Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

Sampling-based model-predictive control (MPC) is a promising tool for feedback control of
robots with complex, non-smooth dynamics, and cost functions. However, the …

This paper presents the computing system design in our commercial autonomous vehicles,
and provides a detailed performance, energy, and cost analyses. Drawing from our …

This paper explores the problem of collision-free motion generation for manipulators by
formulating it as a global motion optimization problem. We develop a parallel optimization …

While much attention has been paid to data centers' greenhouse gas emissions, less
attention has been paid to autonomous vehicles'(AVs) potential emissions. In this work, we …

We present a real-time motion-synthesis method for robot manipulators, called RelaxedIK,
that is able to not only accurately match end-effector pose goals as done by traditional IK …

Coordination is essential in the design of dynamic control strategies for multi-arm robotic
systems. Given the complexity of the task and dexterity of the system, coordination …

Modern sampling-based motion planning algorithms typically take between hundreds of
milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom …

This letter presents a closed-form approach to obstacle avoidance for multiple moving
convex and star-shaped concave obstacles. The method takes inspiration in harmonic …

Motion planning algorithms have seen considerable progress and expansion across various
domains of science and technology during the last few decades, where rapid advancements …