This survey explores the transformative impact of robotics on intralogistics, driven by supply chain complexities and evolving consumer demands. It reviews various robotic systems alongside foundational algorithms for their operation, such as simultaneous localization and mapping, diverse path planning strategies, and advanced perception/manipulation techniques. An important focus is put on multirobot system coordination, task allocation, and fleet management in logistics. The article then examines human–robot collaboration and relevant safety standards. Finally, it identifies key challenges for future development, including interoperability, advanced AI integration, scalability, robustness in dynamic environments, and economic barriers to adoption.
The increasing use of neural networks (NNs) in control systems for robots, autonomous systems, and other safety-critical applications will demand confidence in their safety, stability, and robustness. The aim of this article is to review recently developed NN model structures (both static and dynamic) with built-in certification of robustness in various forms, including bounded sensitivity to adversarial disturbances (Lipschitzness), dynamical stability, and robust invertibility. In doing so, we aim to help strengthen connections between control theory and machine learning. We present a unifying overview of tools to guarantee such robustness properties while retaining model expressivity and computational efficiency of training and inference. Expressivity is maintained via sophisticated certification methods building on the theory of integral quadratic constraints in robust control, while computational efficiency is maintained via direct parameterizations, which enable learning of robust models via standard unconstrained optimization methods such as gradient descent, without any auxiliary constraints or projections. We then show how such robust NN models can be used in building blocks within architectures for learning control system components such as physics-informed nonlinear observers (state estimators) with guaranteed convergence, control policy parameterizations with guaranteed stability and robustness, and Lyapunov functions and their variants, such as storage and value functions.
This survey reviews recent developments in fault diagnosis for both linear and nonlinear dynamical systems, covering model-based and data-driven approaches as well as passive and active detection and estimation methods. A central focus is placed on the geometric interpretation of diagnosis filters and their connection to the concept of behavioral sets, providing an intuitive view of their performance. We also review optimization-based techniques that enhance the robustness of linear filters when applied to nonlinear or uncertain systems. Furthermore, we point out recent progress in active fault diagnosis, where input design plays a key role in improving detectability and estimation accuracy. To bridge theory and practice, we include a set of real-world industrial applications that demonstrate the implementation and effectiveness of these methods in realistic settings.
Robot contests serve as catalysts for scientific advancement and innovation in robotics. While traditional robotics research focuses mainly on subsystem optimization or theoretical analysis and design, robot contests provide a crucial platform for benchmarking complete robot systems by encouraging integrated system development and novel solutions to realistic problems common to all participants. This article explains how robot competitions and challenges designed according to best practices foster progress across various domains. It highlights their significant impact on education, including their promotion of teamwork and problem-solving skills, and their role in technology transfer, exemplified by successful spin-off companies. Popular robot contests based on more traditional approaches are surveyed as sources of a diversity of approaches to scoring performance and the development of good practices. Alternative concepts, such as cooperative competitions to foster transferability across robotic platforms and tasks as well as performance assessments using benchmarking metrics, are introduced in the last part of the article.
Teach and repeat (T&R) navigation has gained popularity over the past 15 years for its reliable path tracking in global navigation satellite system (GNSS)–denied environments. By using topometric navigation, it blends the scalability of graphs to connect distant places of interest, with metric estimates of a robot's state that allow control algorithms to correct path-tracking errors. Robots have been field-tested in off-road environments and found commercial applications in mining, cleaning, and agriculture. We present a summary of the different interpretations of T&R, introduce the relative strengths of various sensors in T&R, highlight methods that boost the reliability of T&R on robot hardware, and propose that the effectiveness of local maps in topometric navigation comes from a sensor-dependent bias–variance trade-off.
