Automation, Volume 6, Issue 3 (September 2025) – 7 articles

Wheel loaders in mines and construction sites repeatedly load soil from a pile to load receivers. Automating this task presents a challenging planning problem since each loading’s performance depends on the pile state, which depends on previous loadings. We investigate an end-to-end optimization approach considering future loading outcomes and transportation costs between the pile and load receivers. To predict the evolution of the pile state and the loading performance, we use world models that leverage deep neural networks trained on numerous simulated loading cycles. A look-ahead tree search optimizes the sequence of loading actions by evaluating the performance of thousands of action candidates, which expand into subsequent action candidates under the predicted pile states recursively. Test results demonstrate that, over a horizon of 15 sequential loadings, the look-ahead tree search is 6% more efficient than a greedy strategy, which always selects the action that maximizes the current single loading performance, and 14% more efficient than using a fixed loading controller optimized for the nominal case. Full article

This paper presents an advanced rescheduling approach that jointly applies two sets of objective functions within a novel multi-objective search algorithm and a production simulation of the manufacturing system. The role of the first set of objective functions is to optimize the performance of production systems, while the second newly proposed set of objective functions aims to minimize the intervention changes from the original schedule, thereby supporting schedule stability and smooth manufacturing processes. The combined use of these two objective sets is ensured by a flexible candidate-qualification method, which allows for priorities to be assigned to each objective function, offering precise control over the rescheduling process. The applicability of this approach is presented through an example of an extended flexible flow shop manufacturing system. A new test problem containing 16 objective functions has been developed. The effectiveness of the proposed new objective functions and rescheduling method is validated by a simulation model. The obtained numerical results are also presented in this paper. The aim of this study is not to compare different search algorithms but rather to demonstrate the beneficial impact of change-minimizing objective functions within a given search framework. Full article

Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems. Full article

This paper presents a non-fragile observer-based dissipative control strategy for the suspension systems of electric vehicles equipped with in-wheel motors, accounting for input delays, actuator faults, and observer gain uncertainty. Traditional control approaches—such as $H_{\infty }$, passive control, and robust feedback schemes, often address these challenges in isolation and with increased conservatism. In contrast, this work introduces a unified framework that integrates fault-tolerant control, delay compensation, and robust state estimation within a dissipativity-based setting. A novel dissipativity analysis tailored to Electric Vehicle Active Suspension Systems (EV-ASSs) is developed, with nonzero delay bounds explicitly incorporated into the stability conditions. The observer is designed to ensure accurate state estimation under gain perturbations, enabling robust full-state feedback control. Stability and performance criteria are formulated via Linear Matrix Inequalities (LMIs) using advanced integral inequalities to reduce conservatism. Numerical simulations validate the proposed method, demonstrating effective fault-tolerant performance, disturbance rejection, and precise state reconstruction, thereby extending beyond the capabilities of traditional control frameworks. Full article

The control of multichannel objects is an independent section of cybernetics. Traditionally, objects are considered in which the number of inputs coincides with the number of outputs, but there are separate publications devoted to cases when the number of inputs does not coincide with the number of outputs. Even for this purpose, special terminology has been invented. If traditionally the mathematical model of an object is a square matrix transfer function, then in this case the term “non-square matrix transfer function” is used. This term is unsuccessful, as shown in this article, since it combines problems that are simplified in comparison with traditional ones, and problems that are, strictly speaking, unsolvable. This article demonstrates that an additional number of object inputs is not only not a problem, but also offers additional opportunities, while an excess number of outputs is an insurmountable problem: one can only abandon the problem of controlling redundant outputs. This situation should not be confused with the situation of the presence of additional outputs of sensors of the controlled variable or intermediate values, which also serve to simplify the solution of the problem and not to complicate it. If output signals are understood as independent output values that should be independently controlled, then the number of outputs should never exceed the number of inputs, although this situation can easily be confused with some other similar situations. This article also shows an example of how additional signals, sometimes mistakenly called additional outputs, can be used, and gives recommendations for various situations. Full article

Autonomous vehicles are increasingly being adopted, with manufacturers competing to enhance automation capabilities. While full automation eliminates human input, lower levels still require driver intervention under specific conditions. This study presents the design and development of a prototype vehicle featuring both low- and high-level control systems, integrated with a 5G-based teleoperation interface that enables seamless switching between autonomous and remote-control modes. The system includes a malfunction surveillance unit that monitors communication latency and obstacle conditions, triggering a hardware-based emergency braking mechanism when safety thresholds are exceeded. Field experiments conducted over four test phases around Chulalongkorn University demonstrated stable performance under both driving modes. Mean lateral deviations ranged from 0.19 m to 0.33 m, with maximum deviations up to 0.88 m. Average end-to-end latency was 109.7 ms, with worst-case spikes of 316.6 ms. The emergency fallback system successfully identified all predefined fault conditions and responded with timely braking. Latency-aware stopping analysis showed an increase in braking distance from 1.42 m to 2.37 m at 3 m/s. In scenarios with extreme latency (>500 ms), the system required operator steering input or fallback to autonomous mode to avoid obstacles. These results confirm the platform’s effectiveness in real-world teleoperation over public 5G networks and its potential scalability for broader deployment. Full article

This paper describes an innovative wireless mobile robotics control system based on speech recognition, where the ESP32 microcontroller is used to control motors, facilitate Bluetooth communication, and deploy an Android application for the real-time speech recognition logic. With speech processed on the Android device and motor commands handled on the ESP32, the study achieves significant performance gains through distributed architectures while maintaining low latency for feedback control. In experimental tests over a range of 1–10 m, stable 110–140 ms command latencies, with low variation (±15 ms) were observed. The system’s voice and manual button modes both yield over 92% accuracy with the aid of natural language processing, resulting in training requirements being low, and displaying strong performance in high-noise environments. The novelty of this work is evident through an adaptive keyword spotting algorithm for improved recognition performance in high-noise environments and a gradual latency management system that optimizes processing parameters in the presence of noise. By providing a user-friendly, real-time speech interface, this work serves to enhance human–robot interaction when considering future assistive devices, educational platforms, and advanced automated navigation research. Full article