Control Barrier Function Based UAV Safety Controller in Autonomous Airborne Tracking and Following Systems (2312.17215v1)
Abstract: Safe operations of UAVs are of paramount importance for various mission-critical and safety-critical UAV applications. In context of airborne target tracking and following, UAVs need to track a flying target avoiding collision and also closely follow its trajectory. The safety situation becomes critical and more complex when the flying target is non-cooperative and has erratic movements. This paper proposes a method for collision avoidance in an autonomous fast moving dynamic quadrotor UAV tracking and following another target UAV. This is achieved by designing a safety controller that minimally modifies the control input from a trajectory tracking controller and guarantees safety. This method enables pairing our proposed safety controller with already existing flight controllers. Our safety controller uses a control barrier function based quadratic program (CBF-QP) to produce an optimal control input enabling safe operation while also follow the trajectory of the target closely. We implement our solution on AirSim simulator over PX4 flight controller and with numerical results, we validate our approach through several simulation experiments with multiple scenarios and trajectories.
- A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs for safety critical systems,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3861–3876, 2016.
- A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in 2019 18th European control conference (ECC). IEEE, 2019, pp. 3420–3431.
- B. Xu and K. Sreenath, “Safe teleoperation of dynamic uavs through control barrier functions,” in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 7848–7855.
- C. Lerch, D. Dong, and I. Abraham, “Safety-critical ergodic exploration in cluttered environments via control barrier functions,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 10 205–10 211.
- D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine, vol. 4, no. 1, pp. 23–33, 1997.
- T. Schouwenaars, “Safe trajectory planning of autonomous vehicles,” Ph.D. dissertation, Massachusetts Institute of Technology, 2006.
- R. A. Zitar, A. Mohsen, A. E. Seghrouchni, F. Barbaresco, and N. A. Al-Dmour, “Intensive review of drones detection and tracking: Linear kalman filter versus nonlinear regression, an analysis case,” Archives of Computational Methods in Engineering, pp. 1–20, 2023.
- Y.-H. Hsu and R.-H. Gau, “Reinforcement learning-based collision avoidance and optimal trajectory planning in uav communication networks,” IEEE Transactions on Mobile Computing, vol. 21, no. 1, pp. 306–320, 2020.
- P. Fraga-Lamas, L. Ramos, V. Mondéjar-Guerra, and T. M. Fernández-Caramés, “A review on iot deep learning uav systems for autonomous obstacle detection and collision avoidance,” Remote Sensing, vol. 11, no. 18, p. 2144, 2019.
- X. Dai and M. Nagahara, “Platooning control of drones with real-time deep learning object detection,” Advanced Robotics, vol. 37, no. 3, pp. 220–225, 2023.
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
- M. Tayal and S. Kolathaya, “Control barrier functions in dynamic uavs for kinematic obstacle avoidance: A collision cone approach,” arXiv preprint arXiv:2303.15871, 2023.
- Y. Chen, A. Singletary, and A. D. Ames, “Guaranteed obstacle avoidance for multi-robot operations with limited actuation: A control barrier function approach,” IEEE Control Systems Letters, vol. 5, no. 1, pp. 127–132, 2020.
- M. Machida and M. Ichien, “Consensus-based control barrier function for swarm,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 8623–8628.
- W. Qing, H. Chen, X. Wang, and Y. Yin, “Collision-free trajectory generation for uav swarm formation rendezvous,” in 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2021, pp. 1861–1867.
- H. Gunnarsson and A. Åsbrink, “Intelligent drone swarms: Motion planning and safe collision avoidance control of autonomous drone swarms,” 2022.
- J. Ghommam, L. F. Luque-Vega, and M. Saad, “Distance-based formation control for quadrotors with collision avoidance via lyapunov barrier functions,” International Journal of Aerospace Engineering, vol. 2020, pp. 1–17, 2020.
- H. Dan, T. Hatanaka, J. Yamauchi, T. Shimizu, and M. Fujita, “Persistent object search and surveillance control with safety certificates for drone networks based on control barrier functions,” Frontiers in Robotics and AI, vol. 8, p. 740460, 2021.
- “Microsoft airsim,” https://microsoft.github.io/AirSim/, accessed: 2023-09-14.
- S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” 2017.
- “Px4 autopilot,” https://px4.io/software/software-overview/, accessed: 2023-09-14.
- L. Meier, D. Honegger, and M. Pollefeys, “Px4: A node-based multithreaded open source robotics framework for deeply embedded platforms,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015, pp. 6235–6240.
- “Windows subsystem for linux 2 (wsl 2),” https://learn.microsoft.com/en-us/windows/wsl/about, accessed: 2023-09-14.
- “Holybro m8n gps module specification,” https://docs.holybro.com/gps-and-rtk-system/m8n-m9n-m10-gps/standard-m10-m9n-m8n-gps/overviewspecification, accessed: 2023-09-14.
- A. Carron, E. Arcari, M. Wermelinger, L. Hewing, M. Hutter, and M. N. Zeilinger, “Data-driven model predictive control for trajectory tracking with a robotic arm,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3758–3765, 2019.
- P. Rabiee and J. B. Hoagg, “Soft-minimum and soft-maximum barrier functions for safety with actuation constraints,” arXiv preprint arXiv:2305.10620, 2023.