In order for automated mobile vehicles to navigate in the real world with minimal collision
risks, it is necessary for their planning algorithms to consider uncertainties from …

Rendezvous and docking operations have been an integral component of manned
spaceflight from the beginning of the space age. However, there is now a growing interest in …

This paper presents a tool for addressing a key component in many algorithms for planning
robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are …

Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For
robust execution, we must take into account uncertainty, which arises due to uncertain …

We present an approach for safe motion planning under robot state and environment
(obstacle and landmark location) uncertainties. To this end, we first develop an approach …

This paper presents a novel sampling-based planner, CC-RRT*, which generates robust,
asymptotically optimal trajectories in real-time for linear Gaussian systems subject to …

This article presents a novel approach, named Monte Carlo Motion Planning (MCMP), to the
problem of motion planning under uncertainty, ie, to the problem of computing a low-cost …

This paper presents a real-time path planning algorithm that guarantees probabilistic
feasibility for autonomous robots with uncertain dynamics operating amidst one or more …

We tackle the problem of trajectory planning in an environment comprised of a set of
obstacles with uncertain time-varying locations. The uncertainties are modeled using widely …

One of the most difficult challenges in robot motion planning is to account for the behavior of
other moving agents, such as humans. Commonly, practitioners employ predictive models to …