25 research outputs found
Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking
In this paper, we propose an online learning approach that enables the
inverse dynamics model learned for a source robot to be transferred to a target
robot (e.g., from one quadrotor to another quadrotor with different mass or
aerodynamic properties). The goal is to leverage knowledge from the source
robot such that the target robot achieves high-accuracy trajectory tracking on
arbitrary trajectories from the first attempt with minimal data recollection
and training. Most existing approaches for multi-robot knowledge transfer are
based on post-analysis of datasets collected from both robots. In this work, we
study the feasibility of impromptu transfer of models across robots by learning
an error prediction module online. In particular, we analytically derive the
form of the mapping to be learned by the online module for exact tracking,
propose an approach for characterizing similarity between robots, and use these
results to analyze the stability of the overall system. The proposed approach
is illustrated in simulation and verified experimentally on two different
quadrotors performing impromptu trajectory tracking tasks, where the quadrotors
are required to accurately track arbitrary hand-drawn trajectories from the
first attempt.Comment: European Control Conference (ECC) 201
Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge
This work presents an online learning-based control method for improved
trajectory tracking of unmanned aerial vehicles using both deep learning and
expert knowledge. The proposed method does not require the exact model of the
system to be controlled, and it is robust against variations in system dynamics
as well as operational uncertainties. The learning is divided into two phases:
offline (pre-)training and online (post-)training. In the former, a
conventional controller performs a set of trajectories and, based on the
input-output dataset, the deep neural network (DNN)-based controller is
trained. In the latter, the trained DNN, which mimics the conventional
controller, controls the system. Unlike the existing papers in the literature,
the network is still being trained for different sets of trajectories which are
not used in the training phase of DNN. Thanks to the rule-base, which contains
the expert knowledge, the proposed framework learns the system dynamics and
operational uncertainties in real-time. The experimental results show that the
proposed online learning-based approach gives better trajectory tracking
performance when compared to the only offline trained network.Comment: corrected version accepted for ICRA 201
A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions
Due to changes in model dynamics or unexpected disturbances, an autonomous
robotic system may experience unforeseen challenges during real-world
operations which may affect its safety and intended behavior: in particular
actuator and system failures and external disturbances are among the most
common causes of degraded mode of operation. To deal with this problem, in this
work, we present a meta-learning-based approach to improve the trajectory
tracking performance of an unmanned aerial vehicle (UAV) under actuator faults
and disturbances which have not been previously experienced. Our approach
leverages meta-learning to train a model that is easily adaptable at runtime to
make accurate predictions about the system's future state. A runtime monitoring
and validation technique is proposed to decide when the system needs to adapt
its model by considering a data pruning procedure for efficient learning.
Finally, the reference trajectory is adapted based on future predictions by
borrowing feedback control logic to make the system track the original and
desired path without needing to access the system's controller. The proposed
framework is applied and validated in both simulations and experiments on a
faulty UAV navigation case study demonstrating a drastic increase in tracking
performance.Comment: 2021 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS) (to appear) 2021 copyright IEE
An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers
Developing optimal controllers for aggressive high-speed quadcopter flight is
a major challenge in the field of robotics. Recent work has shown that neural
networks trained with supervised learning can achieve real-time optimal control
in some specific scenarios. In these methods, the networks (termed G&CNets) are
trained to learn the optimal state feedback from a dataset of optimal
trajectories. An important problem with these methods is the reality gap
encountered in the sim-to-real transfer. In this work, we trained G&CNets for
energy-optimal end-to-end control on the Bebop drone and identified the
unmodeled pitch moment as the main contributor to the reality gap. To mitigate
this, we propose an adaptive control strategy that works by learning from
optimal trajectories of a system affected by constant external pitch, roll and
yaw moments. In real test flights, this model mismatch is estimated onboard and
fed to the network to obtain the optimal rpm command. We demonstrate the
effectiveness of our method by performing energy-optimal hover-to-hover flights
with and without moment feedback. Finally, we compare the adaptive controller
to a state-of-the-art differential-flatness-based controller in a consecutive
waypoint flight and demonstrate the advantages of our method in terms of energy
optimality and robustness.Comment: 7 pages, 11 figure