Time-Continuous Real-Time Trajectory Generation for Safe Autonomous Flight of a Quadrotor in Unknown Environment
Source abstract
Abstract
In this paper, we present an efficient global and local replanning method for a quadrotor to complete a flight mission in a cluttered and unmapped environment. A minimum-snap global path planner generates a global trajectory that comprises some waypoints in a cluttered environment. When facing unexpected obstacles, our method modifies the global trajectory using geometrical planning and closed-form formulation for an analytical solution with 9th-order polynomial. The proposed method provides an analytical solution, not a numerical one, and it is computationally efficient without falling into a local minima problem. In a simulation, we show that the proposed method can fly a quadrotor faster than the numerical method in a cluttered environment. Furthermore, we show in experiments that the proposed method can provide safer and faster trajectory generation than the numerical method in a real environment.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Guidance and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Artificial Intelligence Research, Hyundai-Autoever, Seoul 06176, Korea
Department of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea
Funding
Ministry of Science and ICT, South Korea
References (20)
Show source-provided references
1. Unmanned aerial vehicles (uavs): Collision avoidance systems and approaches10.1109/ACCESS.2020.3000064 ↗
2. Mellinger, D., and Kumar, V. (2011, January 9–13). Minimum snap trajectory generation and control for quadrotors. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.10.1109/ICRA.2011.5980409 ↗
3. Richter, C., Bry, A., and Roy, N. (2016). Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. Robotics Research, Springer.10.1007/978-3-319-28872-7_37 ↗
4. Copot, C., Hernandez, A., Mac, T.T., and De Keyse, R. (September, January 29). Collision-free path planning in indoor environment using a quadrotor. Proceedings of the 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland.10.1109/MMAR.2016.7575160 ↗
5. Vision-based reactive planning for aggressive target tracking while avoiding collisions and occlusions10.1109/LRA.2018.2856526 ↗
6. Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R., and Nieto, J. (2017, January 24–28). Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8202315 ↗
7. Visual SLAM algorithms: A survey from 2010 to 2016
8. Burri, M., Oleynikova, H., Achtelik, M.W., and Siegwart, R. (October, January 28). Real-time visual-inertial mapping, re-localization and planning onboard mavs in unknown environments. Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.10.1109/IROS.2015.7353622 ↗
9. Peng, X.Z., Lin, H.Y., and Dai, J.M. (2016, January 1–3). Path planning and obstacle avoidance for vision guided quadrotor UAV navigation. Proceedings of the 2016 12th IEEE International Conference on Control and Automation (ICCA), Kathmandu, Nepal.10.1109/ICCA.2016.7505408 ↗
10. A real-time 3d path planning solution for collision-free navigation of multirotor aerial robots in dynamic environments10.1007/s10846-018-0809-5 ↗
11. Oleynikova, H., Burri, M., Taylor, Z., Nieto, J., Siegwart, R., and Galceran, E. (2016, January 9–14). Continuous-time trajectory optimization for online UAV replanning. Proceedings of the 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS), Daejeon, Korea.10.1109/IROS.2016.7759784 ↗
12. Survey of numerical methods for trajectory optimization10.2514/2.4231 ↗
13. An implicit filtering algorithm for optimization of functions with many local minima10.1137/0805015 ↗
14. A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization10.1109/TIE.2018.2886798 ↗
15. Autonomous flight control of a nano quadrotor helicopter in a GPS-denied environment using on-board vision10.1109/TIE.2015.2420036 ↗
16. Furrer, F., Burri, M., Achtelik, M., and Siegwart, R. (2016). Robot Operating System (ROS): The Complete Reference (Volume 1), Springer International Publishing.
17. Meier, L., Tanskanen, P., Fraundorfer, F., and Pollefeys, M. (2011, January 9–13). Pixhawk: A system for autonomous flight using onboard computer vision. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.10.1109/ICRA.2011.5980229 ↗
18. OctoMap: An efficient probabilistic 3D mapping framework based on octrees10.1007/s10514-012-9321-0 ↗
19. Lee, T., Leok, M., and McClamroch, N.H. (2010, January 15–17). Geometric tracking control of a quadrotor UAV on SE (3). Proceedings of the 49th IEEE conference on decision and control (CDC), Atlanta, GA, USA.10.1109/CDC.2010.5717652 ↗
20. Usenko, V., Von Stumberg, L., Pangercic, A., and Cremers, D. (2017, January 24–28). Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer. Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.10.1109/IROS.2017.8202160 ↗