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Robots have become a significant part of our daily life nowadays. This article would mainly study how robot navigation works and why it is so popular nowadays. Nowadays, robots have become a part of various industries like space, transportation, defense, and many more. Mobile robots are also known to perform various functions like managing a disaster and different emergency and rescue events. Robots are also used on platforms for extensive searches, for example, a system to search for patents and trademarks.
Robots need a safe and smooth environment to travel freely from the start position to the main goal. This safe travel is ensured by robot navigation. There are various navigation techniques to ensure this on a dynamic and static level. Robot navigation can be defined as the robot's own ability to decide upon a path and orientation within its "frame of reference" to reach its goal. Three factors form the main base for mobile navigation and obstacle avoidance: The first one is self-localization; the second method is path planning. The last approach is map building and interpretation.
There are various navigation methods like Voronoi graph, grids, visibility graphs, "Artificial Potential Field method," etc. There are three broad categories of algorithms of mobile navigation: the first and foremost is the deterministic algorithm, then comes the non-deterministic algorithm and the last one in the list is the evolutionary algorithm. This is the overall general classification of algorithms. Under these three broad categories come other forms of algorithms as well. Navigation is a very significant task, and it can be on a global or a local basis.
SLAM OR Simultaneous localization and mapping are a problem in computation geometry that helps update maps. It was first researched upon in s detailed manner in the year 1986. This also keeps track of an agent at the same time. SLAM algorithms are popularly found in self-driving cars, rovers to keep a check on planetary movements, and various other robots. There are also different types of SLAM algorithms. "Collaborative SLAM" helps to form 3d images by compounding images from more than one robot. There is also something known as the "audio-visual SLAM" that was originally designed for human-robot interaction.
ORB SLAM mono camera is one of the first real-time SLAM systems that is visual. This helps in visually studying and forming the maps for robot navigation and obstacle avoidance. Various search websites that use robots for their search algorithm need robot navigation as well. Optical vision is also used to view maps. Various computer algorithms and optical sensors are used to do this.
Thus, it can be concluded that since robots are an important part of our lives, robot navigation and obstacle avoidance are very essential as well. that is because the robots need to navigate freely in their environment.
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