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

Alessio Colombo, Daniele Fontanelli, Dhaval Gandhi, Antonella De Angeli, Luigi Palopoli, Sean Sedwards, Axel Legay
2013 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA)  
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
more » ... istical 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.
doi:10.1109/etfa.2013.6648081 dblp:conf/etfa/ColomboFGAPSL13 fatcat:cie6gkkwkbeg3ebrdfxznyrl3m