insight - Robotics Control - # Hybrid force-motion control with surface normal estimation

A Novel Hybrid Force-Motion Control Framework with Real-Time Surface Normal Estimation for Robust Manufacturing Applications

Q: How can the proposed surface normal estimation method be extended to handle more complex surface geometries, such as those with discontinuities or sharp edges?

The proposed surface normal estimation method can be extended to handle more complex surface geometries by incorporating advanced algorithms for surface reconstruction and feature detection. For surfaces with discontinuities or sharp edges, additional sensors such as 3D cameras or LiDAR can be integrated to provide more detailed information about the surface geometry. Machine learning techniques can be employed to analyze the sensor data and identify regions of interest where the surface normal estimation may be challenging. By training the algorithm on a diverse dataset that includes various surface geometries, the system can learn to adapt and accurately estimate surface normals even in complex scenarios. Furthermore, the algorithm can be enhanced to dynamically adjust its estimation strategy based on the detected surface features, allowing for more robust performance on surfaces with discontinuities or sharp edges.

Q: What are the potential limitations of the friction compensation approach, and how could it be further improved to handle more diverse surface properties?

One potential limitation of the friction compensation approach is the reliance on accurate friction models, which may not always capture the complex and dynamic nature of friction in real-world scenarios. Variations in surface properties, temperature, and environmental conditions can affect the effectiveness of the friction compensation strategy. To address this limitation, the friction compensation approach could be further improved by implementing adaptive control techniques that continuously update the friction model based on real-time sensor feedback. By incorporating adaptive algorithms that adjust the friction compensation parameters on-the-fly, the system can adapt to changing surface properties and environmental conditions, enhancing its performance on diverse surfaces. Additionally, integrating robust estimation methods for surface friction, such as Kalman filters or neural networks, can improve the accuracy of friction compensation and enable the system to handle a wider range of surface properties.

Q: What other manufacturing applications, beyond the ones discussed, could benefit from the integration of this hybrid force-motion control framework with real-time surface normal estimation?

The integration of the hybrid force-motion control framework with real-time surface normal estimation can benefit various manufacturing applications beyond those discussed in the context. Some potential applications include: Grinding and Polishing: Precision grinding and polishing processes require accurate force control and surface tracking. By integrating the proposed framework, manufacturers can achieve superior surface finishing on complex workpieces with irregular geometries. Assembly and Inspection: In assembly lines, robots need to exert controlled forces while assembling components. Real-time surface normal estimation can enhance the accuracy of assembly tasks and enable robots to inspect surfaces for defects or anomalies during the manufacturing process. Material Handling: Robots involved in material handling tasks, such as picking and placing objects, can benefit from precise force control and surface tracking. By integrating the hybrid force-motion control framework, manufacturers can improve the efficiency and reliability of material handling operations in diverse industrial settings. Additive Manufacturing: 3D printing processes require precise control of the printing nozzle's contact with the printing surface. Real-time surface normal estimation can optimize the printing path and ensure consistent layer deposition, leading to high-quality additive manufacturing outcomes. By applying the hybrid force-motion control framework with real-time surface normal estimation to these manufacturing applications, companies can enhance automation processes, improve product quality, and increase overall operational efficiency.

Conceitos Básicos

This paper proposes a novel hybrid force-motion control framework that utilizes real-time surface normal updates. The surface normal is estimated by leveraging force sensor measurements and velocity commands to compensate for surface friction bias, enabling robust execution of precision force-controlled tasks in manufacturing.

Resumo

The paper presents a novel hybrid force-motion control framework that incorporates a method for real-time estimation of the surface normal. The key highlights are:

A hybrid force-motion control framework is introduced, featuring a newly developed methodology to estimate the surface normal in real-time. This method computes the surface normal by constructing a surface friction model using force sensor feedback and estimating the surface Coulomb coefficient.
The proposed approach is validated through simulations and experiments on a 7-DoF robot manipulator equipped with a force/torque sensor at the end-effector. The robot is tasked with executing various trajectories, including linear, sinusoidal, and curved paths, as well as paths resembling a semi-circular arc (dome), on different workpieces.
The surface normal estimation method enables improved position tracking accuracy, especially on curved surfaces where the normal direction changes along the path, compared to methods without surface normal updates. The average accuracy increase achieved by the proposed method is approximately 5%.
The hybrid force-motion control command is formulated to incorporate the estimated surface normal, allowing the robot to accurately trace the desired trajectory while exerting the required force perpendicular to the surface.
The proposed framework and algorithms are implemented within the ROS2 environment, demonstrating the practical applicability of the approach for real-world manufacturing applications.

