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Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation

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Academic year: 2019

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Figure

Figure 1.1: Freezing robot problem as a result of unconstrained prediction. The robot is representedas the black star, and the ellipses represent the predictive covariance
Figure 1.2: If the predictive covariance of individual agents is held to a small value, navigation canproceed in an optimal manner—if the crowd is sparse enough.
Figure 1.3: (a)common crowd configurations (shoulder to shoulder walking, sparse crowd) can lead to evasivemaneuvering by the robot Even if we hold pedestrian predictive covariance to be extremely small (grey circles), (b) An illustration of what is occurri
Figure 1.4: Including interaction in the predictive models causes the prediction tubes to bend aroundone another, in a jointly cooperative way
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