Human Center of Mass Trajectory Models Using Nonlinear Model Predictive Control
| Autor: | Alexander Joos, Marian Hoffmann, Thorsten Stein |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Basis (linear algebra) Computer science 010401 analytical chemistry Work (physics) 02 engineering and technology 01 natural sciences 0104 chemical sciences Model predictive control 020901 industrial engineering & automation Control and Systems Engineering Control theory Obstacle Trajectory Robot Center of mass Focus (optics) |
| Zdroj: | SyRoCo |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2018.11.569 |
| Popis: | In this paper we describe a nonlinear model predictive control (NMPC) scheme to model human Center of Mass (CoM) trajectories in an environment with an obstacle. This NMPC model can be used onboard a robot for planning its motion in a way humans do, or can in addition be used by the robot to predict a probable future CoM trajectory of a human participant. Both features support human-robot interaction. The focus of this work is on the derivation of appropriate parameters of the NMPC and a suitable geometrical model of a safety zone around the obstacle. To this end, we have conducted experiments with human participants and have computed the resulting CoM trajectories. On this basis we have derived suited NMPC parameters to imitate the human CoM trajectory planning behavior with a high precision. With models tailored to each experiment, the NMPC approximates the human motion with mean distances of less than 5cm between modelled and real CoM trajectory. With a generalized model, which is identical for all experiments, the mean distance per experiment is less than 12cm. |
| Databáze: | OpenAIRE |
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