A Probabilistic Steering Parameter Model for Deterministic Motion Planning Algorithms

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dc.contributor.authorAgethen, Philippen_US
dc.contributor.authorGaisbauer, Felixen_US
dc.contributor.authorRukzio, Enricoen_US
dc.contributor.editorChen, Min and Benes, Bedrichen_US
dc.date.accessioned2019-03-17T09:57:03Z
dc.date.available2019-03-17T09:57:03Z
dc.date.issued2019
dc.description.abstractThe simulation of two‐dimensional human locomotion in a bird's eye perspective is a key technology for various domains to realistically predict walk paths. The generated trajectories, however, are frequently deviating from reality due to the usage of simplifying assumptions. For instance, common deterministic motion planning algorithms predominantly utilize a set of static steering parameters (e.g. maximum acceleration or velocity of the agent) to simulate the walking behaviour of a person. This procedure neglects important influence factors, which have a significant impact on the spatio‐temporal characteristics of the finally resulting motion—such as the operator's physical conditions or the probabilistic nature of the human locomotor system. In overcome this drawback, this paper presents an approach to derive probabilistic motion models from a database of captured human motions. Although being initially designed for industrial purposes, this method can be applied to a wide range of use cases while considering an arbitrary number of dependencies (input) and steering parameters (output). To underline its applicability, a probabilistic steering parameter model is implemented, which models velocity, angular velocity and acceleration as a function of the travel distances, path curvature and height of a respective person. Finally, the technical performance and advantages of this model are demonstrated within an evaluation.The simulation of two‐dimensional human locomotion in a bird's eye perspective is a key technology for various domains to realistically predict walk paths. The generated trajectories, however, are frequently deviating from reality due to the usage of simplifying assumptions. For instance, common deterministic motion planning algorithms predominantly utilize a set of static steering parameters (e.g. maximum acceleration or velocity of the agent) to simulate the walking behaviour of a person. This procedure neglects important influence factors, which have a significant impact on the spatio‐temporal characteristics of the finally resulting motion—such as the operator's physical conditions or the probabilistic nature of the human locomotor system. In overcome this drawback, this paper presents an approach to derive probabilistic motion models from a database of captured human motions. Although being initially designed for industrial purposes, this method can be applied to a wide range of use cases while considering an arbitrary number of dependencies (input) and steering parameters (output).en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13591
dc.identifier.issn1467-8659
dc.identifier.pages549-563
dc.identifier.urihttps://doi.org/10.1111/cgf.13591
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13591
dc.publisher© 2019 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjecthuman simulation
dc.subjectanimation
dc.subjectmotion capture
dc.subjectanimation
dc.subjectmotion planning
dc.subjectanimation
dc.subjectComputing methodologies → Modelling and simulation
dc.subjectModel development and analysis
dc.subjectAnimation
dc.titleA Probabilistic Steering Parameter Model for Deterministic Motion Planning Algorithmsen_US
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