Starship Technologies is a company that has risen in popularity for having small self-driving delivery robots optimized to deliver goods within close proximity. The robots operate mainly autonomously, and their process of improvement and machine-based learning is very intriguing.
The way the robots work is very efficient. The robots have sensors built into them such as cameras and radars to tell them when something like a vehicle or bicycle is approaching, and ultrasonic sensors to tell them when something like a curb or pole is approaching. This information then gets sent back to a cloud-based infrastructure that allows the technology to determine which is the best and safest path to take in order to arrive at the destination. After looking at its surroundings, by going into a machine learning environment it is able to navigate around these obstacles effectively and continuously. This process has gotten better through experience and data, especially through the 3.6 million km that the robots have traversed. For example, originally human operators had to cross roads for them, but now it’s mainly autonomous as they have learned how to do it by themselves and the humans rarely need to step in. However, safety is always the #1 priority so if a robot needs to, it will stop and send a look over to the remote operator for them to see and evaluate the situation, although this only happens around 10% of the time. But if there are new crossings that are more complex, the remote operator will take over until the robot has learned how to cross autonomously.
In the beginning, the robots stopped at everything including common pedestrians and joggers, but now operations are much smoother since the robots have learned how to handle the situation and avoid humans and other obstacles nicely. Originally the robots had to drive down the middle of the sidewalk in order to optimize speed, but of course, there are obstacles in the middle of the sidewalk such as pedestrians. This slowed the robots down, but since the interactions were safer it subsequently changed how they operated. Again, for starship safety is always a priority over speed, and the deliveries will get better and quicker over time. They are also done at all times of the day, in the rain, snow, and at night. Originally, snow was something that the robots didn’t know how to deal with, so the system had to be trained to operate in those conditions. Since the robots are based around machine learning, good algorithmic decisions can be made, but they need data, and as more and more of this information gets piled in, the system and the speed at which it operates gets better each time.
This type of business model is also scalable for the future. CEO Alastair Westgarth mentioned that since groceries and restaurants were all segments that fit together well, especially in the town starship first started using their delivery robots in, Milton Keynes, England, this is likely to be their business model for the immediate future since they know this space well, which would allow them to scale and improve it most effectively. The robots could even have a place in the supply chain with autonomous ships, trucks, planes, and ships.
New career opportunities in programming and software design will be created as well. Though some people are concerned about autonomy causing a loss of jobs, the number of jobs that will be created will likely offset the number of jobs lost. With the scalability, improvement, and new jobs autonomous delivery robots can create, its future is going to be an exciting one to watch.
