A Novel Approach for Real Time Monitoring System to Manage UAV Delivery

Yang, Nan; San, Khin Thida; Chang, Yoon Seok

doi:10.1109/iiai-aai.2016.195

Cited by 14 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kim and Morrison [16] present a task scheduling method of UAVs in the scenarios requiring continuous service and multiple docking locations for UAVs to refuel. Yang et al [17] propose a novel real time monitoring system for the UAV delivery, which consists of ground control and UAV autonomous. Ferrandez et al [18] optimize the locations of multiple launch sites and the number of UAVs per truck in the extended truck-UAV package delivery system.…”

Section: Related Workmentioning

confidence: 99%

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

Yuan

Zhu

et al. 2021

IET Communications

View full text Add to dashboard Cite

This paper examines a scheduling problem with heterogeneous logistics unmanned aerial vehicles (UAVs) in urban environment. Different from traditional vehicle routing problem (VRP), it introduces some new characteristics such as the loading capacity, the maximum flight time and the flight speed. As a variant of VRP, the considered scheduling problem is known to be an non-deterministic Polynomial (NP)-hard problem. The UAV scheduling problem model with the heterogeneous UAV settings is formulated first. Secondly, a genetic-based algorithm framework is presented for solving the scheduling problem, in which the encoding/decoding method, the initial population generation method and genetic operations are delicately designed. In order to reduce the search space and faster the execution of this algorithm, a weight-based loading method is adopted. For the purpose of performance evaluation and statistical analysis, the proposed algorithm is compared with the other two existing algorithms. The experimental results show that the presented algorithm can solve this problem efficiently.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Related Workmentioning

confidence: 99%

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

Yuan

Zhu

et al. 2021

IET Communications

View full text Add to dashboard Cite

show abstract

“…The number of applications for unmanned aerial vehicles (UAVs) is widely increasing in the civil arena such as surveillance [1,2], delivery of goods [3,4], power line inspection [5,6], and mapping [7,8].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Wan

Gao

et al. 2020

Sensors

View full text Add to dashboard Cite

Autonomous motion planning (AMP) of unmanned aerial vehicles (UAVs) is aimed at enabling a UAV to safely fly to the target without human intervention. Recently, several emerging deep reinforcement learning (DRL) methods have been employed to address the AMP problem in some simplified environments, and these methods have yielded good results. This paper proposes a multiple experience pools (MEPs) framework leveraging human expert experiences for DRL to speed up the learning process. Based on the deep deterministic policy gradient (DDPG) algorithm, a MEP–DDPG algorithm was designed using model predictive control and simulated annealing to generate expert experiences. On applying this algorithm to a complex unknown simulation environment constructed based on the parameters of the real UAV, the training experiment results showed that the novel DRL algorithm resulted in a performance improvement exceeding 20% as compared with the state-of-the-art DDPG. The results of the experimental testing indicate that UAVs trained using MEP–DDPG can stably complete a variety of tasks in complex, unknown environments.

show abstract

“…Under this bad situation, the efficiency and safety of some tasks, such as rescue, scout and express will be influenced. 1,2 Quadrotor is a kind of small and flexible aircraft which can vertically take off and land. It is also easy to be manipulated and has a low flight cost.…”

Section: Introductionmentioning

confidence: 99%

Cuckoo search-based method for trajectory planning of quadrotor in an urban environment

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.

show abstract

A Novel Approach for Real Time Monitoring System to Manage UAV Delivery

Cited by 14 publications

References 8 publications

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling

Deep Reinforcement Learning Approach with Multiple Experience Pools for UAV’s Autonomous Motion Planning in Complex Unknown Environments

Cuckoo search-based method for trajectory planning of quadrotor in an urban environment

Contact Info

Product

Resources

About