Behavioural templates improve robot motion planning with social force model in human environments

Colombo, A. and Fontanelli, D. and Gandhi, D. and De angeli, A. and Palopoli, L. and Sedwards, S. and Legay, A. (2013) Behavioural templates improve robot motion planning with social force model in human environments. In: International Conference on Emerging Technologies and Factory Automation, ETFA, 10-13 September 2013, Cagliari, Italy.

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Abstract

An accurate model of human behaviour is crucial when planning robot motion in human environments. The Social Force Model (SFM) is such a model, having parameters that control both deterministic and stochastic elements. We have constructed an efficient motion planning algorithm by embedding the SFM in a control loop that determines higher level objectives and reacts to environmental changes. Low level predictive modelling is provided by the SFM fed by sensors; high level logic is provided by statistical model checking. To parametrise and improve our motion planning algorithm, we have conducted experiments to consider typical human interactions in crowded environments. We have identified a number of behavioural patterns which may be explicitly incorporated in the SFM to enhance its predictive power. In this paper we describe the results of these experiments and how we parametrise the SFM.

Keywords:Environmental change, Human interactions, Motion planning algorithms, Predictive modelling, Robot motion planning, Social force models, Statistical model checking, Stochastic elements, Experiments, Factory automation, Model checking, Motion planning, Robot programming, Stochastic models
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:25484
Deposited On:06 Jul 2017 11:21

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