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6 Things to Know About Computational Intelligence in Robotics Before Learning

Studying different methods and strategies for building intelligent devices and systems is known as computational intelligence. It covers techniques like swarm intelligence, fuzzy logic, evolutionary computation, and artificial neural networks.

These methods can be used in various applications, such as robots, autonomous systems, natural language processing, image and audio recognition, and many more. Creating systems that can adapt, learn, and make judgments in complex and dynamic contexts is the general aim of computational intelligence.

In this guide, let’s check out the six things you should know about computational intelligence in robotics before learning it!

1. What is Computational Intelligence in Robotics?

The term “computational intelligence” in robotics applies to using different approaches to create intelligent robots, including evolutionary computation, artificial neural networks, swarm intelligence, and fuzzy logic.

Robots can be made to carry out a variety of activities using these approaches, including navigation, planning, object recognition, control, and human-robot interaction.

2. Prerequisites to Learning Computational Intelligence in Robotics

Before learning computational intelligence in robotics, you’ll need to know the following.

  • Knowledge of fundamental programming languages, data structures, and algorithms in computer science and mathematical ideas like optimization, linear algebra, probability, and statistics.
  • Understanding of supervised and unsupervised machine learning techniques.
  • Knowledge of evolutionary algorithms, neural networks, and expert systems as they relate to artificial intelligence.
  • Knowledge of one or more of the industry-standard programming languages, including Python, Java, C++, and R.
  • Understanding of the industry’s standard tools and libraries, including PyTorch, TensorFlow, and sci-kit-learn.
  • Knowledge of the distinctions among machine learning, Deep learning, and artificial learning.
  • Experience with cloud computing and big data advancements.
  • Understanding of the uses of computational intelligence in several fields, such as autonomous systems, computer vision, and natural language processing.

You can study most of the fields mentioned above, like Python and Java, through some of the best free Udemy courses and also get the required certifications.

3. Robotic Application of Computational Intelligence

Robotic applications of computational intelligence include the following.

  • Artificial Neural Networks

Neural networks are among the main components of computational intelligence. Such systems, which are used to simulate intricate, nonlinear interactions across inputs and outputs, are modeled after the structure and operation of the human mind.

Artificial neural networks may be used to describe a robot’s dynamics and regulate its motion using neural networks.

Artificial neurons that can train from input and find patterns make up the various layers of nodes that make up neural networks. They have been employed in multiple areas, such as control systems, speech and image recognition, and natural language processing.

  • Robotic Decision-Making Using Fuzzy Logic

Robots can utilize fuzzy logic to arrive at decisions in unpredictable and dynamic settings. Fuzzy logic is a crucial component of computational intelligence. Using this method of reasoning, unpredictability and inaccuracy can be represented and controlled.

A set of guidelines and membership functions are used in fuzzy logic algorithms to indicate the extent to which a given input or output falls into a specific class or category. They are extensively utilized in decision-making, pattern recognition, and control systems.

  • Multi-robot Setups Using Swarm Intelligence

Swarm intelligence can direct teams of bots to carry out specific tasks well-coordinatedly. The group behavior of social creatures like ants, wasps, and birds is the inspiration for the relatively new field of computational intelligence known as swarm intelligence.

The activity of many actors, or “swarm members”, is coordinated by these systems using straightforward, decentralized rules to accomplish a shared objective. Swarm intelligence was thus applied to several fields, including robotics, control systems, and optimization.

4. Benefits of Learning Computational Intelligence in Robotics

Learning computational intelligence in robots has many advantages, as stated.

  • Better Robot Performance

By incorporating computational intelligence techniques into robotics, scientists can create robots capable of carrying out more complex jobs and surviving in hostile situations.

  • Enhanced Autonomy

Computational intelligence techniques can be used to create robots that can decide for themselves and change their behavior in response to changing circumstances without human assistance.

  • Improved Human-Robot Collaboration

Researchers can create robots that can communicate with living beings more intuitively and organically by applying computational intelligence techniques like natural-language processing.

  • Enhanced Efficacy and Cost-effectiveness

Computational intelligence techniques can be utilized to enhance robot efficiency and decrease installation prices.

  • Professional Prospects

Learning computational intelligence in robotics could lead to a broad range of job opportunities in transportation, manufacturing, healthcare, and services. In addition, AI and Robotics are constantly developing fields that demand skilled researchers and engineers.

5. Scope of Computational Intelligence In Robotics

The scope of applications for CI is broad and includes numerous fields. It is utilized in various industries, including data mining, robotics, natural language processing, image and signal processing, and control systems.

Many businesses use CI approaches, including banking, medicine, transport, and industry.

6. How to Study Computational Intelligence In Robotics

You can begin studying Computational Intelligence with a solid background in mathematics, especially probability, calculus, and linear algebra.

Additionally, having a solid grasp of computer science fundamentals like algorithms and database structures is crucial. In addition, there are other options for learning about CI, such as online programs, publications, and research papers.

A degree in electrical engineering, computer science, or a related subject can also be used to study CI. In addition, many institutions offer CI-specific courses, programs, and specialized research labs or centers.

The International Fuzzy Systems Association, as well as the International Neural Network Society, among others, are professional organizations and societies devoted to the area that provide information and networking opportunities for anyone interested in researching CI.

Some universities and other organizations offer computational intelligence in robotics programs and courses. Several examples involve:

  • Many robotics and AI programs are available, including a graduate program in robotics, at the Massachusetts Institute of Technology (MIT) in the United States.
  • A robotics and artificial intelligence program are available at Stanford University in the United States.
  • China’s Zhejiang University has a graduate robotics program.
  • Imperial College London offers a graduate program in robotics in the UK.
  • A PhD program in robotics and embedded systems is offered by the Technical University of Munich (TUM) in Germany.
  • An advanced degree program in intelligent robotics is available from the University of Tokyo in Japan.

Final Words

In conclusion, computational intelligence is a subset of artificial intelligence that simulates human intelligence using methods and algorithms to tackle challenging issues. It includes various methodologies, such as swarm intelligence, genetic algorithms, fuzzy logic, and neural networks.

To study CI, you need to have a solid background in computer science and mathematics and pursue a degree in a relevant discipline. Then, get practical experience by implementing CI algorithms and experimenting with various applications.

Main image by Jelleke Vanooteghem on Unsplash

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