, which includes not only a sequence of drone stations to stop at but also the corresponding rest time to recharge the battery. Several typical scenarios about battery recharging are discussed, and a dynamic version of Dijkstra's algorithm is presented to deal with the challenging case where drone station resources are limited. The presented approach is able to find out the optimal flight plan quickly.

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

“…In addition, the parameters of a UAV and IoT devices are extracted from [10] and [25], which are as follows: C 0 = 3.20 × 10 5 J, C tl = 1.25 × 10 4 J, t tl = 50s, v = 4m/s, P 0 = 300W, L max = 4.27Km, the number of IoT devices in each DCU is set to 300, the amount of data of each IoT device is between 500 MB and 1500 MB, G 0 = −30dB, δ 2 = −250dB, n sd = 10, B = 1MHz, P hp = 1000W, and P i = 0.1W.…”

“…Thus, population-based metaheuristic algorithms have more chances to find better solutions than numerical algorithms in solving complex engineering problems [24]. At present, two main methods support the long-distance flight of a UAV, which are the UAV-vehicle model and the UAV-station model [25]. UAV-vehicle model is the cooperation between the UAV and the vehicle.…”

“…The vehicle can add the energy of a UAV by replacing the battery or providing charging service [26]. UAV-station model can add the energy of a UAV by the deployed charging stations on the ground [25]. Given the uncertainty of the traffic, the UAV-station model outperforms the UAV-vehicle model in terms of reliability.…”

“…Each DCU corresponds to a UAV, whose DCS is the CS closest to the center of the DCU. Thus, 18 UAVs are required, whose DCSs are listed in TableII.In addition, the parameters of a UAV and IoT devices are extracted from[10] and[25], which are as follows: C 0 = 3.20 × 10 5 J, C tl = 1.25 × 10 4 J, t tl = 50s, v = 4m/s, P 0 = 300W, L max = 4.27Km, the number of IoT devices in each DCU is set to 300, the amount of data of each IoT device is between 500 MB and 1500 MB, G 0 = −30dB, δ 2 = −250dB, n sd = 10, B = 1MHz, P hp = 1000W, and P i = 0.1W.To verify the performance of EDDE-DPDE, it is compared with DE-DEVIPS, BA-DEVIPS, PSO-DEVIPS, and JAYA-DEVIPS as shown in TableIII. In TableIII, DE, bat algorithm (BA)[32], particle swarm optimization (PSO)[33], and Jaya algorithm (JAYA)[34] are four popular population-based metaheuristic algorithms.…”

Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

“…Trajectory design is one of the major challenges in drone-based delivery systems as it helps reducing the total travel time. Due to diverse delivery locations and charging points, it is often challenging to find the optimal trajectory that can minimize the overall delivery time and enable the drone to successfully fly back to the depot [ 31 ]. Analyzing the drone’s trajectory data and optimizing it is an NP-hard problem, where NP stands for non-deterministic polynomial-time.…”