Abstract
This chapter addresses the problem of path planning considering uncertainty criteria over the belief space. We propose a path planning algorithm that uses a determinant-based measure of uncertainty and a reduced representation of the environment, in order to obtain the minimum uncertainty path from a roadmap. The determinant-based measure of uncertainty is borrowed from the active SLAM literature. We also present in this chapter an overview of the active SLAM problem. Our path planning proposal does not require a priori knowledge of the environment due to the construction of the roadmap via a graph-based SLAM algorithm. We report experimental results of our proposal in four datasets that show its feasibility to obtain the minimum uncertainty path towards an autonomous navigation framework. We also show an improvement in the computation time with respect to the state of the art.
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Carrillo, H., Castellanos, J.A. (2015). Navigation Under Uncertainty Based on Active SLAM Concepts. In: Busoniu, L., Tamás, L. (eds) Handling Uncertainty and Networked Structure in Robot Control. Studies in Systems, Decision and Control, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-26327-4_9
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