Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T07:22:24.309Z Has data issue: false hasContentIssue false
  • English
  • Français

Motion planning of unmanned aerial vehicles in dynamic 3D space: a potential force approach

Published online by Cambridge University Press:  11 April 2022

Mohammad H. Garibeh ,
Ahmad M. Alshorman Open the ORCID record for Ahmad M. Alshorman [Opens in a new window] ,
Mohammad A. Jaradat ,
Ahmad Bani Younes  and
Maysa Khaleel
Mohammad H. Garibeh
Affiliation:
Department of Mechatronics Engineering, German Jordanian University, Amman, Jordan
Ahmad M. Alshorman*
Affiliation:
Mechanical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
Mohammad A. Jaradat
Affiliation:
Mechanical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan Department of Mechanical Engineering, American University of Sharjah, Sharjah, UAE
Ahmad Bani Younes
Affiliation:
Aerospace Engineering, San Diego State University, San Diego, CA, USA Department of Mechanical Engineering, Al-Hussein Technical University, Amman, Jordan
Maysa Khaleel
Affiliation:
Mechanical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.

Keywords

motion planningunmanned aerial vehiclesdynamic 3D spacepotential force
Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3604 - 3630
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

He, X., Bourne, J. R., Steiner, J. A., Mortensen, C., Hoffman, K. C., Dudley, C. J., Rogers, B., Cropek, D. M. and Leang, K. K., “Autonomous chemical-sensing aerial robot for urban/suburban environmental monitoring,” IEEE Syst. J. 13(3), 3524–3535 (2019).CrossRefGoogle Scholar
Santos, L. C., Santos, F. N., Pires, E. J. S., Valente, A., Costa, P. and Magalhães, S., “Path Planning for Ground Robots in Agriculture: A Short Review,” In: 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (IEEE, 2020) pp. 61–66.CrossRefGoogle Scholar
Li, Z., Xu, Q. and Tam, L. M., “A survey on techniques and applications of window-cleaning robots,” IEEE Access 9, 111518–111532 (2021).CrossRefGoogle Scholar
Burgués, J., Esclapez, M. D., Doñate, S., Pastor, L. and Marco, S., “Aerial mapping of odorous gases in a wastewater treatment plant using a small drone,” Remote Sens. 13(9), 1757 (2021).Google Scholar
Alshorman, A. M., Alshorman, O., Irfan, M., Glowacz, A., Muhammad, F. and Caesarendra, W., “Fuzzy-based fault-tolerant control for omnidirectional mobile robot,” Machines 8(3), 55 (2020).CrossRefGoogle Scholar
Skorobogatov, G., Barrado, C. and Salamí, E., “Multiple UAV systems: A survey,” Unmanned Syst. 8(2), 149–169 (2020).CrossRefGoogle Scholar
Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica 33(3), 463–497 (2015).CrossRefGoogle Scholar
Rasekhipour, Y., Khajepour, A., Chen, S.-K. and Litkouhi, B., “A potential fieldbased model predictive path-planning controller for autonomous road vehicles,” IEEE Trans. Intell. Transp. Syst. 18(5), 1255–1267 (2016).CrossRefGoogle Scholar
Jaradat, M. A. K., Al-Rousan, M. and Quadan, L., “Reinforcement based mobile robot navigation in dynamic environment,” Robot. Comput. Integr. Manuf. 27(1), 135–149 (2011).CrossRefGoogle Scholar
Jaradat, M. A. K., Garibeh, M. H. and Feilat, E. A., “Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field,” Soft Comput. 16(1), 153–164 (2012).CrossRefGoogle Scholar
Mohanan, M. G. and Salgoankar, A., “A survey of robotic motion planning in dynamic environments,” Robot. Auton. Syst. 100(2), 17–185 (2018).CrossRefGoogle Scholar
