Abstract
Intelligent 3-D path planning is a crucial aspect of an unmanned aerial vehicle's (UAVs) autonomous flight system. In this chapter, we propose a two-step centralized system for developing a 3-D path-planning for a swarm of UAVs. We trace the UAV position while simultaneously constructing an incremental and progressive map of the environment using visual simultaneous localization and mapping (V-SLAM) method. We introduce a corner-edge points matching mechanism for stabilizing the V-SLAM system in the least extracted map points. In this instance, a single UAV performs the function using monocular vision for mapping an area of interest. We use the particle swarm optimization (PSO) algorithm to optimize paths for multi-UAVs. We also propose a path updating mechanism based on region sensitivity (RS) to avoid sensitive areas if any hazardous events are detected during the execution of the final path. Moreover, the dynamic fitness function (DFF) is developed to evaluate path planning performance while considering various optimization parameters such as flight risk estimation, energy consumption, and operation completion time. This system achieves high fitness value and safely arrives at the destination while avoiding collisions and restricted areas, which validates the efficiency of proposed PSO-VSLAM system as demonstrated by simulation results.
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References
Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ (2016) Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans Rob 32(6):1309–1332
Trujillo JC, Munguia R, Guerra E, Grau A (2018) Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments. Sensors 18(5):1351
Du H, Wang W, Xu C, Xiao R, Sun C (2020) Real-time onboard 3D state estimation of an unmanned aerial vehicle in multi-environments using multi-sensor data fusion. Sensors 20(3):919
Ramezani M, Tinchev G, Iuganov E, Fallon M (May 2020) Online LiDAR-SLAM for legged robots with robust registration and deep-learned loop closure. In: 2020 IEEE international conference on robotics and automation (ICRA), pp 4158–4164
Stentz A, Fox D, Montemerlo M (2003) Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association. In: Proceedings of the AAAI national conference on artificial intelligence
Loo SY, Mashohor S, Tang SH, Zhang H (2020) DeepRelativeFusion: dense monocular SLAM using single-image relative depth prediction. arXiv:2006.04047
Shakhatreh H, Sawalmeh AH, Al-Fuqaha A, Dou Z, Almaita E, Khalil I, Othman NS, Khreishah A, Guizani M (2019) Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7:48572–48634
Mughal UA, Xiao J, Ahmad I, Chang K (2020) Cooperative resource management for C-V2I communications in a dense urban environment. Veh Commun 26:100282
Mughal UA, Ahmad I, Chang K (2019) Virtual cells operation for 5G V2X communications. In: Proceedings of KICS, pp 1486–1487
Shakoor S, Kaleem Z, Baig MI, Chughtai O, Duong TQ, Nguyen LD (2019) Role of UAVs in public safety communications: energy efficiency perspective. IEEE Access 7:140665–140679
Wen S, Zhao Y, Yuan X, Wang Z, Zhang D, Manfredi L (2020) Path planning for active SLAM based on deep reinforcement learning under unknown environments. Intell Serv Robot 1–10
Kalogeiton VS, Ioannidis K, Sirakoulis GC, Kosmatopoulos EB (2019) Real-time active SLAM and obstacle avoidance for an autonomous robot based on stereo vision. Cybern Syst 50(3):239–260
Doitsidis L, Weiss S, Renzaglia A, Achtelik MW, Kosmatopoulos E, Siegwart R, Scaramuzza D (2012) Optimal surveillance coverage for teams of micro aerial vehicles in GPS-denied environments using onboard vision. Auton Robot 33(1):173–188
Alzugaray I, Teixeira L, Chli M (May 2017) Short-term UAV path-planning with monocular-inertial SLAM in the loop. In: 2017 IEEE international conference on robotics and automation (ICRA), pp 2739–2746
Sánchez-García J, Reina DG, Toral SL (2019) A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario. Futur Gener Comput Syst 90:129–148
Shi W, Li J, Xu W, Zhou H, Zhang N, Zhang S, Shen X (2018) Multiple drone-cell deployment analyses and optimization in drone assisted radio access networks. IEEE Access 6:12518–12529
Ghamry KA, Kamel MA, Zhang Y (June 2017) Multiple UAVs in forest fire fighting mission using particle swarm optimization. In: 2017 International conference on unmanned aircraft systems (ICUAS), pp 1404–1409
