Autonomous robotic vehicles are increasingly required to operate safely in complex and uncertain environments while meeting stringent performance objectives. This talk addresses motion control from a path-oriented perspective, encompassing trajectory tracking, path following, and time-flexible task execution under nonlinear dynamics, input constraints, and disturbances.

We present a control framework in which safety, liveness, and performance are treated as coupled design objectives. In contrast to classical approaches based on strict temporal constraints, we show how path-parameterized formulations, where the rate of progression along the path is a control decision variable, substantially enlarge the set of feasible and high-performance controllers.

Theoretical developments grounded in Lyapunov methods, input-to-state stability arguments, and optimization-based safety filters are complemented by simulation and experimental studies. Applications include underactuated robotic vehicles and learning-based control architectures for autonomous systems. The talk aims to provide both conceptual insight and practical guidance for designing motion controllers that are safe by construction, live, and performance-oriented.