5G and Edge-Cloud computing play pivotal roles in fostering the flexibility and intelligence required by Industry 4.0 for modern production systems. This paper presents our investigation into deploying the control logic of two UR5e robotic arms on the Edge Cloud, utilizing 5G for communication and establishing a soft Programmable Logic Controller (PLC). Leveraging the Message Queue Telemetry Transport (MQTT) and Network Time Protocol (NTP), the soft PLC coordinates the two robotic arms synchronously, mitigating the impact of the network latency variance. In the context of a concurrent delay-sensitive use case, our measurements reveal that, compared to Ethernet, wireless 5G results in 12 ms and 9 ms increases in the 99.9th percentiles of uplink times and downlink times, respectively. However, the NTP-based coordination mechanism in our implemented solution demonstrates a 77 % reduction in behavior inconsistencies caused by the 5G network latency variance in 99.98 % of cases.

Utilizing robots for autonomous target search in complex and unknown environments can greatly improve the efficiency of search and rescue missions. However, existing methods have shown inadequate performance due to hardware platform limitations, inefficient viewpoint selection strategies, and conservative motion planning. In this work, we propose HEATS, which enhances the search capability of mobile manipulators in complex and unknown environments. We design a target viewpoint planner tailored to the strengths of mobile manipulators, ensuring efficient and comprehensive viewpoint planning. Supported by this, a whole-body motion planner integrates global path search with local IPC optimization, enabling the mobile manipulator to safely and agilely visit target viewpoints, significantly improving search performance. We present extensive simulated and real-world tests, in which our method demonstrates reduced search time, higher target search completeness, and lower movement cost compared to classic and state-of-the-art approaches. Our method will be open-sourced for community benefit3.

Autonomous ground mobile robots rely on their configuration characteristics to prevent tip-overs and collisions, ensuring safe navigation in complex environments. However, complex configurations with specially designed links and joints produce a higher-dimensional workspace and bring significant challenges for path planning, especially in large-scale rough terrains. To address this, we propose a real-time multi-level terrain-aware path planning framework that integrates different levels of terrain awareness into the global and local layers. An implicit map representation is introduced at the global layer to enable efficient terrain analysis and path planning, while an iterative geometric evaluation is designed at the local layer to estimate configuration stability and improve path smoothness. By sharing the global layer information with the local layer, the framework enhances path planning efficiency and adaptability in complex environments. Its modular design supports diverse robot configurations and pathfinding algorithms, enabling effective autonomous navigation in large-scale 3D terrains with online or offline maps. Simulations and real-world experiments demonstrated that our approach outperforms state-of-the-arts across diverse environments, including uneven terrains, multi-layered structures, and complex debris fields. The results highlighted that our approach provides faster and safer path planning, more accurate and robust configuration-stability estimation, and higher success rates in traversing complex 3D environments.

Cloud and edge computing play a crucial role in enabling the intelligence of Industry 4.0, while wireless tech-nologies like 5G and Wi-Fi 6 enhance its flexibility. However, selecting the appropriate technologies is nontrivial. In this paper, we present our measurements (over 360 hours) and analysis of response times in cloud and edge computing using different network access methods. We provide recommendations for tech-nology selection based on our findings. Our results highlight the unique advantages of 5G and Wi-Fi 6 at different percentiles, and the characteristics of cloud and edge computing in terms of workload processing and network propagation. The choice between these technologies should consider the Quality of Service (QoS) requirements and processing workloads of applications, as well as the computational resources of edge and cloud servers.

5G is a flexible key technology for Industry 4.0. Combined with edge-computing, Industrial Internet-of-Things (IIoT) systems, and other secondary wireless technologies such as Wi-Fi and MuLTEfire, it will transform the Factories of the Future into more advanced, safer, and more flexible smart production environments. In this paper, we present our research visions, and our reference implementation of a 5G-enabled factory where industrial applications and solutions can be developed and tested in operational conditions. The paper introduces the main technological developments, including a selection of observations and performance evaluations useful for engineers in both the communications and manufacturing domains. Future research visions such as the integration of a digital twin environment are also briefly discussed.