The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Learning monocular reactive UAV control in cluttered natural environments
Open accessAbstract
Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.
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Cites
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Cited by
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., et al. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2015.2509024 · Cited by 665
CAD2RL: Real Single-Image Flight Without a Single Real Image
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Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation
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Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
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Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452
“While alternative learning methods, such as learning from demonstration [36], [37], can address this”
Metadata
- Authors
- Ross, S., Melik-Barkhudarov, N., Shankar, K. S., Wendel, A., Dey, D., Bagnell, J. A., & Hebert, M.
- Venue
- 2013 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 1765-1772
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1980969546
- Cited by
- 403
- References
- 30
- FWCI
- 14.96
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ross, S., Melik-Barkhudarov, N., Shankar, K. S., Wendel, A., Dey, D., Bagnell, J. A., & Hebert, M. (2013). Learning monocular reactive UAV control in cluttered natural environments. 2013 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2013.6630809
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