Autonomy in the Real, Unstructured World

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Abstract:

Popular articles about advances in autonomous systems suggest that in the very near future our cities will be covered by delivery drones and disasters will be met with swarms of helpful robots to gather data and deliver aid.  Reaching this future will require autonomy algorithms that are able to understand the assumptions they are making and assess whether or not their hardware and information systems allow them to make correct decisions.  It also requires tight coupling between the design of algorithms for autonomous systems and the design of their hardware for navigation and communications.

In this talk, I will discuss an approach developed at the Naval Research Laboratory to design swarm control laws and mode-switching protocols to account for these "real-world" issues, beginning with a discussion of the trade space between complexity, performance and reliability.  I will then present potential field behaviors that are designed to work at specific instances within this trade space and a means of using linear temporal logic (LTL) to produce correct-by-construction mode selection tools that allow robots to detect their position in this trade space and select an appropriate behavior.  

This LTL framework allows us to separate safety concerns from higher level autonomy such as task selection and scheduling, as the LTL controllers guarantee that vehicles will not engage in unsafe behaviors.  I will discuss how this approach allows us to reduce the state space of higher level autonomy algorithms such as task selection and scheduling.

The talk will conclude with description of a simulated disaster relief mission performed by simulated and live teams of heterogeneous robots in outdoor environments.  The results of these experiments will show lessons learned about how to decouple complex problems into abstract decisions and concrete actions.

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