Autonomous mobile robots (AMRs) have to power efficient and function for a long period of times. Thus, organizations are incorporating computer vision to make them efficient.

Fremont, CA:  It is a general perception that a device consumes more power if more hardware is added to system design or by boosting the performance of the hardware. In many cases, the cause-and-effect relationship rings true; however, in some cases, the converse is true as well. For instance, an autonomous robotic vacuum cleaner needs to function optimally between battery charges. If vacuum cleaner manufacturers add more batteries, then it would increase its weight and enlarge its form factor. Also, as a vacuum cleaner, one of the critical components of the design is to pick up dirt. In this case, powerful motors are heavier, bigger, and drain batteries faster. Adding the additional power to propel the device across the floor is an engineering challenge in itself.

Several vacuum cleaner manufactures have added vision processing to their latest products. As compared to the rudimentary pattern-based cleaning algorithms, computer vision helps in covering the floor efficiently, minimizing the number of times they pass over any particular stretch, as well as avoiding obstacles en route.  This technology enables the cleaner to focus the remaining battery on the primary task, which is cleaning while optimizing the system’s battery for maximum run time.     

In the case of drones, maximizing flight time is the key objective of the design. The drone has to be decent-sized to withstand wind and capable of toting relevant payloads at the same time. If the drone is big, it will potentially have shorter flight time for a given-sized battery array. Computer vision can help solve the design problem. Modern drones leverage their on-board cameras to capture in-flight video footage and efficient navigation. If a drone autonomously avoids obstacles, then it uses less energy.  In both the scenarios of the vacuum cleaner and the drone, the added subsystem must save more energy than it consumes. It must not increase the bill-of-materials as well. Practical computer vision processing is becoming attainable to both low power and low price points. Autonomous mobile robots (AMRs) are used to execute various on-board computer-vision applications. Organizations use trained deep learning models to analyze camera images. Using computer vision in AMRs is saving energy and helping users save money.

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