Catch the Ball: Accurate High-Speed Motions for Mobile Manipulators via Inverse Dynamics Learning
Autor: | Karime Pereida, Ke Dong, Florian Shkurti, Angela P. Schoellig |
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Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning 021103 operations research Mobile manipulator Computer science 0211 other engineering and technologies Control engineering 02 engineering and technology Aerodynamics Workspace Inverse dynamics Machine Learning (cs.LG) Computer Science::Robotics Computer Science - Robotics 020901 industrial engineering & automation Control theory Trajectory Robot Quadratic programming Robotics (cs.RO) Sequential quadratic programming |
Zdroj: | IROS |
DOI: | 10.48550/arxiv.2003.07489 |
Popis: | Mobile manipulators consist of a mobile platform equipped with one or more robot arms and are of interest for a wide array of challenging tasks because of their extended workspace and dexterity. Typically, mobile manipulators are deployed in slow-motion collaborative robot scenarios. In this paper, we consider scenarios where accurate high-speed motions are required. We introduce a framework for this regime of tasks including two main components: (i) a bi-level motion optimization algorithm for real-time trajectory generation, which relies on Sequential Quadratic Programming (SQP) and Quadratic Programming (QP), respectively; and (ii) a learning-based controller optimized for precise tracking of high-speed motions via a learned inverse dynamics model. We evaluate our framework with a mobile manipulator platform through numerous high-speed ball catching experiments, where we show a success rate of 85.33%. To the best of our knowledge, this success rate exceeds the reported performance of existing related systems and sets a new state of the art. Comment: Paper manuscript submitted to IROS 2020 |
Databáze: | OpenAIRE |
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