Phan Huu, Thanh (2021)
Optimal and Distributed Motion Planning for Multiple Robot Systems.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00019707
Dissertation, Erstveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
Within the last decades, autonomous multirobot systems have been received an increasing interest due to their enormous application potential. Motion planning algorithm is a key enabler to a greater autonomy. This thesis focuses particularly on the motion planning algorithm for the autonomous multirobot systems. The contributions of the thesis are threefold.
Firstly, a novel high-order near time-optimal trajectory generation algorithm is presented for twodimensional robots. The proposed trajectory generation algorithm is numerical efficient and can be embedded for any point-to-point movement in factory and logistic automation. Moreover, the generated trajectories are not only near time-optimal but also bounded by high-order time derivatives. Time discretization effects as well as nonlinear constraints due to obstacles avoidance are incorporated into the trajectory generation algorithm. These properties of the trajectory generation algorithm are essential to high-performance industrial motion control systems.
Subsequently, a novel centralized motion coordination solution is introduced, which comprises a collision avoidance algorithm and a conflict resolution algorithm. The system safety can be guaranteed by employing the collision avoidance algorithm, while deadlocks can be prevented by deploying the conflict resolution algorithm. Particularly, the theoretical safety of the collision avoidance algorithm can be proved. Moreover, the centralized coordination solution is highly modular, since it can be applied as a post process to the aforementioned trajectory generation algorithm. Consequently, all important properties such as bounded high-order derivatives or time discretization consideration can be preserved.
Finally, an asynchronous distributed collision avoidance algorithm is developed for multiple passive robots translating on a grid of static rectangular intelligent agents. The distributed algorithm is deployed directly into the static intelligent agents. Furthermore, the distributed algorithm is not only able to guarantee collision-free motions but also robust with respect to stochastic arbitrarily large communication time delays. Therefore, the distributed algorithm is a key to scale existing prevalent industrial systems to another dimension.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2021 | ||||
Autor(en): | Phan Huu, Thanh | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Optimal and Distributed Motion Planning for Multiple Robot Systems | ||||
Sprache: | Englisch | ||||
Referenten: | Konigorski, Prof. Dr. Ulrich ; Adamy, Prof. Dr. Jürgen | ||||
Publikationsjahr: | 2021 | ||||
Ort: | Darmstadt | ||||
Kollation: | XIV, 114 Seiten | ||||
Datum der mündlichen Prüfung: | 4 August 2021 | ||||
DOI: | 10.26083/tuprints-00019707 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/19707 | ||||
Kurzbeschreibung (Abstract): | Within the last decades, autonomous multirobot systems have been received an increasing interest due to their enormous application potential. Motion planning algorithm is a key enabler to a greater autonomy. This thesis focuses particularly on the motion planning algorithm for the autonomous multirobot systems. The contributions of the thesis are threefold. Firstly, a novel high-order near time-optimal trajectory generation algorithm is presented for twodimensional robots. The proposed trajectory generation algorithm is numerical efficient and can be embedded for any point-to-point movement in factory and logistic automation. Moreover, the generated trajectories are not only near time-optimal but also bounded by high-order time derivatives. Time discretization effects as well as nonlinear constraints due to obstacles avoidance are incorporated into the trajectory generation algorithm. These properties of the trajectory generation algorithm are essential to high-performance industrial motion control systems. Subsequently, a novel centralized motion coordination solution is introduced, which comprises a collision avoidance algorithm and a conflict resolution algorithm. The system safety can be guaranteed by employing the collision avoidance algorithm, while deadlocks can be prevented by deploying the conflict resolution algorithm. Particularly, the theoretical safety of the collision avoidance algorithm can be proved. Moreover, the centralized coordination solution is highly modular, since it can be applied as a post process to the aforementioned trajectory generation algorithm. Consequently, all important properties such as bounded high-order derivatives or time discretization consideration can be preserved. Finally, an asynchronous distributed collision avoidance algorithm is developed for multiple passive robots translating on a grid of static rectangular intelligent agents. The distributed algorithm is deployed directly into the static intelligent agents. Furthermore, the distributed algorithm is not only able to guarantee collision-free motions but also robust with respect to stochastic arbitrarily large communication time delays. Therefore, the distributed algorithm is a key to scale existing prevalent industrial systems to another dimension. |
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Alternatives oder übersetztes Abstract: |
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Status: | Verlagsversion | ||||
URN: | urn:nbn:de:tuda-tuprints-197070 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik |
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Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Regelungstechnik und Mechatronik |
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Hinterlegungsdatum: | 14 Okt 2021 06:44 | ||||
Letzte Änderung: | 15 Okt 2021 07:02 | ||||
PPN: | |||||
Referenten: | Konigorski, Prof. Dr. Ulrich ; Adamy, Prof. Dr. Jürgen | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 4 August 2021 | ||||
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