Dealing with variability in open-ended environments requires robots to adapt themselves according to the perceived situation in order to achieve the required quality of service (defined in terms of safety,performance or energy consumption, among other criteria). In this sense,context awareness and runtime self-adaptation allows moving autonomous robot navigation one step forward. The ambition of theMIRoNProjectwas to provide a complete framework enabling designers to endow robots with the ability of self-adapting their course of action at runtime, according to the external and internal context information available. Our proposal relies on the systematic use of models for dynamically reconfiguring the robot behavior, defined in terms of Behavior Trees, according to the runtime prediction and estimation of quality of service metrics based on system-level non-functional properties.
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