Solving the challenge of bringing drone operations to scale has been the holy grail for many in the industry. A lot goes into this process to get buy-in and funding from executives and regulatory institutions with a lot of that revolving around being able to demonstrate multiple levels of safety and security, value, and performance metrics. And all of this usually has to happen before a program even gets started.
Drones eliminate many of the pain points associated with snooper trucks and other legacy methods for bridge inspection. Drones are cheaper to buy (few thousand vs. 200k-500k for a truck), cheaper to operate (reduce cost per inspection by 75%), safer, non-intrusive to traffic, more environmentally friendly (no traffic jams)
In previous weeks, we’ve shared with you details on our new aircraft and autonomy solutions , and how they will upend a variety of drone use cases. This week, let’s dive deep into one of these use cases — Situational Awareness — to understand how Skydio autonomous drones improve outcomes and make everyone safer.
In last week’s blog, I shared with you some of the ways Skydio X2 was built to meet the needs of some of the drone industry’s most forward-thinking fleets. This week, I’d like to share some ideas about autonomy — what true autonomy is and why it matters, and how it will impact drone operations across the industry.
Skydio has raised $100 million in Series C funding led by Next47 with participation from Levitate Capital, NTT DOCOMO Ventures, and existing investors including Andreessen Horowitz, IVP, and Playground.
The Chula Vista Police Department’s new Close Proximity, Low Altitude (CPLA) waiver to enable Beyond Visual Line of Sight (BVLOS) missions in emergency situations. This waiver is the result of months of collaboration on patrol drone operations, ground risk reduction, and policy innovation between the CVPD, the San Diego UAS Integration Pilot Program, and Skydio.
We here at Skydio have been developing and deploying machine learning systems for years due to their ability to scale and improve with data. However, to date our learning systems have only been used for interpreting information about the world; in this post, we present our first machine learning system for actually acting in the world.
"My hands just shake a lot less when I fly.” That was the response from the young police officer and UAS pilot when I asked him to boil down why the Skydio 2 was different than the other drones he flies at work.
In this post we dive in to the Skydio Autonomy Engine — how the Skydio 2 sees the world and decides how to fly and film — by walking through an example. In this clip, watch how Skydio 2 keeps a cinematic framing of the biker while dodging obstacles at high speeds downhill. To do this, it has to estimate its own motion, build an obstacle map, estimate the biker’s path, and handle the aerodynamics of fast flight, all in real time onboard the drone.
