5,644 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
Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV)
and its control is one of the recent research topics related to the field of
autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing
Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced
features like quick flight, vertical take-off and landing, hovering, and fast
turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed
and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized
to demonstrate the NIFW MAV model, which has points of interest over first
principle based modelling since it does not depend on the system dynamics,
rather based on data and can incorporate various uncertainties like sensor
error. The same clustering strategy is used to develop an adaptive fuzzy
controller. The controller is then utilized to control the altitude of the NIFW
MAV, that can adapt with environmental disturbances by tuning the antecedent
and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial
Intelligence and Soft Computing Researc
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 New Data Source for Inverse Dynamics Learning
Modern robotics is gravitating toward increasingly collaborative human robot
interaction. Tools such as acceleration policies can naturally support the
realization of reactive, adaptive, and compliant robots. These tools require us
to model the system dynamics accurately -- a difficult task. The fundamental
problem remains that simulation and reality diverge--we do not know how to
accurately change a robot's state. Thus, recent research on improving inverse
dynamics models has been focused on making use of machine learning techniques.
Traditional learning techniques train on the actual realized accelerations,
instead of the policy's desired accelerations, which is an indirect data
source. Here we show how an additional training signal -- measured at the
desired accelerations -- can be derived from a feedback control signal. This
effectively creates a second data source for learning inverse dynamics models.
Furthermore, we show how both the traditional and this new data source, can be
used to train task-specific models of the inverse dynamics, when used
independently or combined. We analyze the use of both data sources in
simulation and demonstrate its effectiveness on a real-world robotic platform.
We show that our system incrementally improves the learned inverse dynamics
model, and when using both data sources combined converges more consistently
and faster.Comment: IROS 201
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