Video shows Fast, Attemptable Route (FAR) Planner in action. FAR planner ​uses dynamically updated visibility graph for fast replanning. The planner is capable of handling unknown and partially known environments, where it picks up the environment layout along with the navigation and attempts multiple paths to guide the vehicle to goal. The planner can handle dynamic obstacles as well. The planner implementation uses two CPU threads, one for updating the visibility graph using ~20% of the thread (based on an i7 CPU), and the other thread for fast path search finding a path within 3ms.
This work was used by the CMU-OSU Team in attending DARPA Subterranean Challenge.
Paper references:
F. Yang, C. Cao, H. Zhu, J. Oh, and J. Zhang. FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS). Kyoto, Japan, Oct. 2022.
C. Cao, H. Zhu, F. Yang, Y. Xia, H. Choset, J. Oh, and J. Zhang. Autonomous Exploration Development Environment and the Planning Algorithms. IEEE Intl. Conf. on Robotics and Automation (ICRA). Philadelphia, PA, May 2022.
J. Zhang, C. Hu, R. Gupta Chadha, and S. Singh. Falco: Fast Likelihood-based Collision Avoidance with Extension to Human-guided Navigation. Journal of Field Robotics. vol. 37, no. 8, pp. 1300-1313, 2020.
J. Zhang and S. Singh. Laser-visual-inertial Odometry and Mapping with High Robustness and Low Drift. Journal of Field Robotics. vol. 35, no. 8, pp. 1242-1264, 2018.
FAR Planner:
github.com/MichaelFYang/far_p...
Autonomous Exploration Development Environment:
www.cmu-exploration.com
Author websites:
github.com/MichaelFYang
caochao.me
www.hongbiaoz.com
www.cs.cmu.edu/~./jeanoh
frc.ri.cmu.edu/~zhangji