As the first workshop dedicated to exploring synergies and identifying challenges in robotic research enhanced by ranging sensors, this event includes two sessions addressing key topics that span from fundamental concepts to emerging novel theories and techniques, organized as follows:
This session focuses on robotic research that integrates ranging technology to enhance localization and navigation capabilities. With increasing interest in tightly coupling range observations, the invited speakers will begin by presenting fundamental principles and recent advances in integrating GNSS (Dr. Taro Suzuki) and UWB (Dr. Thein-Minh Nguyen). The session will then feature a keynote on advanced GNSS techniques, highlighting their role in enabling seamless robot autonomy.
As range observations hold strong potential in formulating state estimation problems—thanks to the simple and efficient sensor models under nominal conditions and the flexibility to incorporate highly complex noise models in challenging scenarios—they have become a valuable tool for validating novel theories and advancing estimator development. Accordingly, this session also features expert talks on equivariant estimators (Dr. Stephan Weiss) and continuous-time graph optimization (Dr. Thein-Minh Nguyen).
High precision alone is not enough—robots must also be safe and trustworthy. A key challenge in autonomous systems is making navigation resilient to uncertainties. In robotics, robust multi-sensor estimation enables error detection and recovery, while in GNSS, integrity monitoring and fault exclusion techniques ensure reliability in challenging environments (Prof. Matthew Spenko). This raises an interesting question: How could emerging advanced techniques on robustness and integrity from other research domains be adapted to robotic applications and, in turn, inspire novel research directions?
In addition, real-world navigation often faces non-Gaussian noise, where conventional probabilistic models fall short. Advanced noise modeling techniques are therefore essential to achieve robust and stable estimation, strengthening the resilience of autonomous systems (Dr. Tim Pfeifer). Given the shared need for robustness in both estimation and control, a promising direction is to jointly address these problems through an inference- based framework (Prof. Weisong Wen).
In conclusion, the convergence of robotics and ranging technologies is not just about improving positioning accuracy—it is about building a robust, scalable, and intelligent foundation for the next generation of autonomous systems. By integrating relative and absolute positioning, enhancing robustness against noise, ensuring safety and integrity, and exploring bio-inspired navigation, we can unlock new opportunities for real-world robotic applications.
To further advance this vision, we encourage contributions from students and researchers actively working in these domains. Paper submissions provide a platform to share novel ideas, preliminary findings, and emerging challenges, fostering dynamic discussions and potential collaborations. Selected submissions will have the opportunity to present their work and receive valuable feedback from experts across academia and industry, strengthening the research community and paving the way for future breakthroughs.