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Abstract The research on multi-robot systems has been divided into various fields, such as communication, navigation, task allocation, and collaborative transport. While significant progress has been made in each area, there has been limited research integrating these fields to build a fully autonomous multi-robot collaborative transport system. Therefore, we identify the key issues and propose a multi-robot collaborative transport system founded on ROS1 and conduct validation in a simulated environment, laying a solid foundation for the system to run on real robots. The primary contributions of this study include three key areas: (1) modeling and validating robot collaborative transport, (2) developing a visual task allocation system leveraging FastDDS service, and (3) resolving path collision issues in multi-robot navigation through both traditional methods and reinforcement learning techniques. Extensive experimental evaluations demonstrate that the proposed intelligent multi-robot collaborative transport system can autonomously navigate to target points for collaborative transport task. Performance assessments, based on the error between the target point and the object’s arrival point as well as the transport trajectory error, reveal that the system effectively completes the assigned tasks.

With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between Regions Of Interest (ROI) and Regions Of Non-Interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.

In the area of agriculture and livestock management, the integration of the Internet of Things (IoT) has emerged as a groundbreaking strategy to enhance operational efficiency and advance intelligent process management. However, this sector faces significant challenges, including ambiguity in product origins and limited regulatory oversight of IoT devices. This paper explores the innovative integration of blockchain technology within the agricultural and livestock IoT, highlighting how this convergence significantly enhances operational security and transparency. We provide an in-depth review of the latest applications and advancements of blockchain in these domains, offering a comprehensive analysis of the current state of technology and its implications. Furthermore, this paper discusses the potential future development trajectories in agricultural and livestock IoT, emphasizing blockchain’s role in addressing current challenges and shaping future innovations. The findings suggest that blockchain technology not only improves data security and trustworthiness but also opens new avenues for efficient and transparent management in agriculture and animal husbandry.

Abstract Intelligent Transportation has seen significant advancements with Deep Learning and the Internet of Things, making Traffic Signal Control (TSC) research crucial for reducing congestion, travel time, emissions, and energy consumption. Reinforcement Learning (RL) has emerged as the primary method for TSC, but centralized learning poses communication and computing challenges, while distributed learning struggles to adapt across intersections. This paper presents a novel approach using Federated Learning (FL)-based RL for TSC. FL integrates knowledge from local agents into a global model, overcoming intersection variations with a unified agent state structure. To endow the model with the capacity to globally represent the TSC task while preserving the distinctive feature information inherent to each intersection, a segment of the RL neural network is aggregated to the cloud, and the remaining layers undergo fine-tuning upon convergence of the model training process. Extensive experiments demonstrate reduced queuing and waiting times globally, and the successful scalability of the proposed model is validated on a real-world traffic network in Monaco, showing its potential for new intersections.

Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.