This letter addresses the problem of generating robust and accurate trajectories taking into
account uncertainties in the robot dynamic model. Based on the notion of closed-loop …

This paper addresses the problem of optimally controlling nonlinear systems with norm-
bounded disturbances and parametric uncertainties while robustly satisfying con-straints …

Taking into account the effects of parameter uncertainties in the robot model is crucial to the
robustness of motion generation. One approach to address this issue is to compute …

The primary difficulty of on-orbit services is autonomous real-time motion planning,
especially considering collision avoidance among complex modulars. This study considers …

We propose a multi-scale approach for computing abstractions of dynamical systems, that
incorporates both local and global optimal control to construct a goal-specific abstraction …

Approaches for stochastic nonlinear model predictive control (SNMPC) typically make
restrictive assumptions about the system dynamics and rely on approximations to …

Motion planning is a fundamental capability in robotics applications. Real-world scenarios
can introduce uncertainty to the motion planning problem. In this work we study environment …

Robotic manipulation relies on analytical or learned models to simulate the system
dynamics. These models are often inaccurate and based on offline information, so that the …

Autonomous inspection tasks necessitate effective path-planning mechanisms to efficiently
gather observations from points of interest (POI). However, localization errors commonly …

As autonomous systems take on increasingly challenging tasks in safety-critical settings
such as autonomous driving and aerospace, their ability to explicitly account for uncertainty …