Common Software Defects in Embedded Programming

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Participation in ABU Robocon 2009:

The contest was held in Tokyo, Japan in August 2009. 
A demo of robots working:

ABU Robocon 2009, Tokyo, "Travel Together for the Victory Drums" rule animation:

System Identification and Controller Design of Open-loop Unstable Ball and Beam System

Balancing a Ball on a tilting beam is very interesting classical problem in the field of control system engineering. In this paper an Proportional Derivative (PD) controller has been designed for a nonlinear, open-loop unstable Ball and Beam system. The linearized system ID has been deduced via bode plot decomposition method. To create the Bode plot - first, a zero has been added very close to origin for stabilizing that unstable system. Then a set of input/output data has been generated and corresponding Lissajous patterns have been generated from those input/output data. Examining the Lissajous patterns the Bode plot has been generated. Linearized system transfer function has been found by analyzing the Bode plot. Then a PD controller has been designed for the linearized system that meets the desired design specifications. Finally the PD controller is applied on the original nonlinear plant. Success of this method is verified by extensive simulation. A digital realization of the PD controller is implemented, also.


Complete report can be found here.

Corridor Navigating Autonomous Wheelchair

This project “Corridor Navigating Autonomous Wheelchair” aims at providing corridor navigating capabilities to powered wheelchair using imaging and proximity sensors. The system analyses each frame of corridor captured by the image sensor mounted on wheelchair and generates appropriate guiding control signals to the motors of the wheelchair. The proximity sensors mounted on the device help in obstacle detection and collision avoidance and also aid in the locomotion. The system also contains manual control which can bypass the automatic controlling mechanism in case the automatic system fails or the user wishes to do so. The project consists of two versions, one with PC control and the other with FPGA control. The PC controlled version of the system has been successfully built and tested. Several simulation tests have been conducted for the FPGA version of the system.

Please find the complete report here.

Video1                       


Video2

Appearance-based Object Classification

The objective of Project 3 is to construct Eigenspace manifolds of images and to use it for object identification and pose estimation. For this purpose, a set of training images of different objects depicted in Figure 1(left) had been captured. A set of test images were also captured in the similar fashion (i.e, they have the same statistics).

Figure 1: Images of objects used in this project for classification(Left). Each objectis acquired by placing them at the center of an imaging sphere and sampling lines of constant co-latitude [right].

 Computing and  analyzing the Eigenspace:

In this part of the project, the eigenvectors of the images of different objects were calculated and projected in the eigenspace. In order to construct local manifolds, a set of 128 training images of every single objects were considers. Local Eigenspace manifolds for some of the objects are given below.

Local manifold for object Boat

Local manifold for Cabinet

Local manifold for Cup

The manifolds tells us about the distribution of training images on Eigenspace. If we look at the local manifold of cabinet, we observe that most of the part of manifold overlaps, which is due to the fact that the cabinet looks alike from different orientation.


The principle eigenimages for different images are given below.

Principle Eigenimages of Boat

Principle Eigenimages of Cabinet

 

Principle Eigenimages of Cup

Using the relation of Energy recovery ratio, the subspace dimensions were calculated for the desired level of accuracy. If we plot a plot a graph of energy contained by the eigenimages versus the number of subspace dimensions, we can see that most of the energy can be recovered by considering lower no. of subspace dimensions.

Energy recovery versus subspace dimension curve for Boat

Energy recovery versus subspace dimension curve for Cabinet

A table containing the list of subspace dimensions for different objects is given below.

Calculation of subspace dimension for different energy recovery.

In order to construct global manifold, all the training images of different objects were considered at once. It was basically done by two methods.

1. Concatenate all 2B images for each of the n objects into a matrix G and
   compute the eigenspace of this G.
2. Concatenate each of the kn-dimensional eigenspaces into a matrix L and
   compute the eigenspace of L.

The global manifolds for both the methods are given below.

Global manifold using G matrix

Global manifold using L matrix

 The principle Eigenimages for global manifolds are given below.

Global Eigenimages using G matrix

Global Eigenimages using L matrix

The energy recovery plot of global eigenspace are given below.

Energy recovery versus subspace dimension curve for Global manifold(using G matrix)

Energy recovery versus subspace dimension curve for Global manifold(using L matrix)

A plot showing the projection of test images on a local manifold is given below. Since the test images are also captured under the same statistics, it falls close to the points on the manifold. Then we find out the minimum distance of the projected point to the points on the manifold. The one with the least distance is correct match.

A screen shot of the user interaction part of the program is shown below.

 Here are the some of the example of projected test image and the match result with pose estimation.

 For the Global manifold, L matrix decreases computational load but accuracy is comparatively higher in G matrix approach. 

 An attempt to map the appearance manifold to some low dimensional manifold with some desirable geometric structure was made. In this case I tried to map the manifold on a two dimensional circle. By doing so, it allows for the classification of the object to be computed using simple calculation compare to the exhaustive search that we performed in the previous case. This drastically decreases the computational cost.
Results are given below.

(Mt*A-B)<2.6

M*A-B)<0.21

M*A-B)<0.009

References:

1. Designing Eigenspace Manifolds: With Application to Object Identification and Pose Estimation, by Dr. R. C. Hoover, Dr. R. G. Roberts