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

Behavioural templates improve robot motion planning with social force model in human environments2013

Colombo, Alessio;Fontanelli, Daniele;Gandhi, Dhaval Sureshchandra;De Angeli, Antonella;Palopoli, Luigi;Sedwards, Sean Albert;
2013

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
Proceedings of 18th International Conference on Emerging Technologies and Factory Automation
AA. VV.
Cagliari, Italy
IEEE
9781479908622
Colombo, Alessio; Fontanelli, Daniele; Gandhi, Dhaval Sureshchandra; De Angeli, Antonella; Palopoli, Luigi; Sedwards, Sean Albert; A., Legay
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/66688
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