To navigate its environment, a robot must be either remote controlled, preprogrammed in a known unchanging environment, or it must be able to do this autonomously. To be able to navigate autonomously a robot must be able to not only continuously model and update its environment, which can be static and dynamic, but also be able to determine the best path to take based on this model and its own instantaneous location. Based on this model, the navigation algorithm must be able to predict the future states of all the obstacles and objects in the environment. The necessary inputs to overcome these challenges are obtained through sensors, mainly camera but can involve other sensors such as infrared, ultrasonic, LiDAR and more.
Although the background was laid before, the technology saw fast improvement after 2000s. DARPA Grand challenge for autonomous vehicles for example, took it one step ahead each year, until finally it was discontinued due to completion of a path in its entirety was not a challenge anymore. Today we see autonomous navigation technology in cars, trucks and other robotic systems including humanoid robots (androids), various domestic robots such as security, delivery, warehouse robots, to varying degrees. Once full autonomy in traffic is achieved as a widespread application, the technology is expected to transform our life in various ways.
Post Date: December 7th, 2022
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