TY - GEN
T1 - Enhancing 5G-enabled robots autonomy by radio-aware semantic maps
AU - Lendinez Ibanez, Adrian
AU - Zanzi, Lanfranco
AU - Moreno, Sandra
AU - Gari, Guillem
AU - Li, Xi
AU - Qiu, Renxi
AU - Costa-Perez, Xavier
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/12/13
Y1 - 2023/12/13
N2 - Future robotics systems aiming for true autonomy must be robust against dynamic and unstructured environments. The 5th generation (5G) mobile network is expected to provide ubiquitous, reliable and low-latency wireless communications to ground robots, especially in outdoor scenarios. Empowered by 5G, the digital transformation of robotics is emerging, enabled by the cloud-native paradigm and the adoption of edge-computing principles for heavy computational task offloading. However, wireless link quality fluctuates due to multiple aspects such as the topography of the deployment area, the presence of obstacles, robots' movement and the configuration of the serving base stations. This directly impacts not only the connectivity to the robots but also the performance of robot operations, resulting in severe challenges when targeting full robot autonomy. To address such challenges, in this paper, we propose a framework to build a semantic map based on radio quality. By means of our proposed approach, mobile robots can gain knowledge on up-to-date radio context map information of the surrounding environment, hence enabling reliable and efficient robotics operations.
AB - Future robotics systems aiming for true autonomy must be robust against dynamic and unstructured environments. The 5th generation (5G) mobile network is expected to provide ubiquitous, reliable and low-latency wireless communications to ground robots, especially in outdoor scenarios. Empowered by 5G, the digital transformation of robotics is emerging, enabled by the cloud-native paradigm and the adoption of edge-computing principles for heavy computational task offloading. However, wireless link quality fluctuates due to multiple aspects such as the topography of the deployment area, the presence of obstacles, robots' movement and the configuration of the serving base stations. This directly impacts not only the connectivity to the robots but also the performance of robot operations, resulting in severe challenges when targeting full robot autonomy. To address such challenges, in this paper, we propose a framework to build a semantic map based on radio quality. By means of our proposed approach, mobile robots can gain knowledge on up-to-date radio context map information of the surrounding environment, hence enabling reliable and efficient robotics operations.
KW - 5G mobile communication
KW - Reliability
KW - Semantics
KW - Wireless communications
KW - mobile robots
KW - surfaces
KW - task analysis
UR - https://www.scopus.com/pages/publications/85182526124
U2 - 10.1109/IROS55552.2023.10342279
DO - 10.1109/IROS55552.2023.10342279
M3 - Conference contribution
SN - 9781665491907
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6267
EP - 6272
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Y2 - 1 October 2023 through 5 October 2023
ER -