Motion‐ and Uncertainty‐aware Path Planning for Micro Aerial Vehicles1. Issue 4 (July 2014)

• Record Type: Journal Article
• Authors: Achtelik, Markus W.; Lynen, Simon; Weiss, Stephan; Chli, Margarita; Siegwart, Roland; Kendoul, Farid; Siegwart, Roland; Roberts, Jonathan
• Is Part Of: Journal of field robotics. Volume 31:Issue 4(2014:Jul./Aug.)
• Page Start: 676
• Page End: 698
• Publication Date: 2014-07
• Subjects: Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21522
• Languages: English
• ISSNs: 1556-4959

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Localization and state estimation are reaching a certain maturity in mobile robotics, often providing both a precise robot pose estimate at a point in time and the corresponding uncertainty. In the bid to increase the robots' autonomy, the community now turns to more advanced tasks, such as navigation and path planning. For a realistic path to be computed, neither the uncertainty of the robot's perception nor the vehicle's dynamics can be ignored. In this work, we propose to specifically exploit the information on uncertainty, while also accounting for the physical laws governing the motion of the vehicle. Making use of rapidly exploring random belief trees, here we evaluate offline multiple path hypotheses in a known map to select a path exhibiting the motion required to estimate the robot's state accurately and, inherently, to avoid motion in modes, where otherwise observable states are not excited. We demonstrate the proposed approach on a micro aerial vehicle performing visual‐inertial navigation. Such a system is known to require sufficient excitation to reach full observability. As a result, the proposed methodology plans safe avoidance not only of obstacles, but also areas where localization might fail during real flights compensating for the limitations of the localization methodology available. We show that our planner actively improves the precision of the state estimation by selecting paths that minimize the uncertainty in the estimated states. Furthermore, our experiments illustrate by comparison that a naive planner would fail to reach the goal within bounded uncertainty in most cases.</p> </abstract>