Abstract
Robots often use a topological graph to perform their navigation. To perform this navigation efficiently the traversal time along the edges of the graph needs to be properly estimated.
In this paper, we show an approach which estimates the traversal time along edges using only the information of the traversal time from one vertex to any other vertex in the graph. The approach does not need any detailed information which edges were actually traversed. Instead, it is assumed that the robot moves the fastest path in the graph.
To address the problem of noise measurements the approach uses a probabilistic model to estimate the traversal time. This paper we show how the probabilistic model can be simplified to allow to solve the estimation problem efficiently.
Finally, we show an evaluation of the approach on different sets of generated graphs and traversals showing that the approach estimates the of the traversal times for the shortest path correctly.
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Notes
- 1.
For space reasons we omit the proof but the proof follows closely the derivation of the equation.
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Acknowledgement
This work is supported by the Austrian Research Promotion Agency (FFG) under grant 843468.
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Mühlbacher, C., Gspandl, S., Reip, M., Steinbauer, G. (2018). Adapting Edge Weights for Optimal Paths in a Navigation Graph. In: Ferraresi, C., Quaglia, G. (eds) Advances in Service and Industrial Robotics. RAAD 2017. Mechanisms and Machine Science, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-61276-8_41
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DOI: https://doi.org/10.1007/978-3-319-61276-8_41
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