Behavioural templates improve robot motion planning with social force model in human environments
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
behavioural sciences
formal verification
human-robot interaction
mobile robots
path planning
statistical analysis
stochastic processes
SFM
behavioural patterns
behavioural templates
control loop
crowded environments
deterministic elements
high level logic
human behaviour model
human environments
human interactions
low level predictive modelling
robot motion planning algorithm
social force model
statistical model checking
stochastic elements
Force
Planning
Predictive models
Protocols
Sensors
Trajectory
Visualization
Domains
Computer Science [cs]
Origin : Files produced by the author(s)
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