Robust Predictive Motion Planning by Learning Obstacle Uncertainty
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| Titel: | Robust Predictive Motion Planning by Learning Obstacle Uncertainty |
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| Autoren: | Zhou, Jian, Gao, Yulong, Johansson, Ola, Olofsson, Björn, Frisk, Erik |
| Weitere Verfasser: | Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Engineering Health, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Teknik för hälsa, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Automatic Control, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för reglerteknik, Originator |
| Quelle: | IEEE Transactions on Control Systems Technology. 33(3):1006-1020 |
| Schlagwörter: | Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Control Engineering, Teknik, Elektroteknik och elektronik, Reglerteknik |
| Beschreibung: | Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego robotic system. In this article, an efficient, robust, and safe motion-planning algorithm is developed by learning the obstacle uncertainties online. More specifically, the unknown yet intended control set of obstacles is efficiently computed by solving a linear programming (LP) problem. The learned control set is used to compute forward reachable sets (FRSs) of obstacles that are less conservative than the worst case prediction. Based on the forward prediction, a robust model predictive controller is designed to compute a safe reference trajectory for the ego robotic system that remains outside the reachable sets of obstacles over the prediction horizon. Themethod is applied to a car-like mobile robot in both simulations and hardware experiments to demonstrate its effectiveness. |
| Zugangs-URL: | https://arxiv.org/abs/2403.06222 |
| Datenbank: | SwePub |
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Nájsť tento článok vo Web of Science | Abstract: | Safe motion planning for robotic systems in dynamic environments is nontrivial in the presence of uncertain obstacles, where estimation of obstacle uncertainties is crucial in predicting future motions of dynamic obstacles. The worst case characterization gives a conservative uncertainty prediction and may result in infeasible motion planning for the ego robotic system. In this article, an efficient, robust, and safe motion-planning algorithm is developed by learning the obstacle uncertainties online. More specifically, the unknown yet intended control set of obstacles is efficiently computed by solving a linear programming (LP) problem. The learned control set is used to compute forward reachable sets (FRSs) of obstacles that are less conservative than the worst case prediction. Based on the forward prediction, a robust model predictive controller is designed to compute a safe reference trajectory for the ego robotic system that remains outside the reachable sets of obstacles over the prediction horizon. Themethod is applied to a car-like mobile robot in both simulations and hardware experiments to demonstrate its effectiveness. |
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| ISSN: | 10636536 15580865 |
| DOI: | 10.1109/TCST.2025.3533378 |