Zear, A. and Ranga, V., “Path Planning of Unmanned Aerial Vehicles: Current State and Future Challenges,” In: First International Conference on Sustainable Technologies for Computational Intelligence (Springer, 2020) pp. 409–419.Google Scholar
Goerzen, C., Kong, Z. and Mettler, B., “A survey of motion planning algorithms from the perspective of autonomous UAV guidance,” J. Intell. Robot. Syst. 57(1), 65–100 (2010).CrossRefGoogle Scholar
Scherer, S., Singh, S., Chamberlain, L. and Saripalli, S., “Flying Fast and Low among Obstacles,” In: Proceedings 2007 IEEE International Conference on Robotics and Automation (IEEE, 2007) pp. 2023–2029.Google Scholar
Lau, D., Eden, J. and Oetomo, D., “Fluid motion planner for nonholonomic 3-D mobile robots with kinematic constraints,” IEEE Trans. Robot. 31(6), 1537–1547 (2015).CrossRefGoogle Scholar
Cheng, C., Zhu, D., Sun, B., Chu, Z., Nie, J. and Zhang, S., “Path Planning for Autonomous Underwater Vehicle based on Artificial Potential Field and Velocity Synthesis,” In: 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE) (IEEE, 2015) pp. 717–721.Google Scholar
Zhuoning, D., Rulin, Z., Zongji, C. and Rui, Z., “Study on UAV path planning approach based on fuzzy virtual force,” Chinese J. Aeronaut. 23(3), 341–350 (2010).CrossRefGoogle Scholar
Paul, T., Krogstad, T. R. and Gravdahl, J. T., “Modelling of UAV formation flight using 3D potential field,” Simul. Model. Pract. Theory 16(9), 1453–1462 (2008).CrossRefGoogle Scholar
Yin, L. and Yin, Y., “An Improved Potential Field Method for Mobile Robot Path Planning in Dynamic Environments,” In: 2008 7th World Congress on Intelligent Control and Automation (IEEE, 2008) pp. 4847–4852.Google Scholar
Yao, P., Wang, H. and Su, Z., “Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment,” Aerosp. Sci. Technol. 47(6), 269–279 (2015).CrossRefGoogle Scholar
Dong, Z., Chen, Z., Zhou, R. and Zhang, R., “A Hybrid Approach of Virtual Force and a $^{*}$ Search Algorithm for UAV Path Re-Planning,” In: 2011 6th IEEE Conference on Industrial Electronics and Applications (IEEE, 2011) pp. 1140–1145.Google Scholar
Budiyanto, A., Cahyadi, A., Adji, T. B. and Wahyunggoro, O., “UAV Obstacle Avoidance Using Potential Field Under Dynamic Environment,” In: 2015 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC) (IEEE, 2015) pp. 187–192.Google Scholar
NanWang, M. S. and JunWang, T. G.., “A Flow-Field Guided Method of Path Planning for Unmanned Ground Vehicles,” In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC) (IEEE, 2017) pp. 2762–2767.CrossRefGoogle Scholar
Huang, S. and Low, K. H., “A Path Planning Algorithm for Smooth Trajectories of Unmanned Aerial Vehicles via Potential Fields,” In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (IEEE, 2018) pp. 1677–1684.CrossRefGoogle Scholar
Sun, J., Tang, J. and Lao, S., “Collision avoidance for cooperative uavs with optimized artificial potential field algorithm,” IEEE Access 5, 18382–18390 (2017).CrossRefGoogle Scholar
Shi, H., Li, X., Pan, W., Hwang, K.-S. and Li, Z, “Anovel fuzzy three-dimensional grid navigation method for mobile robots,” Int. J. Adv. Robot. Syst. 14(3), 1729881417710444 (2017).CrossRefGoogle Scholar
Schofield, R. T., Wilde, G. A. and Murphy, R. R., “Potential Field Implementation for Move-to-Victim Behavior for a Lifeguard Assistant Unmanned Surface Vehicle,” In: 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (IEEE, 2018) pp. 1–2.CrossRefGoogle Scholar
