In parallel with the efforts towards advancing 5G and 6G networks, significant efforts have been attained in robotics. However, the areas of robotics and wireless communication have so far progressed in a rather silo manner. In the emerging era of 6G (such as future smart cities and factories), applications will become increasingly complex, requiring seamless integration of robots (aerial, ground, and underwater robots) and wireless communications. The main challenges revolve around effectively combining these areas to optimally orchestrate and deliver services and the overall use case deployment in various scenarios. Two possible scenarios can be envisioned for such intelligent wireless networks: i) network-aided robotics, where the network can provide safe communication for control and monitoring robots beyond the visual line-of-sight, and ii) robot-aided networks in which robots will assist the network in various ways ranging from coverage extension to accurate sensing and localization.

The main principles of future mobile communication systems are the ability to handle a higher degree of flexibility and functionality enabling future intelligent connectivity and control in a distributed manner along with co-resident communication and sensing: Moving away from traditional network architectures with passive nodes toward a ubiquitous intelligence, where native AI plays a prominent role to orchestrate different parts of the network from core to edge and to the cloud. In parallel, the 6G Radio Access Network (RAN) is evolving towards an Open and disaggregated architecture. 3GPP has surveyed and documented many functional splits in TR 38.801, leading toward the emergence of fully disaggregated RAN implementation. This evolution aligns with integrating robots into wireless networks, where each robot can act as a distributed node to assist the network. Despite the widespread applications of distributed networks and considerable attention received from academia and industry, integrating robots into distributed networks for collaborative learning and decision-making is still in its infancy. In particular, several challenges regarding the high communication overhead, modeling the behavior of robots in the presence of radio components, modeling robot-to-robot and robot-to-infrastructure radio links, and, in general, the performance and feasibility regarding the implementation and deployment of such networks are yet to be addressed.

Paper Submission Deadline: 6 February 2025 (Firm)

Paper Acceptance Notification: 10 March 2025

Camera Ready: 31 March 2025

Submission link via EDAS: https://edas.info/N33205

For more details please check out the IEEE ICC 2025 website: https://icc2025.ieee-icc.org