Sampling-based Motion Planning for Active Multirotor System Identification

TL;DR

This paper introduces a sampling-based motion planning algorithm for multirotor MAVs that autonomously generates trajectories to efficiently identify system parameters, significantly reducing convergence time and uncertainty.

Contribution

The paper presents a novel sampling-based planning method that actively optimizes MAV trajectories for rapid system identification using belief dynamics approximation.

Findings

Reduces model parameter convergence time by a factor of four.

Validates approach in simulation and real-world experiments.

Creates trajectories that maximize information gain for 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.