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Research Article

Monocular vision guided deep reinforcement learning UAV systems with representation learning perception

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Article: 2183828 | Received 05 Jun 2022, Accepted 17 Feb 2023, Published online: 08 Mar 2023

References

  • Anderson, W. C., Carey, K., Sturzinger, E. M., & Lowrance, C. J. (2019). Autonomous Navigation via a deep Q network with one-hot image encoding. 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). https://doi.org/10.1109/ismcr47492.2019.8955697
  • Azar, A. T., Koubaa, A., Ali Mohamed, N., Ibrahim, H. A., Ibrahim, Z. F., Kazim, M., Ammar, A., Benjdira, B., Khamis, A. M., Hameed, I. A., & Casalino, G. (2021). Drone deep reinforcement learning: A review. Electronics, 10(9), 999. https://doi.org/10.3390/electronics10090999
  • Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. https://doi.org/10.1109/tpami.2013.50
  • Bonatti, R., Madaan, R., Vineet, V., Scherer, S., & Kapoor, A. (2020). Learning visuomotor policies for aerial navigation using cross-modal representations. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros45743.2020.9341049
  • Caltagirone, L., Bellone, M., Svensson, L., & Wahde, M. (2019). LIDAR–camera fusion for road detection using fully convolutional neural networks. Robotics and Autonomous Systems, 111, 125–131. https://doi.org/10.1016/j.robot.2018.11.002
  • Caselles-Dupré, H., Garcia-Ortiz, M., & Filliat, D. (2018, December 11). Continual state representation learning for reinforcement learning using Generative Replay. arXiv.org. Retrieved October 25, 2022, from https://arxiv.org/abs/1810.03880
  • Devo, A., Mezzetti, G., Costante, G., Fravolini, M. L., & Valigi, P. (2020). Towards generalization in target-driven visual navigation by using deep reinforcement learning. IEEE Transactions on Robotics, 36(5), 1546–1561. https://doi.org/10.1109/tro.2020.2994002
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021, June 3). An image is worth 16(16 words: Transformers for image recognition at scale. arXiv.org. Retrieved June 3, 2022, from https://arxiv.org/abs/2010.11929
  • Elkholy, H. A., Azar, A. T., Shahin, A. S., Elsharkawy, O. I., & Ammar, H. H. (2020). Path planning of a self driving vehicle using artificial intelligence techniques and machine vision. Advances in Intelligent Systems and Computing, 1153, 532–542. https://doi.org/10.1007/978-3-030-44289-7_50
  • Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., & Kavukcuoglu, K. (2018, June 28). Impala: Scalable distributed deep-RL with importance weighted actor-learner architectures. arXiv.org. Retrieved June 3, 2022, from https://arxiv.org/abs/1802.01561
  • Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., & Abbeel, P. (2016). Deep spatial autoencoders for Visuomotor Learning. 2016 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2016.7487173
  • Fujimoto, S., van Hoof, H., & Meger, D. (2018, October 22). Addressing function approximation error in actor-critic methods. arXiv.org. Retrieved June 3, 2022, from https://arxiv.org/abs/1802.09477
  • Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., & Levine, S. (2019, January 29). Soft actor-critic algorithms and applications. arXiv.org. Retrieved June 3, 2022, from https://arxiv.org/abs/1812.05905v2
  • Hasselt, H. v., Guez, A., & Silver, D. (2016, February 1). Deep reinforcement learning with double Q-learning: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Guide Proceedings. Retrieved June 3, 2022, from https://dl.acm.org/doi/10.55553016100.3016191
  • He, D., Zou, Z., Chen, Y., Liu, B., Yao, X., & Shan, S. (2021). Obstacle detection of rail transit based on deep learning. Measurement, 176, 109241. https://doi.org/10.1016/j.measurement.2021.109241
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017, April 17). MobileNets: Efficient convolutional neural networks for Mobile Vision Applications. arXiv.org. Retrieved January 23, 2023, from https://arxiv.org/abs/1704.04861
  • Kumar, V. R., Klingner, M., Yogamani, S., Milz, S., Fingscheidt, T., & Mader, P. (2021). SynDistNet: Self-supervised monocular fisheye camera distance estimation synergised with semantic segmentation for autonomous driving. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv48630.2021.00011
  • Lange, S., & Riedmiller, M. (2010). Deep auto-encoder neural networks in reinforcement learning. The 2010 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2010.5596468
  • Lee, H. Y., Ho, H. W., & Zhou, Y. (2020). Deep learning-based monocular obstacle avoidance for unmanned aerial vehicle navigation in tree plantations. Journal of Intelligent & Robotic Systems, 101, 1. https://doi.org/10.1007/s10846-020-01284-z
  • Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2019, July 5). Continuous control with deep reinforcement learning. arXiv.org. Retrieved June 3, 2022, fromhttps://arxiv.org/abs/1509.02971
