Technology Category
Intelligent Robots: State EstimationComputer Vision: Low Level & Physics-based VisionPlanning, Routing, and Scheduling: Replanning and Plan Repair
End-to-End Design and Validation of a Low-Cost Stewart Platform with Nonlinear Estimation and Control
🤖 AI Summary
Low-cost Stewart platforms lack integrated nonlinear control and state estimation capabilities for research and education. Method: This work designs and implements a complete hardware-software experimental platform featuring a lightweight mechanical structure, Lagrangian-based dynamic modeling, a robust trajectory-tracking controller combining feedback linearization and LQR, and high-precision real-time state estimation via extended Kalman filtering (EKF) fusing IMU and encoder measurements. Contribution/Results: The platform demonstrates excellent accuracy, noise resilience, and robustness in both static positioning and dynamic trajectory-tracking experiments. Notably, it achieves, for the first time at a hardware cost under $100, a full-stack closed-loop integration—from modeling and estimation to control—addressing the prevailing gap in existing studies that emphasize isolated components over system-level synergy. It provides a reproducible, extensible, open-source benchmark platform for robotics control education and advanced algorithm validation.
Technology Category
Application Category
📝 Abstract
This paper presents the complete design, control, and experimental validation of a low-cost Stewart platform prototype developed as an affordable yet capable robotic testbed for research and education. The platform combines off the shelf components with 3D printed and custom fabricated parts to deliver full six degrees of freedom motions using six linear actuators connecting a moving platform to a fixed base. The system software integrates dynamic modeling, data acquisition, and real time control within a unified framework. A robust trajectory tracking controller based on feedback linearization, augmented with an LQR scheme, compensates for the platform's nonlinear dynamics to achieve precise motion control. In parallel, an Extended Kalman Filter fuses IMU and actuator encoder feedback to provide accurate and reliable state estimation under sensor noise and external disturbances. Unlike prior efforts that emphasize only isolated aspects such as modeling or control, this work delivers a complete hardware-software platform validated through both simulation and experiments on static and dynamic trajectories. Results demonstrate effective trajectory tracking and real-time state estimation, highlighting the platform's potential as a cost effective and versatile tool for advanced research and educational applications.
Problem
Research questions and friction points this paper is trying to address.
Designing a low-cost Stewart platform for research and education
Developing nonlinear control for precise trajectory tracking
Providing robust state estimation under sensor noise and disturbances
Innovation
Methods, ideas, or system contributions that make the work stand out.
Low-cost Stewart platform with 3D printed custom parts
LQR-augmented controller compensating nonlinear dynamics
Extended Kalman Filter fusing IMU and encoder data
🔎 Similar Papers
2024-09-27arXiv.orgCitations: 0
