Abstract
The accurate localization of robots in indoor environments is a well-known measurement problem, which becomes particularly critical in crowded scenarios, where collision with obstacles, objects, and human operators is very high. In this article, the problem is solved through an optimal path planning strategy that keeps into account the ranging measurement uncertainty from a given set of wireless anchors (e.g., ultra wideband (UWB) transceivers) that are used to track the position of the robot in a known reference frame through multilateration. In particular, assuming to use an observability-based filter (ObF), namely, a position tracking estimator conceived to ensure the global observability of the robot state (namely, its planar position and orientation) with quantifiable uncertainty, a potential-based path planning strategy is applied to drive the robot toward the goals while minimizing localization uncertainty. Several simulation results using two alternative artificial potential functions confirm the correctness and optimal performances of the proposed approach, which is entirely driven by observability and measurement uncertainty concerns and, as such, it is quite different and novel in the context of robots’ path planning techniques.
Published Version
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