Category Archives: Robotics

Robotics for dummies- is it required!!!

Robotics for dummies

Compiled by :Vineeta Tawney

CNESystems

Definition of Robotics

RFDummies1According to the Robot Institute of America (1979) a robot is:
“A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks”.

A lot of inspiring definition will be found in Webster. According to Webster a robot is:
“An automatic device that performs functions normally ascribed to humans or a machine in the form of a human.” Continue reading

Intelligent Automation – The future of Robotic Process Automation

By : Vineeta Tawney
CNESystems

WHAT IS RPA?
Robotic Process Automation (RPA) is considered as a revolution in the field of business automation. It is the use of software robots (bots) with Artificial Intelligence (AI) and Machine Learning to carry out some high-volume tasks, which were carried out by humans earlier. RPA is used to streamline business operations, to automate standard business practices, and to reduce costs.

Three broad categories of RPA are classified into three:

Probots: Probots are bots that follow repeatable rules to process data.
Knowbots: User-specified information is gathered and stored using Knowbots.
Chatbots: Chat bots are virtual agents who can respond to customer queries through chat in real-time.

Artificial Intelligence (AI) and Machine Learning

Artificial Intelligence, also termed as machine intelligence, is a machine that thinks and works like a human. AI performs complex tasks like problem-solving, speech recognition, etc. Machine Learning is a method of data analysis that automates the analytical model; it is like teaching a computer how to make an accurate prediction when fed with any data. Advancement in machine learning (ML) and artificial intelligence has paved the way for Intelligent Automation (IA).
Continue reading

Capsule Networks

Capsule Networks

By Vineeta Tawney
CNESystems

What are Capsule Networks?
It is also known as Capsule Neural Network. It is a machine learning system which is used to better model hierarchical relationships. It is commonly known as CapsNet.
Definition of Capsule Networks
In simpler words, CapsNet is combined of numerous capsules. Every capsule is a group of neurons which learns to identify an object (e.g., a square) in a given region of the image.
It outputs a vector (e.g., an 8-dimensional vector) whose length represents the estimated probability that the object is present, and whose orientation (e.g., in 8D space) encodes the object’s pose parameters (e.g., precise position, rotation, etc.). If the position of an object is changed a little (e.g., shifted, rotated, resized, etc.) then the capsule output will be a vector image of the same length but placed slightly differently.
A CapsNet is arranged in multiple layers, very much like a regular neural network. The lowest layer capsules are called primary capsules: each of them obtains a small region of the image as input (called its receptive field). It tries to detect the presence and pose of a specific pattern, for example, a square. The higher layer Capsules are called routing capsules, identify larger and more complex objects, such as boats.
What do Capsule Networks do:
The purpose behind Capsule Networks is to perform computer vision as inverse graphics. In graphics, an object is represented through using a tree part. A specific rotation describes the conversion from the viewpoint of the part to the viewpoint of the parent.
CapsNets are encouraged by these tree-like representations and try to learn conversions relating the parts of an object to the whole. Capsules could be viewed as parts/object, with parent parts/objects that are also capsules.
Capsule Networks Deep Learning
Deep Learning is a feature of artificial intelligence (AI). In simple words, Deep Learning is a way to automate Predictive Analytics. Whereas traditional machine learning algorithms are linear, Deep Learning algorithms are stacked in a hierarchy of increasing difficulty and abstraction.

In simple terms, a CapsNet is combined of capsules and a capsule is a group of artificial neurons that learn to detect a specific object in a given region of the image. It produces a vector whose length represents the estimated probability of the object’s presence and whose orientation encodes the object’s position, size, and rotation. If the object is customized (for example, translated, rotated, or resized), the capsule will then produce a vector of the similar length, but with a slightly different orientation.
Capsule Networks: Deeper Analysis
CapsNet is organized in multiple layers. The deep layer is composed of primary capsules that receive a small portion of the input image and detect the presence and placement of a subject, such as a square, for example.
The high layer capsules, more commonly known as routing capsules, are capable of detecting larger and more complex objects. Capsules communicate mostly through an iterative “routing-by-agreement” mechanism: a lower level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.
“Lower level capsule can send its input to the higher-level capsule that ‘agrees’ with its input. This is the essence of the dynamic routing algorithm.” Most professionals working on Capsule Networks paper believe CapsNets to be an improvement on convolutional neural networks (CNN).
CapsNets attempts to solve the issues caused by Max Pooling and Deep Neural Networks like loss of information regarding the order and orientation of features. For example, a CNN used for face recognition will extract certain facial features of the image such as eyes, eyebrows, a mouth, a nose etc. Then the higher-level layers (the ones deeper down within the network) will merge those features and check if all of those features were found within the image regardless of order.

The mouth and nose may have switched places and your eyes can be sideways in the picture, but the CNN can still put together the facts and classify that as a face. This problem exacerbates the deeper your network gets as the features become more and more abstract and also shrink in size because of pooling and filtering. The idea behind CapsNets is that the low-level features could also be arranged in a certain order for the object to be classified as a face.
For example, it would learn that your nose must be between your two eyes and your mouth must be below that. Images with these features in the specific order can then be classified as a face, everything else will be rejected.
The publication of “Dynamic Capsule Routing” has led various researchers to work intensely towards refining algorithms and implementations, and advances have been published at a speedy pace.
Advantages of using CapsNets for Deep Learning:
1. Good preliminary results.
2. Requires less training data.
3. Works good with overlapping objects.
4. Potentially good on crowded scene.
5. Can detect partially visible objects.
6. Results are interpretable, components hierarchy can be mapped.
7. Equivariance (classifier adapt to small changes in input).
Disadvantages of using CapsNets
1. No known yet accuracy on large images.
2. Slow training time (so far).
3. Nonlinear squashing may not reflect the probability nature.
Future of Capsule Networks
Capsule Networks have presented a new building block that can be used in Deep Learning to better model hierarchical relationships inside of internal knowledge representation of a neural network.
To know more about Capsule Networks deep learning, refer critical essays on CapsNet models for Deep Learning. Or refer Capsule Networks paper for expert discussions on Deep Learning. Also, refer papers that contain discussions on Hinton’s Capsule Networks.

Robotics Assisting in Climate Change

“Maybe they could help”

Author: Pratibha Ambesinge

Date: 12-04-2019

Introduction

Climate change, while having an obvious and brutal impact on our atmosphere, effects our oceans with an even greater magnitude. Rising sea levels, ocean acidification, and ecosystem reform are just three of the issues taking our oceans by storm. 

Until now, scientists have been limited to expensive, stationary machines that stay at sea for short periods of only a few months at a time to take samples and relay data. Now a new horizon has opened up, bridging the areas of sea exploration and robotics with environmental activism.

Using cameras and sensors to navigate their environment, the SnoMotes will be able to work as an autonomous team without the use of remote control. Once released from a selected base camp, the robots will collaborate to ensure that the selected research area is well covered and can venture into areas that are unsafe for humans.

Two navigation systems are being developed. The first enables the robots “bid” on a desired location based on their proximity to the location and taking into consideration how well their instruments are working. The second involves the use of a mathematical “net” that can be applied to particular research areas.

Three prototypes have so far been created to prove mobility (which is a big challenge in white-out conditions) and communications capabilities, with a full range of sensors to be added at a later date. There are also plans for larger rovers.

“In order to say with certainty how climate change affects the world’s ice, scientists need accurate data points to validate their climate models,” said Ayanna Howard, lead on the project and an associate professor in the School of Electrical and Computer Engineering at Georgia Tech. “Our goal was to create rovers that could gather more accurate data to help scientists create better climate models. It’s definitely science-driven robotics.”

Robotics is already a multi-billion dollar industry with its core in manufacturing automation. The market for personal robots that can perform domestic tasks such as cleaning, vacuuming, and mowing is already significant. Yet this is only the beginning of the robotics revolution. Robotic driving and flying will transform transportation, reducing energy costs. Efficient walking robots that can perceive and manipulate will bring robots into homes, hospitals, and retail environments, where they will assist the elderly and the handicapped. Robots that function in environments inaccessible to humans by swimming through coral reefs or hovering above a rain forest canopy can yield powerful insights for scientists studying phenomena such as climate change.

ROVs

An ROV, as many of you may already know, is a remotely operated vehicle, most commonly in use underwater. In the past, these machines have been used for exploration and discovery by large organizations with deep pockets.
Deep water ROVs (with costs in the hundreds of thousands) are responsible for the discovery of the RMS Titanic and other lost vessels.

Now that start-ups from around the country have made certain ROV components available to the private sector for lower rates, the technology of underwater vehicles is starting to become popular.

Can Technology Reverse Climate Change?

Do you believe that climate change is a vast left-wing conspiracy that does little more than create jobs for scientists while crippling businesses with pointless regulation? Or, quite the contrary, are you convinced that climate change is the biggest crisis confronting the planet, uniquely capable of wreaking havoc on a scale not seen in recorded history?

Many of you are probably in one camp or the other. No doubt some of you will tell us how disappointed/angry/outraged you are that we (a) gave credence to this nonsense or (b) failed to convey the true urgency of the situation.

The Role of Technology in Climate Change

Avoiding the impact of climate change simply means reducing the emission of green house gases. Climate change solutions lie in technological innovations and creativity in response to the effects of climate change. Across the years it has been evident policy development and change around climate change has been full of controversy. A look at the situation in different countries this is quite evident. A prominent example being the very unclear stand of the United States on the Climate Change agenda. That not with standing various countries have committed to the climate change agenda and in different ways supporting the adaptation and mitigation strategies. One way of addressing climate change as mentioned is through innovative technological solutions. The true threat of climate change is the effect this has on Sustainable Development the adverse effects are felt by the poor and future generations.

Robots Solve Climate Problem

The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The confluence of these forces of mankind and nature intersect beautifully in the alternative energy market. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet.

Drone companies are also entering the maintenance space. Upstart aerial power claims to have designed a “SolarBrush” quadcopter that cleans panels. The solar-powered drone professes to reduce 60% of a solar farm’s operational costs. Solar Brush also promises an 80% savings over existing solutions like Ecoppia since there are no installation costs. However, Aerial Power has yet to fly its product in the field as it is still in development. SolarPower is selling its own drone survey platform to assess development sites and oversee field operations.

Drones today serve a variety of functions, from capturing aerial photography to assisting in military operations. But one particular service may end up being their chief contribution: by acting as mitigating forces against the impacts of climate change. These little gadgets are now capable of everything from fighting dwindling bee populations and reducing carbon emissions to tracking changes in wildlife population and gathering water samples.

On a solar farm, workers typically do inspections by slowly making their way through the field of solar panels. To inspect a wind turbine, for example, a worker must climb to the top of a tower and then dangle by wire, a time-consuming and potentially dangerous task. Drones could also help to inspect the vast network of transmissions lines our electrical grid depends on.

Climate change is a problem impacting every part of our world, including the creatures that live in it. To understand the impacts of climate change and mitigate them, we need to go to places that are hard to access — something drones excel at. Whether they’re in the air or underwater, drones may be a vital tool in finding solutions to the many challenges of climate change.

Tags: Robotics, Climate change, Technology, Robots, AI, Machine Learning, Tech, Automation, Education