Sampling-based motion planning for active multirotor system identification

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.

Authors

• Rik Bähnemann
• Michael Burri
• Enric Galceran
• Roland Siegwart
• Juan I. Nieto 0001

Keywords

• Trajectory
• Solid modeling
• Robots
• Planning
• Uncertainty
• Covariance matrices
• Calibration
• System Identification
• Path Planning
• Model Parameters
• Unknown Parameters
• Kalman Filter
• Parameter Uncertainty
• Extended Kalman Filter
• Micro Air Vehicles
• Parameter Estimates
• High-dimensional
• Covariance Matrix
• Nonlinear Model
• Rotational Speed
• Morphine
• Brownian Motion
• Information Gathering
• Model Predictive Control
• Configuration Space
• Covariance Parameters
• Random Tree
• Rapidly-exploring Random Tree
• Nominal State
• Base Frame
• Random Trajectories
• Belief Propagation
• Flat State
• World Frame
• Robot State
• Moment Of Inertia
• Dimensional Space