MDP-Based Adaptive Motion Planning for Autonomous Robot Operations Under DegradedConditions

Phillip Seaton
Autonomous mobile robots (AMR) like ground and aerial vehicles may encounter internal failures and external disturbances when deployed in real-world scenarios compromising the success of a mission. This thesis proposes an online learning method to adapt the motion planner to recover and continue an operation after a change in a robot's dynamics. Our proposed framework builds on the Markov Decision Process (MDP) and leverages the residual - defined in this work as the difference between...
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