The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.

Intervention planning is crucial for maintenance operations in particle accelerator environments with ionizing radiation, during which the radiation dose received by maintenance workers should be reduced to a minimum. In this context, we discuss the development of a new software tool and the entailed methodology, including the visualization aspects. The software tool integrates interactive exploration of a scene depicting an accelerator facility augmented with residual radiation level simulations, with the visualization of intervention data such as the followed trajectory and maintenance tasks. Its conception allows for future inclusion of measurements performed by mobile robotic devices. In this work, we explore the systems engineering life cycle of the development process of an interactive intervention planner, which includes the needs analysis, specification explicitation, conceptual mathematical modelling, iterative implementation, design and prototype testing and usability testing.