Cheng Z, Wang E, Tang Y, Wang Y (2014) Real-time path planning strategy for UAV based on improved particle swarm optimization. JCP 9(1):209–214
Bircher A, Kamel M, Alexis K, Oleynikova H, Siegwart R (May 2016) Receding horizon “next-best-view” planner for 3d exploration. In: 2016 IEEE international conference on robotics and automation (ICRA), pp 1462–1468
Teng H, Ahmad I, Msm A, Chang K (2020) 3D optimal surveillance trajectory planning for multiple UAVs by using particle swarm optimization with surveillance area priority. IEEE Access 8:86316–86327
Pattanayak S, Choudhury BB (2021) Modified crash-minimization path designing approach for autonomous material handling robot. Evol Intel 14(1):21–34
Yu X, Li C, Zhou J (2020) A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios. Knowl-Based Syst 204:106209
Dasdemir E, Köksalan M, Öztürk DT (2020) A flexible reference point-based multi-objective evolutionary algorithm: an application to the UAV route planning problem. Comput Oper Res 114:104811
Qu C, Gai W, Zhang J, Zhong M (2020) A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning. Knowl-Based Syst 194, 105530
Atencia CR, Del Ser J, Camacho D (2019) Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning. Swarm Evol Comput 44:480–495
Shakoor S, Kaleem Z, Do DT, Dobre OA, Jamalipour A (2020) Joint optimization of UAV 3D placement and path loss factor for energy efficient maximal coverage. IEEE Internet Things J 9776–9786
Do-Duy T, Nguyen LD, Duong TQ, Khosravirad S, Claussen H (2021) Joint optimisation of real-time deployment and resource allocation for UAV-Aided disaster emergency communications. IEEE J Sel Areas Commun 1–14
Nguyen KK, Vien NA, Nguyen LD, Le MT, Hanzo L, Duong TQ (2020) Real-time energy harvesting aided scheduling in UAV-assisted D2D networks relying on deep reinforcement learning. IEEE Access 9:3638–3648
Do DT, Nguyen TTT, Le CB, Voznak M, Kaleem Z, Rabie KM (2020) UAV relaying enabled NOMA network with hybrid duplexing and multiple antennas. IEEE Access 8:186993–187007
Kaleem Z, Yousaf M, Qamar A, Ahmad A, Duong TQ, Choi W, Jamalipour A (2019) UAV-empowered disaster-resilient edge architecture for delay-sensitive communication. IEEE Network 33(6):124–132
Zhou H, Zhang T, Jagadeesan J (2018) Re-weighting and 1-point RANSAC-Based P $ n $ n P solution to handle outliers. IEEE Trans Pattern Anal Mach Intell 41(12):3022–3033
Chum O, Matas J (June 2005) Matching with PROSAC-progressive sample consensus. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05), vol 1, pp 220–226
Bellavia F, Tegolo D, Valenti C (2011) Improving Harris corner selection strategy. IET Comput Vision 5(2):87–96
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698
Karami E, Prasad S, Shehata M (2017) Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv:1710.02726
Ohta Y, Kanade T (1985) Stereo by intra-and inter-scanline search using dynamic programming. IEEE Trans Pattern Anal Mach Intell 2:139–154
Lowe DG (1991) Fitting parameterized three-dimensional models to images. IEEE Trans Pattern Anal Mach Intell 13(5):441–450
Zheng C, Li L, Xu F, Sun F, Ding M (2005) Evolutionary route planner for unmanned air vehicles. IEEE Trans Rob 21(4):609–620
Kennedy J, Eberhart R (Nov 1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol 4, pp 1942–1948
Roberge V, Tarbouchi M, Labonté G (2012) Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Industr Inf 9(1):132–141
Fonder M, Van Droogenbroeck M (2019) Mid-air: a multi-modal dataset for extremely low altitude drone flights. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 0–0
Guo X, Chen S, Lin H, Wang H, Wang S (July 2017) A 3D terrain meshing method based on discrete point cloud. In: 2017 IEEE international conference on information and automation (ICIA), pp 12–17
Kneip L, Scaramuzza D, Siegwart R (2011) A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation. CVPR 2011:2969–2976
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) under Grant NRF-2019R1F1A1061696.
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Mughal, U.A., Ahmad, I., Pawase, C.J., Chang, K. (2022). UAVs Path Planning by Particle Swarm Optimization Based on Visual-SLAM Algorithm. In: Kaleem, Z., Ahmad, I., Duong, T.Q. (eds) Intelligent Unmanned Air Vehicles Communications for Public Safety Networks. Unmanned System Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-19-1292-4_8
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