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Interactive Natural Motion Planning for Robot Systems Based on Representation Space

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Abstract

Endowing robot with interactive natural motion planning is of great importance, since user has to get more and more involved for better realization with versatile tasks under increasingly complicated environments. In this paper, studies on interactive natural motion planning are summarized and a general theoretic framework for which is presented in triple-folds. Firstly, the motion planning model is formulated based on representation space, and essential guidelines on planning algorithm selection are also proposed. Then user intention inference, which consists of grid-based intention model and Bayesian filtering based inferring algorithm, is investigated to mitigate the impact of network induced imperfections during human–robot interaction. Finally, for further rejecting various uncertainties, \({\mathcal {H}}_\infty \) control theory based disturbance observer is proposed. All three algorithmic design procedures are stated in detail and the efficiency of which are presented via existing results, followed by discussions on the future directions.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (NFSC) under Grants 61533012, 91748120 and 61521063.

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  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Guofei Xiang & Jianbo Su

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  1. Guofei Xiang
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  2. Jianbo Su
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Correspondence to Jianbo Su.

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Xiang, G., Su, J. Interactive Natural Motion Planning for Robot Systems Based on Representation Space. Int J of Soc Robotics 12, 345–354 (2020). https://doi.org/10.1007/s12369-019-00552-9

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