Path Planning for Autonomous Drones: Challenges and Future Directions

被引:65
作者
Gugan, Gopi [1 ]
Haque, Anwar [1 ]
机构
[1] Univ Western Ontario, Dept Comp Sci, London, ON N6A 5B7, Canada
关键词
unmanned aerial vehicles (UAV); autonomous drones; flight path planning; UNMANNED AERIAL VEHICLE; OBSTACLE AVOIDANCE; ALGORITHM; SWARM; NAVIGATION; DESIGN; RISK; UAVS;
D O I
10.3390/drones7030169
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Unmanned aerial vehicles (UAV), or drones, have gained a lot of popularity over the last decade. The use of autonomous drones appears to be a viable and low-cost solution to problems in many applications. Path planning capabilities are essential for autonomous control systems. An autonomous drone must be able to rapidly compute feasible and energy-efficient paths to avoid collisions. In this study, we review two key aspects of path planning: environmental representation and path generation techniques. Common path planning techniques are analyzed, and their key limitations are highlighted. Finally, we review thirty-five highly cited publications to identify current trends in drone path planning research. We then use these results to identify factors that need to be addressed in future studies in order to develop a practical path planner for autonomous drones.
引用
收藏
页数:18
相关论文
共 100 条
[1]  
Abdilla A, 2015, IEEE INT C INT ROBOT, P675, DOI 10.1109/IROS.2015.7353445
[2]   Comprehensive Energy Consumption Model for Unmanned Aerial Vehicles, Based on Empirical Studies of Battery Performance [J].
Abeywickrama, Hasini Viranga ;
Jayawickrama, Beeshanga Abewardana ;
He, Ying ;
Dutkiewicz, Eryk .
IEEE ACCESS, 2018, 6 :58383-58394
[3]  
aboutamazon, DRONE PROGRAM TAKING
[4]   Efficient Energy Flight Path Planning Algorithm Using 3-D Visibility Roadmap for Small Unmanned Aerial Vehicle [J].
Ahmad, Zahoor ;
Ullah, Farman ;
Tran, Cong ;
Lee, Sungchang .
INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING, 2017, 2017
[5]   A Survey on UAV Computing Platforms: A Hardware Reliability Perspective [J].
Ahmed, Foisal ;
Jenihhin, Maksim .
SENSORS, 2022, 22 (16)
[6]  
Al-Sabban WH, 2013, IEEE INT CONF ROBOT, P784, DOI 10.1109/ICRA.2013.6630662
[7]   Autonomous Recharging and Flight Mission Planning for Battery-Operated Autonomous Drones [J].
Alyassi, Rashid ;
Khonji, Majid ;
Karapetyan, Areg ;
Chau, Sid Chi-Kin ;
Elbassioni, Khaled ;
Tseng, Chien-Ming .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 20 (02) :1034-1046
[8]  
[Anonymous], FAA aerospace forecast fiscal years 2018-2038
[9]   Drone Deep Reinforcement Learning: A Review [J].
Azar, Ahmad Taher ;
Koubaa, Anis ;
Ali Mohamed, Nada ;
Ibrahim, Habiba A. ;
Ibrahim, Zahra Fathy ;
Kazim, Muhammad ;
Ammar, Adel ;
Benjdira, Bilel ;
Khamis, Alaa M. ;
Hameed, Ibrahim A. ;
Casalino, Gabriella .
ELECTRONICS, 2021, 10 (09)
[10]  
Boskovic JD, 2002, P AMER CONTR CONF, V1-6, P1825, DOI 10.1109/ACC.2002.1023832