Personalizar Resumo

Reescrever com IA

Gerar Citações

Traduzir Texto Original

Para Outro Idioma

Gerar Mapa Mental

do conteúdo original

Visitar Fonte

arxiv.org

Estatísticas

The robot utilizes a 7-DoF manipulator equipped with an F/T sensor and a probe at the end-effector.
The desired force is set to [0, 0, -2] N.
The offset between the probe and the desired contact point is set to [0, 0, 0.05] m.

Citações

"Precise hybrid motion-force control is greatly sought after in robotic manufacturing processes. However, such precision is constrained and affected by environmental factors like friction."
"Failing to accurately estimate the surface normal can lead to undesired outcomes, potentially causing the task to fail."

Principais Insights Extraídos De

Hybrid Force Motion Control with Estimated Surface Normal for Manufacturing Applications

by Ehsan Nasiri... às arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04419.pdf

Hybrid Force Motion Control with Estimated Surface Normal for Manufacturing Applications

Perguntas Mais Profundas

How can the proposed surface normal estimation method be extended to handle more complex surface geometries, such as those with discontinuities or sharp edges?

The proposed surface normal estimation method can be extended to handle more complex surface geometries by incorporating advanced algorithms for surface reconstruction and feature detection. For surfaces with discontinuities or sharp edges, additional sensors such as 3D cameras or LiDAR can be integrated to provide more detailed information about the surface geometry. Machine learning techniques can be employed to analyze the sensor data and identify regions of interest where the surface normal estimation may be challenging. By training the algorithm on a diverse dataset that includes various surface geometries, the system can learn to adapt and accurately estimate surface normals even in complex scenarios. Furthermore, the algorithm can be enhanced to dynamically adjust its estimation strategy based on the detected surface features, allowing for more robust performance on surfaces with discontinuities or sharp edges.

What are the potential limitations of the friction compensation approach, and how could it be further improved to handle more diverse surface properties?

One potential limitation of the friction compensation approach is the reliance on accurate friction models, which may not always capture the complex and dynamic nature of friction in real-world scenarios. Variations in surface properties, temperature, and environmental conditions can affect the effectiveness of the friction compensation strategy. To address this limitation, the friction compensation approach could be further improved by implementing adaptive control techniques that continuously update the friction model based on real-time sensor feedback. By incorporating adaptive algorithms that adjust the friction compensation parameters on-the-fly, the system can adapt to changing surface properties and environmental conditions, enhancing its performance on diverse surfaces. Additionally, integrating robust estimation methods for surface friction, such as Kalman filters or neural networks, can improve the accuracy of friction compensation and enable the system to handle a wider range of surface properties.

What other manufacturing applications, beyond the ones discussed, could benefit from the integration of this hybrid force-motion control framework with real-time surface normal estimation?

The integration of the hybrid force-motion control framework with real-time surface normal estimation can benefit various manufacturing applications beyond those discussed in the context. Some potential applications include:

Grinding and Polishing: Precision grinding and polishing processes require accurate force control and surface tracking. By integrating the proposed framework, manufacturers can achieve superior surface finishing on complex workpieces with irregular geometries.

Assembly and Inspection: In assembly lines, robots need to exert controlled forces while assembling components. Real-time surface normal estimation can enhance the accuracy of assembly tasks and enable robots to inspect surfaces for defects or anomalies during the manufacturing process.

Material Handling: Robots involved in material handling tasks, such as picking and placing objects, can benefit from precise force control and surface tracking. By integrating the hybrid force-motion control framework, manufacturers can improve the efficiency and reliability of material handling operations in diverse industrial settings.

Additive Manufacturing: 3D printing processes require precise control of the printing nozzle's contact with the printing surface. Real-time surface normal estimation can optimize the printing path and ensure consistent layer deposition, leading to high-quality additive manufacturing outcomes.

By applying the hybrid force-motion control framework with real-time surface normal estimation to these manufacturing applications, companies can enhance automation processes, improve product quality, and increase overall operational efficiency.