Abstract
Designing collision-aware routing (path planning) protocols for UAV (Unmanned Aerial Vehicle) Networks requires multimodal analysis of various network and node-level parameter sets. These include node-to-node distance, energy constraints, communication constraints, QoS (Quality of Service) constraints, etc. Existing collision-aware UAV routing models are either highly complex or have lower efficiency, which limits their deployment abilities. Moreover, these models usually do not consider energy constraints and are applied to static targets. To overcome these limitations, this article gave an idea about the design of a novel hybrid bioinspired model. The proposed model initially collects node-level and network-level parametric sets that include Cartesian location, residual energy levels, temporal routing performance, and temporal collision performance levels. The model then deploys a Grey Wolf Optimization (GWO) based routing process to identify optimal routes between two anchor points. The routes are again tuned via a Firefly based Optimization (FFO) which assists in estimating high-trust routes based on their temporal performance via continuous data update operations. The selected route sets are further scrutinized via a continuous learning framework (CLF), which assists in the identification of dynamic moving targets, and uses this information for incremental route updates. Due to the integration of CLF, the model can identify optimal paths even under moving target scenarios. The model was validated under multiscale networks, and its performance was evaluated in terms of collision avoidance accuracy, routing delay, energy requirements, and computational complexity levels w.r.t. dynamic scenarios. This performance was compared with various state-of-the-art methods, and it was seen that the proposed model has 10.5% lower routing delay, with 8.3% lower energy consumption, and 23.9% lower collisions while maintaining lower computational complexity. Due to these enhancements, the proposed model can deploy a wide variety of real-time UAV network scenarios.
Similar content being viewed by others
Data availability
Not applicable.
References
Airlangga G, Liu A (2022) Online path planning framework for UAV in rural areas. IEEE Access 10:37572–37585. https://doi.org/10.1109/ACCESS.2022.3164505
Akbar R, Prager S, Silva AR, Moghaddam M, Entekhabi D (2022) Wireless sensor network informed UAV path planning for soil moisture mapping. IEEE Trans Geosci Remote Sens 60:1–13. https://doi.org/10.1109/TGRS.2021.3088658
Bono Rossello N, Carpio RF, Gasparri A, Garone E (2022) Information-driven path planning for UAV with limited autonomy in large-scale field monitoring. IEEE Trans Autom Sci Eng 19(3):2450–2460. https://doi.org/10.1109/TASE.2021.3085365
Bruggemann T (2022) Automated feature-driven flight planning for airborne inspection of large linear infrastructure assets. IEEE Trans Autom Sci Eng 19(2):804–817. https://doi.org/10.1109/TASE.2021.3062154
Cao Y, Cheng X, Mu J (2022) Concentrated coverage path planning algorithm of UAV formation for aerial photography. IEEE Sens J 22(11):11098–11111. https://doi.org/10.1109/JSEN.2022.3168840
Chang H, Chen Y, Zhang B, Doermann D (2022) Multi-UAV mobile edge computing and path planning platform based on reinforcement learning. IEEE Trans Emerg Top Comput Intell 6(3):489–498. https://doi.org/10.1109/TETCI.2021.3083410
Chen J, Zhang Y, Wu L, You T, Ning X (2022) An adaptive clustering-based algorithm for automatic path planning of heterogeneous UAVs. IEEE Trans Intell Transp Syst 23(9):16842–16853. https://doi.org/10.1109/TITS.2021.3131473
Cheng Z, Zhao L, Shi Z (2022) Decentralized multi-UAV path planning based on two-layer coordinative framework for formation rendezvous. IEEE Access 10:45695–45708. https://doi.org/10.1109/ACCESS.2022.3170583
Cui Z, Wang Y (2021) UAV path planning based on multi-layer reinforcement learning technique. IEEE Access 9:59486–59497. https://doi.org/10.1109/ACCESS.2021.3073704
Dong Y, He C, Wang Z, Zhang L (2022) Radio map assisted path planning for UAV Anti-jamming communications. IEEE Signal Process Lett 29:607–611. https://doi.org/10.1109/LSP.2022.3149374
Du B, Chen J, Sun D, Manyam SG, Casbeer DW (2022) UAV trajectory planning with probabilistic geo-fence via iterative chance-constrained optimization. IEEE Trans Intell Transp Syst 23(6):5859–5870. https://doi.org/10.1109/TITS.2021.3060377
