Robots_part 2

Autonomous robots

Autonomous robots, on the other hand, operate in a much wider variety of environments. Many work indoors, where GPS signals are weak or unavailable. Instead, they must rely entirely on their own sensors—using technologies like SLAM (Simultaneous Localization and Mapping), LiDAR, cameras, and IMUs to build and constantly update their understanding of the environment.

Navigating without any external positioning support makes developing autonomous robots even more challenging, especially in dynamic or cluttered spaces like warehouses, offices, or hospitals.

Their speeds are usually much lower, and their environments can be more unpredictable—but often less deadly if something goes wrong. Flexibility and real-time problem solving are critical.

While both systems use similar technologies—like perception algorithms and path planning—the way they prioritize their “intelligence” is different.

Self-driving cars focus heavily on prediction, safety, and strict compliance with external rules. Autonomous robots prioritize adaptability, obstacle negotiation, and efficiency in confined or changing spaces.

Both are amazing examples of Physical AI at work, but they are tuned to solve very different kinds of problems. In the future, the gap between what a car can do and what a warehouse or delivery robot can do might start to blur—but for now, the skills they need are shaped by the environments they were built for.