Autonomous driving technology pledges safety, convenience, and energy efficiency. Its
challenges include the unknown intentions of other road users: communication between …

In autonomous systems, the ability to make forecasts and cope with uncertain predictions is
synonymous with intelligence. Model predictive control (MPC) is an established control …

This article is concerned with the problem of planning optimal maneuver trajectories and
guiding the mobile robot toward target positions in uncertain environments for exploration …

From quadrotors delivering packages in urban areas to robot arms moving in confined
warehouses, motion planning around obstacles is a core challenge in modern robotics …

In this paper, a novel integrated real-time trajectory planning and tracking control framework
capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is …

The optimal performance of robotic systems is usually achieved near the limit of state and
input bounds. Model predictive control (MPC) is a prevalent strategy to handle these …

Fast feedback control and safety guarantees are essential in modern robotics. We present
an approach that achieves both by combining novel robust model predictive control (MPC) …

As a fundamental component of autonomous driving systems, motion planning has garnered
significant attention from both academia and industry. This paper focuses on efficient and …

We present an optimization-based approach for autonomous parking. Building on recent
advances in the area of optimization-based collision avoidance (OBCA), we show that the …

Obstacle avoidance between polytopes is a chal-lenging topic for optimal control and
optimization-based tra-jectory planning problems. Existing work either solves this problem …