Automation, Volume 5, Issue 4 (December 2024) – 10 articles

A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given the constraints of battery, bandwidth, multi-hop, mobility, and enormous network sizes. Recently, Swarm Intelligence (SI) methods have been employed in MANET routing due to similarities between swarm behavior and routing. These methods are applied to obtain ideal solutions that ensure flexibility. This paper implements an enhanced Particle Swarm Optimization (EPSO) algorithm that improves MANET performance by enhancing the routing protocol. The proposed algorithm selects the stable path by considering multiple metrics such as short distance, delay of the path, and energy consumption. The simulation results illustrate that the EPSO outperforms other existing approaches regarding throughput, PDR, and number of valid paths. Full article

Electric Vehicles (EVs) are set to play a crucial role in the energy transition. Although EVs offer significant environmental benefits, their technology still faces major challenges related to performance optimization, energy efficiency improvement, and cost reduction. A key point to address these challenges is the accurate identification of the speed/torque operating points of the drive systems. However, this identification is generally achieved using mechanical sensors, which are fragile, bulky, and expensive. This paper aims to develop, implement, and validate a speed/torque observer in real time based on the Extended Kalman Filter (EKF) approach for an EV equipped with an Open-End Winding Induction Motor with Dual Inverter (OEWIM-DI). The implementation of the EKF is based on the state modeling of the OEWIM-DI, enabling the observation of the torque and speed using voltage and current measurements. The validation of this approach is conducted experimentally on the FPGA and DS1104 boards. The results show that this approach offers excellent performance in terms of accuracy, stability, and real-time response speed. These results suggest that the proposed method could significantly contribute to the advancement of EV technology by providing a more robust and cost-effective alternative to traditional mechanical sensors while improving the overall efficiency and performance of EV drive systems. Full article

A method for cooperative transportation, which required formation change in a traveling environment, is gaining interest. Deep reinforcement learning is used in formation changes for multi-robot cases. The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. On the other hand, re-learning may be required in unrecognized circumstances by using the MADDPG method. Although the development of MADDPG using model-based learning and imitation learning has been applied to reduce learning time, it is unclear how the learning results are transferred when the number of robots changes. For example, in the GASIL-MADDPG (Generative adversarial self-imitation learning and Multi-agent Deep Deterministic Policy Gradient) method, how the results of three robot training can be transferred to the four robots’ neural networks is uncertain. Nowadays, Scaled Dot Product Attention (SDPA) has attracted attention and is highly impactful for its speed and accuracy in natural language processing. When transfer learning is combined with fast computation, the efficiency of edge-level re-learning is improved. This paper proposes a formation change algorithm that allows easy and fast multi-robot knowledge transfer using SDPA combined with MAPPO (Multi-Agent Proximal Policy Optimization), compared to other methods. This algorithm applies SDPA to multi-robot formation learning and performs fast learning by transferring the acquired knowledge of formation changes to a certain number of robots. The proposed algorithm is verified by simulating the robot formation change and was able to achieve dramatic high-speed learning capabilities. The proposed SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) learned 20.83 times faster than the Deep Dyna-Q method. Furthermore, using transfer learning from a three-robot to five-robot case, the method shows that the learning time can be reduced by about 56.57 percent. A scenario of three-robot to five-robot is chosen based on the number of robots often used in cooperative robots. Full article

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings. Full article

Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to create a highway traffic environment where the agent can be guided safely through surrounding vehicles. A hierarchical control framework is then provided to manage high-level driving decisions and low-level control commands, such as speed and acceleration. Next, a special DRL-based method called deep deterministic policy gradient (DDPG) is used to derive decision strategies for use on the highway. The performance of the DDPG algorithm is compared with that of the DQN and PPO algorithms, and the results are evaluated. The simulation results show that the DDPG algorithm can effectively and safely handle highway traffic tasks. Full article

