Active Task-Space Sensing and Localization of Autonomous Vehicles

In this paper, an active line-of-sight-sensing (LOS) methodology is proposed for the docking of autonomous vehicles/robotic end-effectors. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). In such instances, a guidance-based technique must be employed to move the vehicle to its desired pose using corrective actions at the final stages of its motion. The objective of the proposed guidance method is, thus, to successfully minimize the systematic errors of the vehicle, accumulated after a long-range motion, while allowing it to converge within the random noise limits via a three-step procedure: active LOS realignment, determination of the new (actual) location of the vehicle, and implementation of a corrective action. The proposed system was successfully tested via simulation for a three degree-of-freedom (dof) planar robotic platform and via experiments.

I. Introduction

The fundamental localization (docking) problem refers to the on-line guidance of the autonomous device to achieve a desired docking pose within required tolerances [1]. Addressing this problem via a localization technique that depends on feedback solely from internal sensors would result in the accumulation of systematic errors over time. Use of external task-space sensors, via passive or active sensing techniques, thus, has been proposed in order to reduce the detrimental impact of such errors on vehicle-motion accuracy. In this context, researchers have suggested the use of either (i) high-precision proximity sensors in the latter stage of a two-stage, on-line, navigation-based path-planning algorithm [2]–[6], or (ii) high-speed cameras in visual-servoing mode [7]–[9].