687582 event 1769092406 1769092520 Learning-based techniques have become increasingly popular as a tool for improving autonomy of aerial robotics platforms. These methods typically lack safety guarantees using formal verification. We contribute to autonomy for aerial robotics by using learning-based techniques and a safe verification method for supervising unverified controllers. Specifically, we propose a navigation framework for urban air mobility and aerial robotics that uses real-time motion planning with continuous-time learning, adapting an actor-critic model predictive control approach for urban air mobility, and using reachability with control barrier functions for verifying these learning-based methods. Leveraging mixed monotonicity to quickly overapproximate reachable sets for aerial drone dynamics in real time. Additionally, we explore multi-agent reinforcement learning to teach drone swarms to cooperate in a multi-agent pursuit evasion problem. Finally, we contribute software tools for the aerial robotics community for simulating hardware code for the Crazyflie nano quadrotor.

]]> Join us for a presentation by Georgia Tech PhD Candidate and Sandia student intern Christian Llanes who will be speaking on his research in Reinforcement Learning-based Control and Safety Verification for Aerial Robotics 

]]> Ron Briggs

rb74@gatech.edu

]]> https://teams.microsoft.com/l/meetup-join/19%3ameeting_MjZkNjM0MmEtZGFhNi00ZTM5LThmMjUtYWEwZmU2MWEyN2Mz%40thread.v2/0?context=%7b%22Tid%22%3a%227ccb5a20-a303-498c-b0c1-29007381b574%22%2c%22Oid%22%3a%228915c310-6508-42a4-bf6b-93c78751e18e%22%7d 1182 1795