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 …

We consider the problem of trajectory planning in an environment comprised of a set of
obstacles with uncertain locations. While previous approaches model the uncertainties with …

We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty.
Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain …

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 consider the problem of risk-sensitive motion planning in the presence of randomly
moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose …

Autonomous vehicles need to be able to plan trajectories to a specified goal that avoid
obstacles, and are robust to the inherent uncertainty in the problem. This uncertainty arises …

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

Agent behavior is arguably the greatest source of uncertainty in trajectory planning for
autonomous vehicles. This problem has motivated significant amounts of work in the …

Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles.
Previous approaches that used a constrained optimization approach to solve for finite …

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 …