Jiang, L., Huang, H. and Ding, Z., “Path plan ning for intelligent robots based on deep q-learning with experience replay and heuristic knowledge,” IEEE/CAA J. Autom. Sin. 7(4),  1179–1189 (2019).CrossRefGoogle Scholar
Sun, P. and Yu, Z., “Tracking control for a cushion robot based on fuzzy path planning with safe angular velocity,” IEEE/CAA J. Autom. Sin. 4(4), 610–619 (2017).Google Scholar
Yang, Y., Liao, L., Yang, H. and Li, S., “An optimal control strategy for multi-UAVs target tracking and cooperative competition,” IEEE/CAA J. Autom. Sin. 8(12), 1931–1947 (2020).CrossRefGoogle Scholar
Chuan-ling, L., Jing, T. and Jing-yu, Y., “Path Planning of Mobile Robot Using New Potential Field Method in Dynamic Environments,” In: 2011 Seventh International Conference on Natural Computation (IEEE, vol. 2, 2011) pp. 1011–1014.CrossRefGoogle Scholar
Liang, X., Liu, C., Song, X. and Hao, C., “Research on Mobile Robot Path Planning in Dynamic Environment,” In: 2017 Chinese Automation Congress (CAC) (IEEE, 2017) pp. 3890–3894.CrossRefGoogle Scholar
Morais, C., do Nascimento, T. P., Brito, A. V. and , G Basso, ., “A 3D Anticollision System Based on Artificial Potential Field Method for a Mobile Robot,” In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART ) (SciTePress, 2017) pp. 308–313.CrossRefGoogle Scholar
Albaghdadi, A. F. and Ali, A. A., “3D Path Planning of Fixed and Mobile Environments using Potential Field Algorithm with Genetic Algorithm,” In: 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) (IEEE, 2019) pp. 115–119.CrossRefGoogle Scholar
Macktoobian, M. and Shoorehdeli, M. A., “Time-variant artificial potential field (tapf): A breakthrough in power-optimized motion planning of autonomous space mobile robots,” Robotica 34(5), 1128–1150 (2016).CrossRefGoogle Scholar
Saravanakumar, S. and Asokan, T., “Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space,” Intell. Serv. Robot. 6(4), 211–224 (2013).CrossRefGoogle Scholar
Sfeir, J., Saad, M. and Saliah-Hassane, H., “An Improved Artificial Potential Field Approach to Real-Time Mobile Robot Path Planning in An Unknown Environment,” In: 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE) (IEEE, 2011) pp. 208–213.CrossRefGoogle Scholar
Sabudin, E. N., Omar, R., Joret, A., Ponniran, A., Sulong, M. S., Kadir, H. A. and Debnath, S. K., “Improved Potential Field Method for Robot Path Planning with Path Pruning,” In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 (Springer, 2021) pp. 113–127.Google Scholar
Receveur, J.-B., Victor, S.éphane and Melchior, P., “New interpretation of fractional potential fields for robust path planning,” Fract. Calc. Appl. Anal. 22(1), 113–127 (2019).CrossRefGoogle Scholar
Ruiz, K., Victor, S., Melchior, P. and Chaumette, S., “A new dynamical repulsive fractional potential for UAVs in 3D dynamical environments,” IFAC-PapersOnLine 53(2), 3719–3724 (2020).CrossRefGoogle Scholar
Zhang, F., Gao, X., Xie, Z. and Liu, Y., “Multi-robot Rounding Strategy based on Artificial Potential Field Method in Dynamic Environment,” In: 2019 Chinese Automation Congress (CAC) (IEEE, 2019) pp. 2294–2299.CrossRefGoogle Scholar
Ravankar, A. A., Ravankar, A., Emaru, T. and Kobayashi, Y., “HPPRM: Hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots,” IEEE Access 8, 221743–221766 (2020).Google Scholar
Khatib, O., “Real-time Obstacle Avoidance for Manipulators and Mobile Robots,” In: Autonomous Robot Vehicles, (Springer, 1986) pp. 396–404.CrossRefGoogle Scholar
Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robot. 13(3), 207–222 (2002).CrossRefGoogle Scholar