Page 83 - Robotics and AI class 10
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For example, in autonomous, self driving cars or drones, LiDAR sensors are used for mapping, localisation, and
obstacle detection. The LiDAR sensor scans the environment, creating a 3D map that includes information about
the position and shape of objects. This map can be used for localisation, allowing the robot to determine its
position within the mapped environment. Additionally, the LiDAR sensor can detect obstacles by identifying
objects or changes in the environment, enabling collision avoidance and safe navigation.
LiDAR sensors are particularly effective in environments where accurate distance measurements and detailed 3D
mapping are required, such as outdoor navigation, warehouse automation, or autonomous vehicle applications.
They provide reliable spatial information that helps robots understand their surroundings and make informed
navigation decisions.
Speech Recognition Sensor
This although not strictly categorised as a sensor, is a technology used
in robotics to convert spoken language into written text or commands. It
enables robots to understand and respond to human speech, facilitating
natural and intuitive human-robot interaction. Speech recognition
systems analyse the audio input, identify spoken words or phrases, and
convert them into text or actionable commands. Let’s explore an example
of speech recognition technology used in robotics: voice-controlled
personal assistants.
Voice-controlled personal assistants, such as Amazon’s Alexa, Google Assistant, or Apple’s Siri, utilise speech
recognition technology to understand and respond to user commands or queries. These assistants are embedded
in devices such as smart speakers, smartphones, or home automation systems.
For instance, imagine a robot equipped with a voice-controlled personal assistant. The user can speak commands
or questions to the robot, and the speech recognition system within the personal assistant interprets the speech
and converts it into text. This text is then processed, and the robot’s software can determine the appropriate
action or response based on the user’s input.
The speech recognition system analyses the audio input, taking into account factors such as pronunciation,
accent, and context, to accurately transcribe the spoken words. Natural Language Processing (NLP) techniques
are often employed to improve the accuracy and understanding of the input.
Once the speech is transcribed into text, the robot can utilise this information to execute specific commands,
perform tasks, or provide responses. For example, a user might say, “Robot, turn on the lights,” and the speech
recognition system converts the spoken words into text, allowing the robot to understand the command and
activate the corresponding light control mechanism.
Speech recognition technology in robotics enhances human-robot interaction, making it more intuitive and user-
friendly. It enables robots to understand spoken instructions, answer questions, or even engage in conversations
with users. This technology finds applications in various domains, including home automation, customer service
robots, voice-controlled robots, or assistive robots for individuals with disabilities.
It’s worth noting that speech recognition technology is continuously advancing, and different systems may
have varying levels of accuracy and language support. However, the goal remains the same: to enable robots to
understand and respond to human speech, improving the overall user experience and interaction.
Components of Robots as a System 81

