In real-world robotics, motion planning remains to be an open challenge. Not only robotic systems are required to move through unexplored environments, but also their maneuverability is constrained by their dynamics and often suffer from uncertainty.
In this project, we seek to overcome this problem through incrementally mapping the surroundings while, simultaneously, planning a safe and feasible path to a desired goal. This is especially critical in environments, where autonomous vehicles must deal with both motion and environment uncertainties. In order to cope with these constraints, this project investigates an uncertainty-based framework for mapping and planning feasible motions online with safety guarantees. The resulting algorithms will be evaluated on autonomous underwater vehicles and small unmanned aerial vehicles. If successful, this project can enable many applications such as autonomous drone delivery, autonomous planetary exploration, search and rescue, and underwater explorations.
If you are interested in learning more, please reach out to Qi Heng Ho at email@example.com or checkout some of our recent related publications.
È. Pairet, J. D. Hernández, M. Carreras, Y. Petillot, and M. Lahijanian, Online mapping and motion planning under uncertainty for safe navigation in unknown environments,” in arXiv preprint arXiv:2004.12317, 2020.
È. Pairet, J. D. Hernández, M. Lahijanian, and M. Carreras, “Uncertainty-based online mapping and motion planning for marine robotics guidance,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, pp. 2367–2374.