Start-up profile: Knightscope
What happens when you mix big data analytics, a policeman, homeland security, some funding from an insurance company and robotics?
You get Knightscope, a Santa Clara, California start-up developing a mobile robot that is a part of a multi-part augmentation strategy created to meet the needs of local authorities – cities and their police departments.
Strapped for cash and with limited resources to allocate, municipalities are faced with the same type of problems globally competitive businesses have: the need to use automation where it can either augment or replace limited manpower. There are about 18,000 police departments in the U.S. and about 133,000 K-12 public and private schools. These days, almost all those schools want some form of police monitoring, if not physical presence, further straining city and police resources.
The robot is a Segway-based platform with a payload of sensors, cameras and communication devices and a navigation and collision-avoidance system customized to fit various types of community needs. It has a nifty design, enough weight to thwart all but the huskiest of thieves, and sensors for day and nighttime imaging, air quality and temperature monitoring, and cameras for license plate recognition, heat mapping and much more. Linked with other devices such as external cameras, the robot’s own streaming cameras, communications with local authorities, and a set of smart software, the robot can patrol and add a wealth of data and support to those other resources.
Stacy Stephens, Knightscope’s VP for Marketing and Sales, is an ex-policeman; William Santana Li, the CEO, has design and auto industry and successful start-ups in his background.
What struck me as unusual were the backgrounds of the people involved and their focus on solving a clear and growing problem within our cities. Their approach, which includes many sciences, and cooperation between those sciences and local authorities, is to augment the manpower of those agencies where criteria can be met using sensors, quick analysis of the data, and alarms, rather than feet on the street.