Public transport for smart cities: Recent innovations and future challenges

This research explored the potential of artificial intelligence (AI)-assisted learning using ChatGPT in an engineering course at a university in South-east Asia. The study investigated the benefits and challenges that students may encounter when utilising ChatGPT-3.5 as a learning tool. This research developed an AI-assisted learning flow that empowers learners and lecturers to integrate ChatGPT into their teaching and learning processes. The flow was subsequently used to validate and assess a variety of exercises, tutorial tasks and assessment-like questions for the course under study. Introducing a self-rating system allowed the study to facilitate users in assessing the generative responses. The findings indicate that ChatGPT has significant potential to assist students; however, there is a necessity for training and offering guidance to students on effective interactions with ChatGPT. The study contributes to the evidence of the potential of AI-assisted learning and identifies areas for future research in refining the use of AI tools to better support students' educational journey.
Implications for practice or policy

Educators and administrators could review the usage of ChatGPT in an engineering technology course and study the implications of generative AI tools in higher education.
Academics could adapt and modify the proposed AI-assisted learning flow in this paper to suit their classroom.
Students can review and adopt the proposed AI-assisted learning flow in this paper for their studies.
Researchers could follow up on the application of ChatGPT in teaching and learning: teaching quality and student experience, academic integrity and assessment design.


Digital transformation in engineering education: Exploring the potential of AI-assisted learning

pdf/towards-lifelong-federated-learning-in-autonomous-mobile-3t2sgkli.pdf

Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer

Deep neural networks (DNNs) are increasingly utilized in robotic tasks. However, resource-constrained mobile robots often do not have sufficient onboard computing resources or power reserves to run the most accurate and state-of-the-art DNNs. Cloud robotics has the benefit of enabling robots to offload DNNs to cloud servers, which is considered a promising technology to address the issue. However, comprehensive issues exist, including flexibility, convenience, offloading policy, and especially network robustness in its implementations and deployments. Although it is essential to promote cloud robotics to be practical, a cloud robotic system that addresses these issues comprehensively has never been proposed. Accordingly, in this work, we present RoboEC2, a novel cloud robotic system with dynamic network offloading implemented assisted by Amazon EC2. To realize the goal, we present a cloud-edge cooperation framework based on ROS and Amazon Web Services (AWS) and a network offloading approach with a dynamic splitting way. RoboEC2 is capable of executing its network offloading program in any conditions, including disconnected. We model the DNN offloading problem in RoboEC2 to a specific multi-objective optimization problem and address it by proposing the Spotlight Criteria Algorithm (SCA). RoboEC2 is flexible, convenient, and robust. It is the first cloud robotic system with no constraints on time, location, or computing power. Finally, We demonstrate RoboEC2 with analyses and experiments that it performs better in comprehensive metrics compared with the state-of-the-art approach. We open-source the system at https://github.com/RoboEC2/RoboEC2. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners —RoboEC2 is a work that combines cloud computing and robotics. As the deep learning models are becoming larger, robots are becoming more and more difficult to run the state-of-the-art models locally. It has become one of the major problems in robotics. RoboEC2 was proposed to address this problem. It enables more robotics researchers to equip their robots with the power of cloud computing. To be honest, it is very difficult for us to complete this work that is a robotic system with cloud computing. We need to address a lot of difficulties such as network, the cloud platform, algorithms, robot platforms, and conduct various robotic tasks. We have spent more than one year on this system and overcome countless difficulties to complete it. All of what we do is to make robotics developer easier strengthen their robots with cloud. Whether you are an autonomous driving engineer, robotic arm developer, SLAM researcher, mobile robotics researcher, or any other developer working on robotics applications based on ROS and deep learning models, you can use RoboEC2 to make them perform better. You don’t need to worry about networking, because RoboEC2 has solved it perfectly. You don’t need to worry about the serious algorithms in the system, because we provide easily used interact files for you to configure. You just need to tell RoboEC2 which metrics your robotics application needs to focus on. With RoboEC2, all the robotic researchers/developers are capable of enhancing their robotic applications with cloud computing in just a few simple steps and executing them in any network conditions. So, why not?

RoboEC2: A Novel Cloud Robotic System With Dynamic Network Offloading Assisted by Amazon EC2

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.

pdf/communication-resources-constrained-hierarchical-federated-1s0nthrf.pdf

Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized collaboratively. To address this issue, the work presents Peer-Assisted Robotic Learning (PARL) in robotics, which is inspired by the peer-assisted learning in cognitive psychology and pedagogy. PARL implements data collaboration with the framework of cloud robotic systems. Both data and models are shared by robots to the cloud after semantic computing and training locally. The cloud converges the data and performs augmentation, integration, and transferring. Finally, fine tune this larger shared dataset in the cloud to local robots. Furthermore, we propose the DAT Network (Data Augmentation and Transferring Network) to implement the data processing in PARL. DAT Network can realize the augmentation of data from multi-local robots. We conduct experiments on a simplified self-driving task for robots (cars). DAT Network has a significant improvement in the augmentation in self-driving scenarios. Along with this, the self-driving experimental results also demonstrate that PARL is capable of improving learning effects with data collaboration of local robots.

Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems

Most of the current path planning applications only focus on a single agent, without considering the surrounding robots or dynamic obstacles. In safety-critical environments, such as the crowded playground, the robot may have increased difficulty reaching the destination with crowds moving around, sometimes even cause damages. With the development of cloud computing and swarm intelligence, the concept of “cloud robotic” has been followed by more scholars. With collaboration and information sharing between robots, the cluster has better problem-solving skills than the individual. In this paper, we propose a cloud-based multi-agent navigation algorithm in dynamic environments. We propose the concept of pheromones of dynamic obstacles that represent congestion to enable clusters to plan collaboratively. Widespread static sensors are introduced to jointly estimate congestion in the environment with robots. When an agent needs route planning, the cloud provides safe and fast route with intermediate points. The robot uses a simple yet effective local planner based on artificial potential field (APF) to trace the trajectory. We demonstrate this approach's effectiveness compared to traditional A* global planner and APF local planner with traditional repulsion function. Experiments show that our cloud-based method reduces the number of collisions by 92.6%, with only 35% increase in path length. All code for reproducing the experiments is at https://github.com/Asber777/CDPP.

Cloud-based Robot Path Planning in Dynamic Environments

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Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems

Cloud-based Robot Path Planning in Dynamic Environments

Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems