With Herbie, humanoid robots are finally here

The concept of anthropomorphic robots has always captured the human imagination, starting with playwright Karel Čapek, who introduced the word “Robot” in the 1920s, to Isaac Asimov’s incredible science fiction. But, in reality, robots have been relegated to doing menial tasks such as lifting things or executing repetitive actions, as is the case with industrial robots. 

However, the latest developments in Artificial intelligence (AI) and advanced automation could give us advanced humanoid robots, capable of carrying out multiple functions that not only replace manual labour in manufacturing industries, but also have significant domestic, everyday uses. While AI enables machines to mimic remarkable human cognitive functions, the developments in the field of automation have widened the scope of its usage across different areas such as agriculture, manufacturing, business, analytics, and more. 

More significantly, AI has been able to transform elementary robotics to intelligent humanoids capable of interpreting voice commands and gestures, interacting with human beings and its own environment, locomoting, and other computational abilities. 

In this article we will discuss one such humanoid known as Herbie and delve into its mechanical structure and sensory faculties. 

Herbie

This humanoid model is a type of Socially Assistive Robot (SAR), developed to facilitate the rehabilitation of children with Cerebral Palsy (CP). CP is a congenital disorder caused by abnormal brain development. It affects muscular movement, motor skills, and results in various disabilities. While Herbie is not the first attempt to provide assistance in the field of medicine and therapy, it is a multi-functional humanoid built to address real-time challenges by leveraging different faculties such as vision, speech and hearing, smell, etc. 

Mechanical Structure of Herbie

The structure of Herbie is made of acrylic sheets which are light-weight. Herbie consists of a skeleton and various chambers, as well as a base and a head. It also has different subsections that hold electronic circuits and enable power supply. The skeletal structure consists of an acrylic tube that is 200mm in diameter and 400mm in height. The base of Herbie is made up of a circular acrylic sheet that has a radius of 192mm and a thickness of 5mm.

The base also consists of two clamps that hold the motor, and two castor wheels attached beneath it. The motors are DC motors and run at 60rpm. And each motor is individually powered and controlled by a driver through Raspberry Pi that functions as Herbie’s brain. 

The latest upgraded version of Herbie is more efficient and stable, with comparatively low power consumption. 

Application of Intelligence

The artificial cognition of Herbie facilities:

• Image processing through the faculty of vision
• Speech processing through faculties of hearing and speaking
• Gaseous detectors through the faculty of smell
• Ultrasonic sensors through the faculty of touch

Faculty of vision and image processing

Herbie’s faculty of vision is used particularly for image processing that can help with:

• Surveillance or obstacle detection
• Navigation or path detection
• Face recognition

Obstacle detection using Herbie

The camera affixed to Herbie captures images and identifies obstacles as well as the path. The image captured is then mapped and labeled using a Planner. Based on the label, the subsequent motion commands are configured. For instance, if the label is “X,” then a motion command to move forward is configured. And if the label is “Y,” it should move to the right. 

Algorithm used for this purpose

• The first step in the algorithm is to detect obstacles from a captured image.
• All the images are then processed as grayscale images as it is a single-layer image and reduces the quantity of information obtained from the image, as compared to a colour image (RGB). 
• The image is then resized and divided into M x N matrices.
• The edge of the image represents the outline of the obstacle. The surface and the shadow of the image will be composed of darker or brighter pixels, which helps Herbie tell the difference.
• The edge of the obstacle is extracted by calculating the variance in a small square window which has the width w.
• The variance value V(p,q) is calculated for each pixel. A Threshold (Th) is fixed for the calculation 

Th = b +tσ; where σ is a standard deviation of V(p,q), t is decided on an experimental basis. 

• If the result of the calculation is greater than the threshold itself, Herbie identifies it as an obstacle. However, if the result of the calculation is lower than the threshold figure, Herbie continues to process images, moving in the default direction. 
• The surface of obstacles is determined by extracting bright and dark pixels.
• Areas that do not generate edge or surface data are considered to be minute areas of irregularity on the surface. Herbie rejects this as it does not detect any obstacle. 

Faculty of Smell and Gas Sensors

Gas sensors are capable of detecting various gases as well smoke. Such sensors consist of sensing material that has lower conductivity when the air is clear of high concentrations of different gases such as LPG, Propane, Hydrogen, Methane, Smoke, Carbon Monoxide, etc. 

Similarly, the sensing material becomes highly conductive when the concentration of such gases are higher in the atmosphere.

• In the case of Herbie, an MQ-2 gas sensor is used. 
• The detection range is 300-10,000ppm. 
• MQ-2 can function between the temperature range of -20℃ to 50℃.
• The sensing material of MQ-2 is SnO2. 
• The response time is less than 10 seconds. 
• The analog reading is the converted to sensor voltage using the following equation:

Sensor Voltage = Analog Reading*3.3V/4095

• The concentration of gas in PPM is then calculated using the equation:

PPM = 10.938*e˄ (1.7742*sensor voltage)

Faculty of touch and ultrasonic sensor

Ultrasonic sensors are used to measure the distance to an object. These sensors are usually environmentally independent. Its module transmits sound pulses and picks up the echo generated. It measures the time lapse between sending and receiving these pulses to calculate the distance to an object or obstacle. 

• Herbie uses HC-SR04 ultrasonic sensors. 
• It has a resolution of 3mm and the ranging distance between 2-50cm
• As soon as the module detects an object it transmits the information to a remote station using a wireless transceiver. 
• A pair of ZigBee is used to transmit and receive data. 
• When a high pulse of 10μs hits the trigger pin, the sensor transmits 8 pulses of 40KHz each.
• The range is then calculated using the difference in the time taken by the pulse/ signal to leave and return as shown in the equation below:

Distance(cm) = Time(sec)/58

Faculty of speech and hearing and speech recognition

The speech recognition process is composed of three major modules: 

1. Acoustic analysis
2. Training
3. Testing

• Multiple utterances of the same words that are part of the vocabulary are recorded. 
• These acoustic signals are then processed using the acoustic analysis module. 
• A knowledge base for the speech recognition system is developed using the training module.
• And the testing module is employed for system testing.
• A multilingual speech recognition system for associated words is developed using Hidden Markov Model Toolkit (HTK).
• The system uses Mel Frequency Cepstral Coefficients (MFCC) as feature vectors for each speech unit. 
• The speech samples are recorded at a sampling rate of 16,000Hz and are represented using 16 bits. 
• For Herbie we chose three languages for recognition namely, Hindi, English, and Kannada. 
• The system’s vocabulary consists of 75 different commands, 25 from each of these three languages. 
• Commands include words such as front, backward, left, right, etc.
• The confusion matrix for speech recognition in English was calculated at 98% accuracy.

Conclusion

Soon, we plan to carry out the training and testing of Herbie in challenging environments and probably extend the recognition capabilities to multiple languages. The estimated market size of assistive technology has expanded over the past couple of years. There is a growing demand for Socially Assistive Robots in the field of healthcare. And although it is not entirely possible to replace human beings, humanoid robots are here to stay and to transform the future of mankind.