waveSLAM: Empowering Accurate Indoor Mapping Using Off-the-Shelf Millimeter-wave Self-sensing

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20deg in angle orientation which is compatible with Lidar when building indoor maps.

Orchestrating Networked Robotic Applications

In this letter, we formulate the orchestration problem for networked robotic applications considering contextual information. Any solution to the proposed formulation provides adequate routing updates, migration, and radio handover decisions as the robot moves. We prove the NP-hard nature of the problem, and solve it for a remote driving robotic application with or without some contextual information, as in state-of-the-art. Results show that without contextual information it is impossible to meet the latency requirements of a remote-driving robotic application.

FoReCo: a forecast-based recovery mechanism for real-time remote control of robotic manipulators

Wireless communications represent a game changer for future manufacturing plants, enabling flexible production chains as machinery and other components are not restricted to a location by the rigid wired connections on the factory floor. However, the presence of electromagnetic interference in the wireless spectrum may result in packet loss and delay, making it a challenging environment to meet the extreme reliability requirements of industrial applications. In such conditions, achieving real-time remote control, either from the Edge or Cloud, becomes complex. In this paper, we investigate a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands caused by interference in the wireless channel. FoReCo is evaluated through both simulation and experimentation in interference prone IEEE 802.11 wireless links, and using a commercial research robot that performs pick-and-place tasks. Results show that in case of interference, FoReCo trajectory error is decreased by x18 and x2 times in simulation and experimentation, and that FoReCo is sufficiently lightweight to be deployed in the hardware of already used in existing solutions.

COTORRA: COntext-aware Testbed fOR Robotic Applications

Edge & Fog computing have received considerable attention as promising candidates for the evolution of robotic systems. In this letter, we propose COTORRA, an Edge & Fog driven robotic testbed that combines context information with robot sensor data to validate innovative concepts for robotic systems prior to being applied in a production environment. In lab/university, we established COTORRA as an easy applicable and modular testbed on top of heterogeneous network infrastructure. COTORRA is open for pluggable robotic applications. To verify its feasibility and assess its performance, we ran set of experiments that show how autonomous navigation applications can achieve target latencies bellow 15ms or perform an inter-domain (DLT) federation within 19 seconds.

DLT federation for Edge robotics

The concept of federation in 5G and NFV networks aims to provide orchestration of services across multiple administrative domains. Edge robotics, as a field of robotics, implements the robot control on the network edge by relying on low-latency and reliable access connectivity. In this paper, we propose a solution that enables Edge robotics service to expand its service footprint or access coverage over multiple administrative domains. We propose application of Distributed ledger technologies (DLTs) for the federation procedures to enable private, secure and trusty interactions between undisclosed administrative domains. The solution is applied on a real-case Edge robotics experimental scenario. The results show that it takes around 19 seconds to deploy & federate a Edge robotics service in an external/anonymous domain without any service down-time.