Abstract
Obstacle avoidance is very important for UAV flying in unknown environment. In this paper, the UAV’s obstacle avoidance method in unknown environment is proposed from two different view combined with the forward looking perception information of UAV. One is to propose a local real-time planning method from the perspective of optimization. The other is to combined with reinforcement learning to propose a method of avoiding action selection. In this paper, simulation experiments and comparative experiments are carried out to prove the effectiveness of the method.
This work was supported by National Nature Science Foundation (NNSF) of China under Grant 61876187.
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References
Nonami K, Kendoul F, Suzuki S et al (2010) Autonomous flying robots: unmanned aerial vehicles and micro aerial vehicles. Springer Science & Business Media
Usenko V, von Stumberg L, Pangercic A et al (2017) Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer. IEEE/RSJ Int Conf Intell Robots Syst (IROS) 2017:215–222
Nießner M, Zollhöfer M, Izadi S et al (2013) Real-time 3D reconstruction at scale using voxel hashing. ACM Trans Graph (ToG) 32(6):1–11
Oleynikova H et al (2016) Voxblox: Building 3D signed distance fields for planning. arXiv-1611
Oleynikova H et al (2018) Safe local exploration for replanning in cluttered unknown environments for microaerial vehicles. IEEE Robot Autom Lett:1474–1481
Hornung A, Wurm KM, Bennewitz M et al (2013) OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton Robot 34(3):189–206
Hu J, Niu Y, Wang Z (2017) Obstacle avoidance methods for rotor UAVS using realsense camera. In: 2017 Chinese automation congress (CAC). IEEE, pp 7151–7155
Ma Z, Wang C, Niu Y et al (2018) A saliency-based reinforcement learning approach for a UAV to avoid flying obstacles. Robot Auton Syst 100:108–118
LaValle SM (1998) Rapidly-exploring random trees: a new tool for path planning
Kuffner JJ, LaValle SM (2000) RRT-connect: an efficient approach to single-query path planning. In: Proceedings 2000 ICRA. Millennium conference. IEEE international conference on robotics and automation. Symposium proceedings. IEEE, vol 2, pp 995–1001
Shan E, Dai B, Song J et al (2009) A dynamic RRT path planning algorithm based on B-spline. In: 2009 second international symposium on computational intelligence and design. IEEE, vol 2, pp 25–29
Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846–894
Webb DJ, Van Den Berg J (2013) Kinodynamic RRT*: asymptotically optimal motion planning for robots with linear dynamics. In: 2013 IEEE international conference on robotics and automation. IEEE, pp 5054–5061
Mellinger D, Kumar V () Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE international conference on robotics and automation. IEEE, pp 2520–2525
Richter C, Bry A, Roy N (2016) Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. Robot Res:649–666
Oleynikova H, Burri M, Taylor Z et al (2016) Continuous-time trajectory optimization for online UAV replanning. In: 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 5332–5339
Gao F, Lin Y, Shen S (2017) Gradient-based online safe trajectory generation for quadrotor flight in complex environments. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 3681–3688
Oleynikova H, Taylor Z, Siegwart R et al (2018) Safe local exploration for replanning in cluttered unknown environments for microaerial vehicles. IEEE Robot Autom Lett 3(3):1474–1481
Li Y (2018) Deep reinforcement learning (2018) ICASSP 2018—2018 IEEE international conference on acoustics, speech and signal processing (ICASSP)
Li Y (2017) Deep reinforcement learning: an overview
Mnih V, Badia A P, Mirza M et al (2016) Asynchronous methods for deep reinforcement learning
Yen GG, Hickey TW (2004) Reinforcement learning algorithms for robotic navigation in dynamic environments. ISA Trans 43(2):217–230
Cheng Y, Zhang W (2018) Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels. Neurocomputing:272
Xie L, Wang S, Markham A et al (2017) Towards monocular vision based obstacle avoidance through deep reinforcement learning
Wang Z, Schaul T, Hessel M et al (2016) Dueling network architectures for deep reinforcement learning. In: International conference on machine learning, pp 1995–2003
Van Hasselt H, Guez A, Silver D (2015) Deep reinforcement learning with double Q-learning. Comput Sci
Xie L, Wang S, Rosa S et al (2018) Learning with training wheels: speeding up training with a simple controller for deep reinforcement learning
Ma Z (2018) Learning based sense-and-avoid of UAVs. National University of Defense Technology
Kim I, Shin S, Wu J et al (2017) Obstacle avoidance path planning for UAV using reinforcement learning under simulated environment. In: IASER 3rd international conference on electronics, electrical engineering, computer science, Okinawa, pp 34–36
González-Prelcic N, Akl N, Behroozi A et al (2018) Deep reinforcement learning for aerial obstacle avoidance using monocular RGB images
Wang C, Wang J, Zhang X (2018) A deep reinforcement learning approach to flocking and navigation of UAVS in largescale complex environments. In: 2018 IEEE global conference on signal and information processing (GlobalSIP). IEEE, pp 1228–1232
Qin K (2000) General matrix representations for B-splines. Vis Comput 16(3–4):177–186
Lee T, Leok M, McClamroch NH (2010) Geometric tracking control of a quadrotor UAV on SE (3). In: 49th IEEE conference on decision and control (CDC). IEEE, pp 5420–5425
Watkins CJCH, Dayan P (1992) Technical note: Q-learning. Mach Learn 8(3–4):279–292
Rangel A, Camerer C, Montague PR (2008) A framework for studying the neurobiology of value-based decision making. Nat Rev Neurosci 9(7):545–556
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Ma, Z., Hu, J., Niu, Y., Yu, H. (2021). Reactive Obstacle Avoidance Method for a UAV. In: Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-77939-9_3
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