A robotic arm assembling a microchip amidst swirling patterns of noise.

Robot Whisperers: How a New Algorithm is Making Robotic Arms More Precise

Reese Marlowe in Tech & Innovation September 2025 • 4 min read.

"SVD-PoE promises greater reliability in robotic calibration, even when the environment gets noisy"

Robotic arms are no longer confined to the realm of science fiction. They're essential players in modern manufacturing, healthcare, and logistics. But, the real world isn't a pristine laboratory. Factors like temperature fluctuations, vibrations, and wear-and-tear can compromise the accuracy of these robotic helpers.

That's where kinematic calibration comes in. Kinematic calibration is the process of fine-tuning a robot's understanding of its own structure. Think of it as giving the robot a more accurate sense of touch. The goal? To minimize errors and ensure the robot performs its tasks with utmost precision.

Now, a team of researchers has unveiled a new algorithm, called SVD-PoE (Singular Value Decomposition-Product of Exponentials), designed to make robot kinematic calibration more robust and reliable. The focus is to solve the problem of singularity of the Denavit-Hartenberg (D-H) parameters.

What is the Big Deal About Robotic Arm Accuracy?

A robotic arm assembling a microchip amidst swirling patterns of noise.

Imagine a robotic arm meticulously assembling the delicate components of a smartphone. Now, picture that arm being just a hair off in its movements. The result could be a faulty product, wasted materials, and a dent in productivity. In industries where precision is paramount, even tiny errors can have big consequences.

Kinematic calibration ensures that the robot's movements align with its intended path. This is achieved by adjusting the robot's control parameters to compensate for any deviations from its ideal model. The challenge lies in achieving this calibration accurately, especially when the robot is operating in less-than-ideal conditions.

Enhanced Precision: Robots perform tasks with greater accuracy, reducing errors and improving product quality.
Increased Efficiency: By minimizing errors, robots can work more efficiently, leading to higher productivity and reduced cycle times.
Reduced Waste: Accurate robots waste less material, lowering costs and promoting sustainability.
Improved Reliability: Calibration ensures consistent performance over time, reducing the risk of unexpected downtime.
Wider Applicability: Robots can be deployed in a broader range of environments, including those with higher levels of noise and vibration.

Many industrial robots use the Denavit-Hartenberg (D-H) parameters. The D-H parameters can be singular when neighboring joint axes are nearly parallel although they are attractive because of their minimal set property. Unlike the D-H-based kinematic representations, the kinematic models based on the PoE formula vary smoothly with changes in the joint axes.

The Future of Robot Precision

The SVD-PoE algorithm represents a significant step forward in the quest for more accurate and reliable robotic systems. By mitigating the impact of environmental noise, this new approach paves the way for robots to perform complex tasks with greater confidence. Further work is planned, combining the algorithms to ANN and GA.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/s12206-018-1038-3, Alternate LINK

Title: A Svd-Least-Square Algorithm For Manipulator Kinematic Calibration Based On The Product Of Exponentials Formula

Subject: Mechanical Engineering

Journal: Journal of Mechanical Science and Technology

Publisher: Springer Science and Business Media LLC

Authors: Nguyen Van Toan, Phan Bui Khoi

Published: 2018-11-01

Everything You Need To Know

What is kinematic calibration and why is it important for robotic arms?

Kinematic calibration is the process of fine-tuning a robot's understanding of its own structure. It's crucial because it minimizes errors, ensuring the robot performs tasks with utmost precision. Environmental factors like temperature fluctuations and vibrations can compromise accuracy; kinematic calibration compensates for these deviations to maintain optimal performance. Without it, robotic arms would be prone to inaccuracies, leading to faulty products and wasted resources.

How does the SVD-PoE algorithm improve robotic arm precision?

The SVD-PoE (Singular Value Decomposition-Product of Exponentials) algorithm is designed to make robot kinematic calibration more robust and reliable, even in noisy environments. It addresses the problem of singularity often encountered with Denavit-Hartenberg (D-H) parameters. By using the SVD-PoE algorithm, robots can perform complex tasks with greater confidence and accuracy, mitigating the impact of environmental noise.

What are Denavit-Hartenberg (D-H) parameters, and what is their limitation that SVD-PoE overcomes?

Denavit-Hartenberg (D-H) parameters are a set of parameters commonly used to describe the kinematics of industrial robots. They are attractive because of their minimal set property. However, a significant limitation is that the D-H parameters can become singular when neighboring joint axes are nearly parallel, leading to inaccuracies. The SVD-PoE algorithm overcomes this limitation because, unlike D-H-based kinematic representations, kinematic models based on the PoE formula vary smoothly with changes in the joint axes.

What are the benefits of using more accurate robotic arms achieved through kinematic calibration and algorithms like SVD-PoE?

More accurate robotic arms, achieved through kinematic calibration and algorithms like SVD-PoE, offer several benefits: enhanced precision in tasks, increased efficiency with minimized errors, reduced material waste, improved reliability with consistent performance over time, and wider applicability in various environments, even those with high noise and vibration. These benefits collectively lead to higher product quality, lower costs, and improved sustainability.

Besides SVD-PoE, what other methods are being explored to improve robot precision and what are the implications of future research in this area?

Future research plans involve combining the SVD-PoE algorithm with Artificial Neural Networks (ANN) and Genetic Algorithms (GA). The implications of advancing robot precision are far-reaching. Increased precision could revolutionize industries requiring intricate assembly, such as electronics and pharmaceuticals, by significantly reducing errors and waste. Moreover, more reliable and adaptable robots could be deployed in hazardous environments or for delicate surgeries, expanding their utility and impact across various sectors. Further advancements could also lead to more autonomous and intelligent robots, capable of learning and adapting to changing conditions in real-time.