2 research outputs found
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study
A Hyperspectral image contains much more number of channels as compared to a
RGB image, hence containing more information about entities within the image.
The convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP)
have been proven to be an effective method of image classification. However,
they suffer from the issues of long training time and requirement of large
amounts of the labeled data, to achieve the expected outcome. These issues
become more complex while dealing with hyperspectral images. To decrease the
training time and reduce the dependence on large labeled dataset, we propose
using the method of transfer learning. The hyperspectral dataset is
preprocessed to a lower dimension using PCA, then deep learning models are
applied to it for the purpose of classification. The features learned by this
model are then used by the transfer learning model to solve a new
classification problem on an unseen dataset. A detailed comparison of CNN and
multiple MLP architectural models is performed, to determine an optimum
architecture that suits best the objective. The results show that the scaling
of layers not always leads to increase in accuracy but often leads to
overfitting, and also an increase in the training time.The training time is
reduced to greater extent by applying the transfer learning approach rather
than just approaching the problem by directly training a new model on large
datasets, without much affecting the accuracy
Control Barrier Functions in UGVs for Kinematic Obstacle Avoidance: A Collision Cone Approach
In this paper, we propose a new class of Control Barrier Functions (CBFs) for
Unmanned Ground Vehicles (UGVs) that help avoid collisions with kinematic
(non-zero velocity) obstacles. While the current forms of CBFs have been
successful in guaranteeing safety/collision avoidance with static obstacles,
extensions for the dynamic case with torque/acceleration-controlled unicycle
and bicycle models have seen limited success. Moreover, with these nonholonomic
UGV models, applications of existing CBFs have been conservative in terms of
control, i.e., steering/thrust control has not been possible under certain
common scenarios. Drawing inspiration from the classical use of collision cones
for obstacle avoidance in path planning, we introduce its novel CBF formulation
with theoretical guarantees on safety for both the unicycle and bicycle models.
The main idea is to ensure that the velocity of the obstacle w.r.t. the vehicle
is always pointing away from the vehicle. Accordingly, we construct a
constraint that ensures that the velocity vector always avoids a cone of
vectors pointing at the vehicle. The efficacy of this new control methodology
is experimentally verified on the Copernicus mobile robot. We further extend it
to the bicycle model and demonstrate collision avoidance under various
scenarios in the CARLA simulator.Comment: Submitted to 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS). 8 pages, 8 figures, For supplement video follow
https://youtu.be/4qWYaWEPduM. The first and second authors have contributed
equall