The increasing adoption of Deep Learning (DL)-based Object Detection (OD) models in smart manufacturing has opened up new avenues for optimizing production processes. Traditional industries facing capacity constraints require noninvasive methods for in-depth operations analysis to optimize processes and increase revenue. In this study, we propose a novel framework for capacity constraint analysis that identifies bottlenecks in production facilities and conducts cycle time studies using an end-to-end pipeline. This pipeline employs a Convolutional Neural Network (CNN)-based OD model to accurately identify potential objects on the production floor, followed by a CNN-based tracker to monitor their lifecycle in each workstation. The extracted metadata are further processed through the proposed framework. Our analysis of a real-world manufacturing facility over six months revealed that the bottleneck station operated at only 73.1% productivity, falling to less than 40% on certain days; additionally, the processing time of each item increased by 53% during certain weeks due to critical labor and materials shortages. These findings highlight significant opportunities for process optimization and efficiency improvements. The proposed pipeline can be extended to other production facilities where manual labor is used to assemble parts, and can be used to analyze and manage labor and materials over time as well as to conduct audits and improve overall yields, potentially transforming capacity management in smart manufacturing environments. Full article

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption. Full article

The integration of thermal imaging data with multimodal large language models (MLLMs) offers promising advancements for enhancing the safety and functionality of autonomous driving systems (ADS) and intelligent transportation systems (ITS). This study investigates the potential of MLLMs, specifically GPT-4 Vision Preview and Gemini 1.0 Pro Vision, for interpreting thermal images for applications in ADS and ITS. Two primary research questions are addressed: the capacity of these models to detect and enumerate objects within thermal images, and to determine whether pairs of image sources represent the same scene. Furthermore, we propose a framework for object detection and classification by integrating infrared (IR) and RGB images of the same scene without requiring localization data. This framework is particularly valuable for enhancing the detection and classification accuracy in environments where both IR and RGB cameras are essential. By employing zero-shot in-context learning for object detection and the chain-of-thought technique for scene discernment, this study demonstrates that MLLMs can recognize objects such as vehicles and individuals with promising results, even in the challenging domain of thermal imaging. The results indicate a high true positive rate for larger objects and moderate success in scene discernment, with a recall of 0.91 and a precision of 0.79 for similar scenes. The integration of IR and RGB images further enhances detection capabilities, achieving an average precision of 0.93 and an average recall of 0.56. This approach leverages the complementary strengths of each modality to compensate for individual limitations. This study highlights the potential of combining advanced AI methodologies with thermal imaging to enhance the accuracy and reliability of ADS, while identifying areas for improvement in model performance. Full article

The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are sustainably reused, thereby extending the life cycle of the resources and enhancing overall environmental sustainability. In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process. This paper reviews the application of AI techniques in various stages of retired battery disassembly. A significant focus is placed on estimating batteries’ state of health (SOH), which is crucial for determining the availability of retired EV batteries. AI-driven methods for planning battery disassembly sequences are examined, revealing potential efficiency gains and cost reductions. AI-driven disassembly operations are discussed, highlighting how AI can streamline processes, improve safety, and reduce environmental hazards. The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency. This comprehensive analysis underscores the transformative potential of AI in revolutionizing the management of retired EVBs. Full article

The automatic detection of road surface cracks is a crucial task in road maintenance, but the complexity of crack topology and the susceptibility of detection results to environmental interference make it challenging. To address this issue, this paper proposes an automatic crack detection method based on density clustering using random forest. First, a shadow elimination method based on brightness division is proposed to address the issue of lighting conditions affecting detection results in road images. This method compensates for brightness and enhances details, eliminating shadows while preserving texture information. Second, by combining the random forest algorithm with density clustering, the impact of noise on crack extraction is reduced, enabling the complete extraction and screening of crack information. This overcomes the shortcomings of the random forest method, which only detects crack edge information with low accuracy. The algorithm proposed in this paper was tested on the CFD and Cracktree200 datasets, achieving precision of 87.4% and 84.6%, recall rates of 83.9% and 82.6%, and F-1 scores of 85.6% and 83.6%, respectively. Compared to the CrackForest algorithm, it significantly improves accuracy, recall rate, and F-1 score. Compared to the UNet++ and Deeplabv3+ algorithms, it also achieves better detection results. The results show that the algorithm proposed in this paper can effectively overcome the impact of uneven brightness and complex topological structures on crack target detection, improving the accuracy of road crack detection and surpassing similar algorithms. It can provide technical support for the automatic detection of road surface cracks. Full article