Kinematics Modeling

Kinematic modeling focuses on mathematically describing the motion of systems, aiming to predict their configuration based on actuator inputs or vice-versa. Current research emphasizes developing accurate and efficient models for diverse systems, including continuum robots (using Cosserat rod models and optimization-based approaches), parallel robots (employing closed-form solutions and graph neural networks), and even biological systems like peroxy radicals (leveraging deep reinforcement learning) and human motion (using stochastic differential equations and Kalman filters). These advancements are crucial for improving the design, control, and prediction capabilities of robots in various applications, from minimally invasive surgery to industrial automation and environmental modeling.