Optimal path planning for uncertain exploration

Authors:
Andrew T. Klesh;Pierre T. Kabamba;Anouck R. Girard
Affiliations:
Aerospace Engineering, University of Michigan, Ann Arbor, MI;Aerospace Engineering, University of Michigan, Ann Arbor, MI;Aerospace Engineering, University of Michigan, Ann Arbor, MI
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 5
Cited 0

Robot motion planning: a distributed representation approach

International Journal of Robotics Research
An algorithm for planning collision-free paths among polyhedral obstacles

Communications of the ACM
Robot Motion Planning

Robot Motion Planning
Planetary Rover Developments Supporting Mars Exploration, Sample Return and Future Human-Robotic Colonization

Autonomous Robots
Optimal sensor placement and motion coordination for target tracking

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Exploration always occurs in the presence of uncertainty. In this paper, we consider path planning for autonomous vehicles equipped with range-based sensors and traveling in an uncertain area. The mission of the vehicles is to explore a set of objects of interest while reducing uncertainty in object position, visibility and state. A connection is shown between the Kalman filter (used to reduce uncertainty) and the so-called Shannon model for exploration through the use of a range-based covariance. This connection is exploited to estimate states and to travel between objects of interest. A bound on the covariance error and several illustrative examples are provided.