Duan C, Feng J, Chang H (2021a) Meteorology-aware path planning for the UAV based on the improved intelligent water drops algorithm. IEEE Access 9:49844–49856. https://doi.org/10.1109/ACCESS.2021.3068972
Duan H, Zhao J, Deng Y, Shi Y, Ding X (2021b) Dynamic discrete pigeon-inspired optimization for Multi-UAV cooperative search-attack mission planning. IEEE Trans Aerosp Electron Syst 57(1):706–720
Feng J, Zhang J, Zhang G, Xie S, Ding Y, Liu Z (2021) UAV dynamic path planning based on obstacle position prediction in an unknown environment. IEEE Access 9:154679–154691. https://doi.org/10.1109/ACCESS.2021.3128295
Guo Y, You C, Yin C, Zhang R (2021) UAV trajectory and communication co-design: flexible path discretization and path compression. IEEE J Sel Areas Commun 39(11):3506–3523. https://doi.org/10.1109/JSAC.2021.3088690
Hsu Y-H, Gau R-H (2022) Reinforcement learning-based collision avoidance and optimal trajectory planning in UAV communication networks. IEEE Trans Mob Comput 21(1):306–320
Huang H, Savkin AV, Huang C (2021) Reliable path planning for drone delivery using a stochastic time-dependent public transportation network. IEEE Trans Intell Transp Syst 22(8):4941–4950. https://doi.org/10.1109/TITS.2020.2983491
Huang H, Savkin AV, Ni W (2022) Online UAV trajectory planning for covert video surveillance of mobile targets. IEEE Trans Autom Sci Eng 19(2):735–746. https://doi.org/10.1109/TASE.2021.3062810
Jensen-Nau KR, Hermans T, Leang KK (2021) Near-optimal area-coverage path planning of energy-constrained aerial robots with application in autonomous environmental monitoring. IEEE Trans Autom Sci Eng 18(3):1453–1468. https://doi.org/10.1109/TASE.2020.3016276
Jinqiang H, Husheng W, Renjun Z, Rafik M, Xuanwu Z (2021) Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior. J Syst Eng Electron 32(6):1463–1476. https://doi.org/10.23919/JSEE.2021.000124
Lee J, Friderikos V (2022) Interference-aware path planning optimization for multiple UAVs in beyond 5G networks. J Commun Netw 24(2):125–138. https://doi.org/10.23919/JCN.2022.000006
Li D, Yin W, Wong WE, Jian M, Chau M (2022) Quality-oriented hybrid path planning based on A* and Q-learning for unmanned aerial vehicle. IEEE Access 10:7664–7674. https://doi.org/10.1109/ACCESS.2021.3139534
Liu Q, Zhang Y, Li M, Zhang Z, Cao N, Shang J (2021) Multi-UAV path planning based on fusion of sparrow search algorithm and improved bioinspired neural network. IEEE Access 9:124670–124681. https://doi.org/10.1109/ACCESS.2021.3109879
Liu H, Li X, Fan M, Wu G, Pedrycz W, NagaratnamSuganthan P (2022) An autonomous path planning method for unmanned aerial vehicle based on a tangent intersection and target guidance strategy. IEEE Trans Intell Transport Syst 23(4):3061–3073. https://doi.org/10.1109/TITS.2020.3030444
Niu G, Wu L, Gao Y, Pun M-O (2022) Unmanned aerial vehicle (UAV)-assisted path planning for unmanned ground vehicles (UGVs) via disciplined convex-concave programming. IEEE Trans Veh Technol 71(7):6996–7007. https://doi.org/10.1109/TVT.2022.3168574
Pan Y, Yang Y, Li W (2021) A deep learning trained by genetic algorithm to improve the efficiency of path planning for data collection with multi-UAV. IEEE Access 9:7994–8005. https://doi.org/10.1109/ACCESS.2021.3049892
Pan Z, Zhang C, Xia Y, Xiong H, Shao X (2022) An improved artificial potential field method for path planning and formation control of the multi-UAV systems. IEEE Trans Circuits Syst II Express Briefs 69(3):1129–1133. https://doi.org/10.1109/TCSII.2021.3112787
Peng C, Huang X, Wu Y, Kang J (2022) Constrained multi-objective optimization for UAV-enabled mobile edge computing: offloading optimization and path planning. IEEE Wireless Commun Lett 11(4):861–865
Qadir Z, Zafar MH, Moosavi SKR, Le KN, Mahmud MAP (2022) Autonomous UAV path-planning optimization using metaheuristic approach for predisaster assessment. IEEE Internet Things J 9(14):12505–12514. https://doi.org/10.1109/JIOT.2021.3137331
Roberge V, Tarbouchi M (2021) Multiunmanned aerial vehicle path planner on graphics processing unit. IEEE Can J Electr Comput Eng 44(3):364–375. https://doi.org/10.1109/ICJECE.2021.3088294
