{"id":1349,"date":"2022-03-28T18:41:00","date_gmt":"2022-03-28T18:41:00","guid":{"rendered":"https:\/\/elo-x.eu\/?p=1349"},"modified":"2024-02-07T09:02:37","modified_gmt":"2024-02-07T09:02:37","slug":"yunfan-gao","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=1349","title":{"rendered":"Yunfan Gao"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tYunfan Gao \n\t\t\t<\/h2 > \n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Advanced Autonomous Systems Department <\/b><\/span><\/p>

Robert Bosch GmbH, Corporate Research<\/b><\/span><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Yunfan Gao obtained the bachelor degree in Electronic engineering from Fudan University, Shanghai, China in 2019. Then she took her master study in Robotics, Systems and Control at ETH Zurich, and graduated in January 2022. Her master thesis was about the integration of projection mapping with mobile robots. Recently in March 2022, she started carrying out a PhD at Bosch Research with the topic \u201cSafety and Robustness in Mobile Robot Motion Planning\u201d.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Project description<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This PhD project is about motion planning for industrial robots and service robots. The aim is to run mobile robots efficiently with safety guaranteed. Currently, the common approach in industry to achieve safety is to deploy a separate safety controller. The controller alters the control commands made by the motion planner if necessary. This approach is too conservative as large portions of the space are marked as unsafe. To achieve safety in motion planning non-conservatively is challenging. Possible research directions include tighter integration between the motion planning and the safety control, prediction of surrounding agents\u2019 trajectories, as well as real-time execution.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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1.<\/div>

Gao, Yunfan; Messerer, Florian; van Duijkeren, Niels; Houska, Boris; Diehl, Moritz<\/p>

Real-Time-Feasible Collision-Free Motion Planning For Ellipsoidal Objects<\/a> Proceedings Article<\/span> Forthcoming<\/span><\/p>

In: <\/span>Forthcoming, (Accepted at the 2024 Conference on Decision and Control (CDC))<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@inproceedings{24_gao_realtimefeasible,
\r\ntitle = {Real-Time-Feasible Collision-Free Motion Planning For Ellipsoidal Objects},
\r\nauthor = {Yunfan Gao and Florian Messerer and Niels van Duijkeren and Boris Houska and Moritz Diehl},
\r\ndoi = {https:\/\/doi.org\/10.48550\/arXiv.2409.12007},
\r\nyear  = {2024},
\r\ndate = {2024-09-18},
\r\nabstract = {Online planning of collision-free trajectories is a fundamental task for robotics and self-driving car applications. This paper revisits collision avoidance between ellipsoidal objects using differentiable constraints. Two ellipsoids do not overlap if and only if the endpoint of the vector between the center points of the ellipsoids does not lie in the interior of the Minkowski sum of the ellipsoids. This condition is formulated using a parametric over-approximation of the Minkowski sum, which can be made tight in any given direction. The resulting collision avoidance constraint is included in an optimal control problem (OCP) and evaluated in comparison to the separating-hyperplane approach. Not only do we observe that the Minkowski-sum formulation is computationally more efficient in our experiments, but also that using pre-determined over-approximation parameters based on warm-start trajectories leads to a very limited increase in suboptimality. This gives rise to a novel real-time scheme for collision-free motion planning with model predictive control (MPC). Both the real-time feasibility and the effectiveness of the constraint formulation are demonstrated in challenging real-world experiments.},
\r\nnote = {Accepted at the 2024 Conference on Decision and Control (CDC)},
\r\nkeywords = {},
\r\npubstate = {forthcoming},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
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Online planning of collision-free trajectories is a fundamental task for robotics and self-driving car applications. This paper revisits collision avoidance between ellipsoidal objects using differentiable constraints. Two ellipsoids do not overlap if and only if the endpoint of the vector between the center points of the ellipsoids does not lie in the interior of the Minkowski sum of the ellipsoids. This condition is formulated using a parametric over-approximation of the Minkowski sum, which can be made tight in any given direction. The resulting collision avoidance constraint is included in an optimal control problem (OCP) and evaluated in comparison to the separating-hyperplane approach. Not only do we observe that the Minkowski-sum formulation is computationally more efficient in our experiments, but also that using pre-determined over-approximation parameters based on warm-start trajectories leads to a very limited increase in suboptimality. This gives rise to a novel real-time scheme for collision-free motion planning with model predictive control (MPC). Both the real-time feasibility and the effectiveness of the constraint formulation are demonstrated in challenging real-world experiments.<\/div>
Close<\/a><\/p><\/div>