Uncertainty Quantification in Robotic Applications

Submission Deadline: 01 May 2020

IEEE Access invites manuscript submissions in the area of Uncertainty Quantification in Robotic Applications.

Uncertainty in engineering systems comes from a variety of sources such as manufacturing imprecision, assembly errors, model variation and stochastic operating conditions. Hence the actual performance of an engineering system may deviate from the design target, resulting in a quality loss, customer dissatisfaction, or even catastrophic failures. To ensure robust and reliable system operations, it is imperative to quantify and reduce the uncertainty effects during the system design, manufacturing and field operation.

For the robotic systems, the dynamic and highly nonlinear performance is significantly affected by operating conditions, time-varying load, and other random stresses. This creates new challenges in measuring and characterizing the performance uncertainty with dynamic performance. Uncertainty quantification methods, such as reliability modeling, reliability analysis, reliability-based design optimization, model validation, sensitivity analysis, and robust design are deemed essential in improving the reliability of robotic systems.

This Special Section in IEEE Access invites academic scholars and industry practitioners to submit full-length articles that report the recent advances in theoretical, numerical, and experimental development in uncertainty quantification. The articles are expected to describe original findings or innovative concepts that address different aspects of uncertainty quantification challenges arising for robotic systems. New uncertainty quantification methods are anticipated to address the safety, reliability and quality issues of emerging robotic technologies, and thus move the robotic industry forward.

The topics of interest include, but are not limited to:

  • Reliability modeling, analysis and design optimization of robotic systems
  • Model verification and validation of robotic systems
  • Sensitivity analysis of robotic systems
  • Robust design of robotic systems
  • Performance reconstruction under uncertainty of robotic systems
  • Big data and machine learning in robotic systems
  • Internet of Things for robotic systems under uncertainty

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.


Associate Editor:  Zhonglai Wang, University of Electronic Science and Technology of China, China

Guest Editors:

  1. Tongdan Jin, Texas State University, USA
  2. Om Prakash Yadav, North Dakota State University, USA
  3. Ningcong Xiao, University of Electronic Science and Technology of China, China
  4. Yi (Leo) Chen, Glasgow Caledonian University, UK


Relevant IEEE Access Special Sections:

  1. Advances in Prognostics and System Health Management
  2. Additive Manufacturing Security
  3. Artificial Intelligence in Cyber Security

IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: wzhonglai@uestc.edu.cn.