The integration of neural networks will enable organizations to develop robust robotic units at relatively inexpensive costs.
FREMONT, CA – Organizations across the world are seeking to automate their delivery process by leveraging robot fleets. The robotic units have to be safe, polite, and quick to facilitate seamless integration. However, it cannot be achieved without robust computational resources and expensive sensor technology solutions. The smooth function of robots will require radars, cameras, and ultrasonics.
The capabilities of current robotic systems are limited. Even though they can sense and identify objects, the failure to categorize them accordingly will hinder their driving decisions. In such cases, the neural networks of machine learning (ML) technology can be leveraged to convert the unstructured low-level data into high-level information.
Since delivery robots are designed to drive on sidewalks and cross streets, their navigational challenges are more complex than self-driving vehicles. The traffic on roads is comparatively more structured and predictable than the movement of humans, who are likely to stop abruptly, meander, and be accompanied by pets.
The capabilities of robots in understanding their environment depends on their object detection module, which processes images and decides the following action. The images utilized by the module are three-dimensional arrays comprising a myriad of numbers representing pixel intensities. The value changes according to sunlight exposure, object color, scale, or position.
The advanced robot software comprises a set of trainable units, including neural networks, where the code is written. A collection of weights represents the program. The numbers are randomly initialized before the weights are iteratively changed.
Big data is not sufficient to train the robotic software. The collected has to be rich and varied. However, the annotation of data requires a lot of time and resources. Organizations can leverage architecture engineering to aid in this process, utilizing its capabilities to encode prior knowledge into neural networks and optimize the process.
Incorporating deep learning in delivery robots is not practical since it requires extensive computing power and robust hardware. Although neural networks offer advanced capabilities, they contain bugs which might affect the functionality of the robots.
The adoption of neural networks in delivery robots will enable the units to seamlessly identify various objects and predict the direction of movement of the objects, including vehicles and humans. It has enabled enterprises to design robotic delivery units with relatively inexpensive hardware and bring greater convenience to humankind.