Sanchez-Fernandez AJ, Romero LF, Bandera G, Tabik S (2022) VPP: visibility-based path planning heuristic for monitoring large regions of complex terrain using a UAV onboard camera. IEEE J Sel Top Appl Earth Observ Remote Sens 15:944–955. https://doi.org/10.1109/JSTARS.2021.3134948
Shao S, He C, Zhao Y, Wu X (2021) Efficient trajectory planning for UAVs using hierarchical optimization. IEEE Access 9:60668–60681. https://doi.org/10.1109/ACCESS.2021.3073420
Shao X-X, Gong Y-J, Zhan Z-H, Zhang J (2022) Bipartite cooperative coevolution for energy-aware coverage path planning of UAVs. IEEE Transactions on Artificial Intelligence 3(1):29–42. https://doi.org/10.1109/TAI.2021.3103143
Shen K, Shivgan R, Medina J, Dong Z, Rojas-Cessa R (2022) Multidepot drone path planning with collision avoidance. IEEE Internet Things J 9(17):16297–16307. https://doi.org/10.1109/JIOT.2022.3151791
Shiri H, Seo H, Park J, Bennis M (2022) Attention-based communication and control for multi-UAV path planning. IEEE Wireless Commun Lett 11(7):1409–1413. https://doi.org/10.1109/LWC.2022.3171602
Vashisth A, Batth RS (2020) An overview, survey, and challenges in UAVs communication network. In: 2020 International conference on intelligent engineering and management (ICIEM), London, UK, pp 342–347. https://doi.org/10.1109/ICIEM48762.2020.9160197
Vashisth A, Singh Batth R, Ward R (2021) Existing path planning techniques in unmanned aerial vehicles (UAVs): a systematic review. In: 2021 International conference on computational intelligence and knowledge economy (ICCIKE), Dubai, United Arab Emirates, pp 366–372. https://doi.org/10.1109/ICCIKE51210.2021.9410787
Vashisth A, Singh B, Batth RS (2023) QMRNB: design of an efficient Q-learning model to improve routing efficiency of UAV networks via bioinspired optimizations. Int J Comput Netw Appl (IJCNA) 10(2):256–264. https://doi.org/10.22247/ijcna/2023/220740
Wang X, Gursoy MC, Erpek T, Sagduyu YE (2022) Learning-based UAV path planning for data collection with integrated collision avoidance. IEEE Internet Things J 9(17):16663–16676. https://doi.org/10.1109/JIOT.2022.3153585
Wu Y, Wu S, Hu X (2021) Cooperative path planning of UAVs & UGVs for a persistent surveillance task in urban environments. IEEE Internet Things J 8(6):4906–4919. https://doi.org/10.1109/JIOT.2020.3030240
Wu T et al (2022) A novel AI-based framework for AoI-optimal trajectory planning in UAV-assisted wireless sensor networks. IEEE Trans Wireless Commun 21(4):2462–2475. https://doi.org/10.1109/TWC.2021.3112568
Xie H, Yang D, Xiao L, Lyu J (2021) Connectivity-aware 3D UAV path design with deep reinforcement learning. IEEE Trans Veh Technol 70(12):13022–13034. https://doi.org/10.1109/TVT.2021.3121747
Xu F et al (2021a) Heuristic path planning method for multistatic UAV-borne SAR imaging system. IEEE J Sel Top Appl Earth Observ Remote Sens 14:8522–8536. https://doi.org/10.1109/JSTARS.2021.3106449
Xu H, Jiang S, Zhang A (2021b) Path planning for unmanned aerial vehicle using a mix-strategy-based gravitational search algorithm. IEEE Access 9:57033–57045. https://doi.org/10.1109/ACCESS.2021.3072796
Zhang S, Zhang R (2021) Radio map-based 3D path planning for cellular-connected UAV. IEEE Trans Wireless Commun 20(3):1975–1989. https://doi.org/10.1109/TWC.2020.3037916
Zhang W, Zhang S, Wu F, Wang Y (2021) Path planning of UAV based on improved adaptive grey wolf optimization algorithm. IEEE Access 9:89400–89411. https://doi.org/10.1109/ACCESS.2021.3090776
Zhao C, Liu J, Sheng M, Teng W, Zheng Y, Li J (2021) Multi-UAV trajectory planning for energy-efficient content coverage: a decentralized learning-based approach. IEEE J Sel Areas Commun 39(10):3193–3207. https://doi.org/10.1109/JSAC.2021.3088669
Zhou X, Gao F, Fang X, Lan Z (2021) Improved bat algorithm for UAV path planning in three-dimensional space. IEEE Access 9:20100–20116. https://doi.org/10.1109/ACCESS.2021.3054179
Funding
The Laboratory Work Research Project of Zhejiang Higher Education Association (Grant no. D202201).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Vashisth, A., Singh, B., Garg, R. et al. BPACAR: design of a hybrid bioinspired model for dynamic collision-aware routing with continuous pattern analysis in UAV networks. Microsyst Technol 30, 411–421 (2024). https://doi.org/10.1007/s00542-023-05547-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-023-05547-1