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. Computer Vision – ECCV, 2016, 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
  • Lu, Y., Xue, Z., Xia, G.-S., & Zhang, L. (2018). A survey on vision-based UAV navigation. Geo-Spatial Information Science, 21(1), 21–32. https://doi.org/10.1080/10095020.2017.1420509
  • Ma, Z., Wang, C., Niu, Y., Wang, X., & Shen, L. (2018). A saliency-based reinforcement learning approach for a UAV to avoid flying obstacles. Robotics and Autonomous Systems, 100, 108–118. https://doi.org/10.1016/j.robot.2017.10.009
  • Nachum, O., Gu, S., Lee, H., & Levine, S. (2018, October 5). Data-efficient hierarchical reinforcement learning. arXiv.org. Retrieved January 22, 2023, from https://arxiv.org/abs/1805.08296v4
  • Ota, K., Oiki, T., Jha, D. K., Mariyama, T., & Nikovski, D. (2020, June 27). Can increasing input dimensionality improve deep reinforcement learning? arXiv.org. Retrieved August 16, 2022, from https://arxiv.org/abs/2003.01629
  • Padhy, R. P., Verma, S., Ahmad, S., Choudhury, S. K., & Sa, P. K. (2018). Deep neural network for autonomous UAV navigation in indoor corridor environments. Procedia Computer Science, 133, 643–650. https://doi.org/10.1016/j.procs.2018.07.099
  • Pham, D., & Le, T. (2020). Auto-encoding variational Bayes for inferring topics and visualization. Proceedings of the 28th International Conference on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.458
  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.91
  • Ruiz-del-Solar, J., Loncomilla, P., & Soto, N. (2018, March 28). A survey on deep learning methods for robot vision. arXiv.org. Retrieved March 2, 2023, from https://arxiv.org/abs/1803.10862
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00474
  • Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2017). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Field and Service Robotics, 5, 621–635. https://doi.org/10.1007/978-3-319-67361-5_40
  • Shin, S.-Y., Kang, Y.-W., & Kim, Y.-G. (2020). Reward-driven U-Net training for obstacle avoidance drone. Expert Systems with Applications, 143, 113064. https://doi.org/10.1016/j.eswa.2019.113064
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
  • Singh, A., Kalaichelvi, V., & Karthikeyan, R. (2022). A survey on vision guided robotic systems with intelligent control strategies for autonomous tasks. Cogent Engineering, 9(1), https://doi.org/10.1080/23311916.2022.2050020
  • Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (1999, November 1). Policy gradient methods for reinforcement learning with function approximation: Proceedings of the 12th International Conference on Neural Information Processing Systems. Guide Proceedings. Retrieved June 3, 2022, from https://dl.acm.org/doi/10.5555/3009657.3009806
  • Wang, Z., Schaul, T., Hessel, M., Hasselt, H. V., Lanctot, M., & Freitas, N. D. (2016, June 1). Dueling network architectures for Deep Reinforcement Learning: Proceedings of the 33rd International Conference on International Conference on Machine Learning - volume 48. Guide Proceedings. Retrieved June 3, 2022, from https://dl.acm.org/doi/10.55553045390.3045601
  • Wu, Y., Mansimov, E., Liao, S., Radford, A., & Schulman, J. (2017, August 18). OpenAI Baselines: ACKTR & A2c. OpenAI. Retrieved June 3, 2022, from https://openai.com/blog/baselines-acktr-a2c/
  • Xiao, X., Liu, B., Warnell, G., & Stone, P. (2022). Motion planning and control for mobile robot navigation using machine learning: A survey. Autonomous Robots, 46, 569–597. https://doi.org/10.1007/s10514-022-10039-8
  • Yue, P., Xin, J., Zhao, H., Liu, D., Shan, M., & Zhang, J. (2019). Experimental research on deep reinforcement learning in autonomous navigation of Mobile Robot. 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA). https://doi.org/10.1109/iciea.2019.8833968
  • Zhang, J., Hao, B., Chen, B., Li, C., Chen, H., & Sun, J. (2019). Hierarchical reinforcement learning for course recommendation in moocs. Proceedings of the AAAI Conference on Artificial Intelligence, 33((01|1)), 435–442. https://doi.org/10.1609/aaai.v33i01.3301435
  • Zhou, X., Bai, T., Gao, Y., & Han, Y. (2019). Vision-based robot navigation through combining unsupervised learning and hierarchical reinforcement learning. Sensors, 19(7), 1576. https://doi.org/10.3390/s19071576
  • Zhou, Y., & Ho, H. W. (2022). Online robot guidance and navigation in non-stationary environment with hybrid hierarchical reinforcement learning. Engineering Applications of Artificial Intelligence, 114, 105152. https://doi.org/10.1016/j.engappai.2022.105152
  • Zieliński, P., & Markowska-Kaczmar, U. (2021). 3D robotic navigation using a vision-based deep reinforcement learning model. Applied Soft Computing, 110, 107602. https://doi.org/10.1016/j.asoc.2021.107602