Controlling a fleet of autonomous mobile robots (AMR) is a complex problem of optimization.
Many approached have been conducted for solving this problem. They range from …

A vast amount of research has been conducted on path planning over recent decades,
driven by the complexity of achieving optimal solutions. This paper reviews multi-robot path …

This letter presents an approach for auto-tuning feedback controllers and online trajectory
planners to achieve robust locomotion of a legged robot. The auto-tuning approach uses an …

State estimation for legged robots is challenging due to their highly dynamic motion and
limitations imposed by sensor accuracy. By integrating Kalman filtering, optimization, and …

In this research, we aim to answer the question: How to combine Closed-Loop State and
Input Sensitivity-based with Observability-aware trajectory planning? These possibly op …

This paper presents a chance-constrained formulation for robust trajectory optimization
during manipulation. In particular, we present a chance-constrained optimization for …

Achieving highly accurate dynamic or simulator models that are close to the real robot can
facilitate model-based controls (eg, model predictive control or linear-quadradic regulators) …

Numerous path planning studies have been conducted in past decades due to the
challenges of obtaining optimal solutions. This paper provides a comprehensive review of …

The goal of this work is to formulate algorithms that can address three key ingredients I
believe are necessary towards making robots autonomous and smart:(1) The robot needs to …

Although robotic locomotion and manipulation have shown some remarkable progress in
the real world, the current locomotion and manipulation algorithms are inefficient in …