Title
Sampling-based Motion Planning for Active Multirotor System Identification.
Abstract
This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.
Year
DOI
Venue
2017
10.1109/ICRA.2017.7989453
ICRA
DocType
Volume
Citations 
Conference
abs/1612.05143
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Rik Bähnemann131.08
M. Burri234318.62
Enric Galceran323613.50
Roland Siegwart47640551.49
Juan I. Nieto593988.52