Lost objects on ISS and with no gravity, using Design Sprint to find them
This is a story about a GV style design sprint. If you’ve never heard of that, you can learn more here or read the book.
NASA has identified Autonomous Logistics Management as a pressing challenge of human spaceflight.
Autonomous logistics management (ALM) technologies provide for the integrated localization, transfer, and status of logistics and mission hardware, as well as for hardware and software to facilitate autonomous and automatic decision-making and planning of consumable usage and spare availability. Technology candidates for ALM include wireless tagging and monitoring systems for inventory tracking, and integrated software components that facilitate tracking, review, and management of the logistics chain.
One of the most important aspects of an ALM system is the ability to automatically track and update the location of hardware as
it is moved around the vehicle or habitat. A database localization tool showing fewer than 0.01 percent of the items missing, in a complex environment, is the capability objective, which is far more accurate than modern commercial warehouse practices that achieve approximately 3 percent of inventory categorized as missing, typically in less complex environments.
The aim of this Sprint is to design a solution for high accuracy, real-time localization of inventory within the International Space Station.
The Challenge:
Operational Intelligence software for complex logistics management, including three-dimensional (3D) localization and automated inventory updates. Operational intelligence to aggregate events and establish inferences to enable 3D asset localization in the absence of a complete data set.
Deliverables:
Finding items onboard the International Space Station (ISS) requires huge amounts of crew time. Providing smart tools that can automatically track where items are and provide that data on demand can significantly reduce crew time and enhance crew safety by enabling the finding of critical items in just minutes instead of hours. Effective logistics management will also enable reduced costs by eliminating unneeded resupply for misplaced items.
What objects usually get lost? — Why are they getting lost? — Is there any “common” path for those items to get lost? — What technologies can we use? — Of these technologies, which can interfere onboard instrumentations? — How can we train a machine to find them and collect them?
After sketching, sharing and voting we decided to focus on the “prevent” phase of the whole project: mainly objects/items get lost for a human error, a distraction of the astronaut, so why don’t we notify the astronaut when an object/item detach from the suit or is left unattended “not on purpose”? By preventing leaving accidentally items/objects we can decrease losses, while collecting data that will help us on our second stage of the Sprint: Autonomously locate lost assets and use minidrones to collect-and-return them!
As the team members are coming from different backgrounds and experiences, the best part is that we spanned into different fields from biomimicry for creating a jellyfish look-a-like drone, for conceptualizing the algorithms that could train the machine, to software engineering for delivering top-notch app, to mechanical engineering, to space engineering and business admin! — I think that this is the secret for a successful outcome in a Sprint: diversity of background and experience!
Of course we are still prototyping and we will need at least other 3 sprints (we had a rapid Design Sprint of only 3 hours) in order to validate the proposal and create a proof of concept. We are now working exhaustively on two main axes:
- Creating the app for notifying astronauts
- Training the machine to recognize objects/dimensionality/position and report
- Training the machine to predict trajectory+positioning of object(s) onboard the ISS
The team was/is composed by different background field member:
Panayotis Tzinis — Sprint Master
Billy Korombillias — Biomimicry UX/UI
Giorgos Profitiliotis — Space Environment Advisor
Vasilapostolos Ouranis — Prototype Advisor
Irene Tsakiridou — Mechanical Engineer
Vicky Fiska — Software Developer Ninja
Elena Veli — Base Control
Kleopatra Alamantariotou — Base Control
