Cognitive Robotics最新文献

The motion cueing algorithm (MCA) enhances the realism of simulator driving experiences by generating vehicle motions within platform limitations. Existing MCAs are typically tuned for worst-case scenarios, limiting their efficiency for medium or slow driving motions. This study proposes a comprehensive MCA unit using learning-based models to overcome this problem and efficiently utilise the simulator workspace for all driving scenarios. Data samples are regenerated to cover various motion signal levels, and three classical washout filters are tuned to extract optimal motion signals. A multilayer perceptron (MLP) is trained with these extracted datasets, forming an AI-based MCA that provides high-fidelity driving motions for any scenario while optimising the platform workspace. Simulink/MATLAB is used for modelling and evaluation. Results demonstrate the proposed model's superior performance, with lower motion sensation errors, a higher correlation between sensed motion signals, and more efficient platform workspace usage.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research.

Social affordances, although key in human-robot interaction processes, have received little attention in robotics. Hence, it remains unclear whether the prevailing mechanisms to exploit and learn affordances in the absence of human interaction can be extended to affordances in social contexts. This study provides a review of the concept of affordance in psychology and robotics and proposes a new view on social affordances in robotics and their differences from physical affordances. We moreover show how the model-based reinforcement learning theory provides a useful framework to study and compare social and physical affordances. To further study their differences, we present a new benchmark task mixing navigation and social interaction, in which a robot has to make a human follow and reach different goal positions in a row. This new task is solved in simulation using a modular architecture and reinforcement learning.

In the realm of speech emotion recognition, researchers strive to refine representation methods for improved emotional information capture. Traditional one-dimensional time series classification falls short in expressing intricate emotional patterns present in speech signals, posing challenges in accuracy and robustness. This study introduces an innovative algorithm leveraging Hilbert curves to transform one-dimensional speech data into two-dimensional form, enhancing feature extraction accuracy. A tiling module based on Hilbert curve maximizes Hilbert curve arrangements for improved emotional information capture. Results reveal spatial efficiency gains up to 23,195 times pixel units, enhancing data storage. With an exceptional 98.73% accuracy, the proposed approach traditional methods, affirming its superior emotion classification performance on the same dataset. These empirical findings underscore the effectiveness of our proposed method in advancing speech emotion recognition.

Driving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the diverse and differentiated features of typographic fonts, simultaneously extracting comprehensive characteristics is an extremely challenging task. To effectively break through these performance degradation issues, a multi-scale tire sidewall text region detection algorithm based on YOLOv5 is introduced, called YOLOT, which fuses comprehensive feature information in both width and depth directions. In this study, we firstly propose the Width and Depth Awareness (WDA) module in the text region detection field and successfully integrated it with the FPN structure to form the WDA-FPN. The purpose of WDA-FPN is to empower the network to capture multi-scale and multi-shape features in images, thereby augmenting the algorithm’s abstraction and representation of image features and concurrently boosting its robustness and generalization performance. Experimental findings indicate that, compared to the primary algorithm, YOLOT achieves significant improvement in accuracy, providing a higher detection reliability. The dataset and code for the paper are available at: https://github.com/Cloude-dehua/YOLOT.

These days, plastic trash is exponentially overwhelming our waterways. The catastrophe has attracted global attention at this point. As a result, protecting the environment on the water's surface has received increasing focus. Currently, manpower can be used to clean up contaminated water bodies like ponds, rivers, and oceans. Using the current cleaning approach results in low efficiency and hazard. The detection, collection, sorting, and removal of plastic trash from such water surfaces has been the subject of relatively little robotic research, despite the dire circumstances. From private sources, there are very few individual efforts to be found. In order to attain great efficiency without human assistance or operation, a fully autonomous water surface cleaning robot is proposed in this study. The robot was created to adapt to any type of water body found in the real world. An efficient object identification machine learning technique can be suggested for the creation of autonomous cleaning robots. This study improved the Single Short Detection (SSD) method to recognise objects accurately. Because of the enhanced detection techniques, the robot is able to collect trash on its own. With a mean average precision (mAP) of 94.099 % and a detection speed of up to 64.67 frames per second, experimental findings show that the enhanced SSD has exceptional detection speed and accuracy.