Intention-Aware Planner for Robust and Safe Aerial Tracking (2309.08854v4)
Abstract: Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.
- B. Jeon, Y. Lee, and H. J. Kim, “Integrated motion planner for real-time aerial videography with a drone in a dense environment,” in 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 1243–1249.
- Z. Han, R. Zhang, N. Pan, C. Xu, and F. Gao, “Fast-tracker: A robust aerial system for tracking agile target in cluttered environments,” in 2021 IEEE international conference on robotics and automation (ICRA). IEEE, 2021, pp. 328–334.
- N. Pan, R. Zhang, T. Yang, C. Cui, C. Xu, and F. Gao, “Fast-tracker 2.0: Improving autonomy of aerial tracking with active vision and human location regression,” IET Cyber-Systems and Robotics, vol. 3, no. 4, pp. 292–301, 2021.
- Q. Wang, Y. Gao, J. Ji, C. Xu, and F. Gao, “Visibility-aware trajectory optimization with application to aerial tracking,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021, pp. 5249–5256.
- J. Ji, N. Pan, C. Xu, and F. Gao, “Elastic tracker: A spatio-temporal trajectory planner for flexible aerial tracking,” in 2022 International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 47–53.
- Z. Zhang, Y. Zhong, J. Guo, Q. Wang, C. Xu, and F. Gao, “Auto filmer: Autonomous aerial videography under human interaction,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 784–791, 2022.
- S. Bonnin, T. H. Weisswange, F. Kummert, and J. Schmüdderich, “Pedestrian crossing prediction using multiple context-based models,” in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2014, pp. 378–385.
- M. Goldhammer, S. Köhler, K. Doll, and B. Sick, “Camera based pedestrian path prediction by means of polynomial least-squares approximation and multilayer perceptron neural networks,” in 2015 SAI Intelligent Systems Conference (IntelliSys). IEEE, 2015, pp. 390–399.
- A. Dominguez-Sanchez, M. Cazorla, and S. Orts-Escolano, “Pedestrian movement direction recognition using convolutional neural networks,” IEEE transactions on intelligent transportation systems, vol. 18, no. 12, pp. 3540–3548, 2017.
- J. Li, Q. Li, N. Chen, and Y. Wang, “Indoor pedestrian trajectory detection with lstm network,” in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol. 1. IEEE, 2017, pp. 651–654.
- D. Kulic and E. A. Croft, “Affective state estimation for human–robot interaction,” IEEE transactions on robotics, vol. 23, no. 5, pp. 991–1000, 2007.
- J. F. P. Kooij, N. Schneider, F. Flohr, and D. M. Gavrila, “Context-based pedestrian path prediction,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13. Springer, 2014, pp. 618–633.
- H. S. Koppula and A. Saxena, “Anticipating human activities using object affordances for reactive robotic response,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 1, pp. 14–29, 2015.
- A. T. Schulz and R. Stiefelhagen, “A controlled interactive multiple model filter for combined pedestrian intention recognition and path prediction,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 2015, pp. 173–178.
- A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 961–971.
- J. Kim and D. H. Shim, “A vision-based target tracking control system of a quadrotor by using a tablet computer,” in 2013 international conference on unmanned aircraft systems (icuas). IEEE, 2013, pp. 1165–1172.
- A. G. Kendall, N. N. Salvapantula, and K. A. Stol, “On-board object tracking control of a quadcopter with monocular vision,” in 2014 international conference on unmanned aircraft systems (ICUAS). IEEE, 2014, pp. 404–411.
- H. Cheng, L. Lin, Z. Zheng, Y. Guan, and Z. Liu, “An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2017, pp. 1732–1738.
- V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang, and M. Grundmann, “Blazepose: On-device real-time body pose tracking,” arXiv preprint arXiv:2006.10204, 2020.
- Z. Wang, X. Zhou, C. Xu, and F. Gao, “Geometrically constrained trajectory optimization for multicopters,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 3259–3278, 2022.
- S. Liu, M. Watterson, K. Mohta, K. Sun, S. Bhattacharya, C. J. Taylor, and V. Kumar, “Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1688–1695, 2017.
- L. S. Jennings and K. L. Teo, “A computational algorithm for functional inequality constrained optimization problems,” Automatica, vol. 26, no. 2, pp. 371–375, 1990.
- Q. Tong, L. Peiliang, and S. Shaojie, “Vins-mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Transactions on Robotics, vol. PP, no. 99, pp. 1–17, 2017.