Trajectory optimization under uncertainty underpins a wide range of applications in robotics.
However, existing methods are limited in terms of reasoning about sources of epistemic and …

In this survey, we design formal verification and control algorithms for autonomous systems
with practical safety guarantees using conformal prediction (CP), a statistical tool for …

Robust autonomy stacks require tight integration of perception, motion planning, and control
layers, but these layers often inadequately incorporate inherent perception and prediction …

Recent advances in path-planning algorithms have transformed robotics. The Rapidly
exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This …

This article extends the optimal covariance steering (CS) problem for discrete time linear
stochastic systems modeled using moment-based ambiguity sets. To hedge against …

Motion planning is a fundamental problem and focuses on finding control inputs that enable
a robot to reach a goal region while safely avoiding obstacles. However, in many situations …

In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion
planning under motion and sensing uncertainties. The original stochastic motion planning …

This paper proposes a formulation for the risk-aware path planning problem which utilizes
multi-objective optimization to dynamically plan trajectories that satisfy multiple complex …

Safe trajectory planning in highly dynamic environments remains a substantial challenge.
Traditional risk-based trajectory planning algorithms solve planning problems in …

For safe operation, a robot must be able to avoid collisions in uncertain environments.
Existing approaches for motion planning with uncertainties often make conservative …