Abstract
This paper describes an optimization-based trajectory planning scheme for handing over an object between a quadrotor and a wheeled robot in a transportation scenario. Concretely, a quadrotor should pick up an object from a moving ground mobile robot and deliver it to its destination. An optimization framework based on discrete mechanics and complementarity constraints is utilized here to jointly ensure dynamic feasibility and determine the position, timing, and coordination of the handover autonomously. Cooperative trajectories of the heterogeneous robot system can be generated simultaneously to satisfy different requirements by adjusting the objective function and constraints. The proposed planning scheme provides a novel paradigm combining trajectory planning and handover decision-making within an optimal control problem.
Zusammenfassung
Dieser Beitrag befasst sich mit einem auf Optimierung basierenden Trajektorieplanungsschema für die Übergabe eines Objektes zwischen einem Quadrotor und einem mobilen Roboter in einem Transportszenario. Konkret soll ein Quadrotor ein Objekt von einem sich bewegenden mobilen Roboter aufnehmen und es an das vorgesehene Ziel abliefern. Verwendet wird ein Optimierungsframework, basierend auf diskreter Mechanik und Komplementaritätsbedingungen, um gemeinsam die dynamische Durchführbarkeit sicherzustellen und die Position, den Zeitpunkt und die Koordination der Übergabe autonom zu bestimmen. Durch die Anpassung der Zielfunktion und der Nebenbedingungen können kooperative Trajektorien generiert werden, die verschiedene Anforderungen gleichzeitig erfüllen. Das dargestellte Planungsschema bietet ein neuartiges Paradigma, das Trajektorienplanung und Entscheidungsfindung für die Objektübergabe in einem Optimierungsproblem kombiniert.
Funding source: German Research Foundation (DFG)
Award Identifier / Grant number: 433183605
About the authors
Jingshan Chen received the B.Sc. degree in mechanical engineering and automation from China University of Mining and Technology-Beijing in 2016 and M.Sc. degree in mechatronics from the University of Stuttgart, Germany in 2021. She is currently pursuing doctoral studies with the Institute of Engineering and Computational Mechanics, University of Stuttgart and is a member of the research staff there. Her research interests include dynamics of multibody systems and control theory, especially in its applications to robotics.
Wei Luo received the B.Sc. degree in 2013 and M.Sc. degree in 2016 in Mechanical Engineering from the University of Stuttgart, Germany. In 2023, he received his doctoral degree at the University of Stuttgart. His research interests include target trajectory prediction, model predictive control, cooperative trajectory planning and applications to heterogeneous swarm robot system.
Henrik Ebel received his B.Sc. and M.Sc. degrees in Simulation Technology from the University of Stuttgart, Germany, in 2014 and 2016, and his doctoral degree in 2021. He is currently a postdoctoral researcher and a member of the research staff at the Institute of Engineering and Computational Mechanics at the University of Stuttgart. His research interests include multibody system dynamics and control theory, especially in its applications to mechanical systems and in the field of robotics. Of particular interest are the cooperation of multiple robotic agents, as well as optimization-based control schemes.
Peter Eberhard is full professor in mechanics/dynamics and since 2002 director of the Institute of Engineering and Computational Mechanics (ITM) at the University of Stuttgart, Germany. He was Treasurer and Bureau member of IUTAM, the International Union of Theoretical and Applied Mechanics, and served before in many national and international organizations, e.g., as Chairman of the IMSD (International Association for Multibody System Dynamics) or DEKOMECH (German Committee for Mechanics).
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant 433183605 and 501890093 (SPP 2353), and through Germany's Excellence Strategy (Project PN4-4 Theoretical Guarantees for Predictive Control in Adaptive Multi-Agent Scenarios) under Grant EXC 2075-390740016.
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Data availability: The raw data can be obtained on request from the corresponding author.
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