@inproceedings{Zhang2024TimeOpt,

title = {Time-optimal Point-to-point Motion Planning: A Two-stage Approach},

author = {Shuhao Zhang and Jan Swevers},

url = {https://doi.org/10.48550/arXiv.2403.03573

https://www.sciencedirect.com/science/article/pii/S2405896324014010},

doi = {https://doi.org/10.1016/j.ifacol.2024.09.022},

year  = {2024},

date = {2024-09-25},

urldate = {2024-04-16},

booktitle = {8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024},

volume = {58},

number = {18},

pages = {139-145},

publisher = {IFAC-PapersOnLine},

address = {Kyoto, Japan},

abstract = {This paper proposes a two-stage approach to formulate the time-optimal point-to-point motion planning problem, involving a first stage with a fixed time grid and a second stage with a variable time grid. The proposed approach brings benefits through its straightforward optimal control problem formulation with a fixed and low number of control steps for manageable computational complexity and the avoidance of interpolation errors associated with time scaling, especially when aiming to reach a distant goal. Additionally, an asynchronous nonlinear model predictive control (NMPC) update scheme is integrated with this two-stage approach to address delayed and fluctuating computation times, facilitating online replanning. The effectiveness of the proposed two-stage approach and NMPC implementation is demonstrated through numerical examples centered on autonomous navigation with collision avoidance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lirias4141698,

title = {Robustified Time-optimal Collision-free Motion Planning for Autonomous Mobile Robots under Disturbance Conditions},

author = {Shuhao Zhang and Mathis Bos and Bastiaan Vandewal and Wilm Decré and Joris Gillis and Jan Swevers},

url = {https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias4141698&context=SearchWebhook&vid=32KUL_KUL:Lirias&search_scope=lirias_profile&adaptor=SearchWebhook&tab=LIRIAS&query=any,contains,LIRIAS4141698&offset=0},

doi = {10.1109/ICRA57147.2024.10610134},

isbn = {979-8-3503-8457-4},

year  = {2024},

date = {2024-08-08},

urldate = {2024-02-07},

booktitle = {2024 IEEE International Conference on Robotics and Automation (ICRA)},

pages = {14258-14264},

publisher = {IEEE},

address = {Yokohama, Japan},

abstract = {This paper presents a robustified time-optimal motion planning approach for navigating an Autonomous Mobile Robot (AMR) from an initial state to a terminal state without colliding with obstacles, even when subjected to disturbances, which are modeled as random process noise and measurement noise. The approach iteratively solves the robustified problem by incorporating updated state-dependent safety margins for collision avoidance, the evolution of which is derived separately from the robustified problem. Additionally, a strategy for selecting an alternative terminal state to reach is introduced, which comes into play when the desired terminal state becomes infeasible considering the disturbances. Both of these contributions are integrated into a robustified motion planning and control pipeline, the efficacy of which is validated through simulation experiments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}