Communication and Control Co-Design with Data-Driven Safety Guarantees for Networked Robots

Cellular communication networks, originally designed for mobile phones, are increasingly being used by autonomous robots that require reliable connectivity to perform complex tasks. Advancements in artificial intelligence are enabling new applications for unmanned aerial vehicles, connected autonomous vehicles, and industrial mobile robots. However, many robots lack the onboard computational resources to run advanced algorithms and must offload processing to edge servers via the communication network. This introduces new challenges, as transmitting large volumes of sensor data can lead to network congestion, especially in multi-robot deployments. To address this, communication-control co-design has emerged as a critical research direction, enabling robots to adapt their behaviors to network conditions and maintain safe, efficient operation with limited resources. The overall objective of this thesis is to propose a new co-design framework for connected mobile robots. Specifically, we investigate how to adaptively trade off communication and control performance while guaranteeing safe navigation usingsensor-based localization. This is important for industrial mobile robots operating in indoor environments such as factories and warehouses, where satellite-based positioning is not an option, but could also be relevant for robots operating in environments with limited or no satellite visibility, such as mines, dense forests, and urban environments. This thesis consists of two main contributions. First, we design a motion controller for a mobile robot to achieve safe navigation among obstacles only detectable by external sensors. Since the robot cannot detect these obstacles with its own sensors, it must avoid them based on its estimated position and a known obstacle map. To account for imperfections in the estimated position, we incorporate a safety filter based on a robust Control Barrier Function that is designed to prevent collisions for a worst-case error in localization. This error is obtained by measuring the error between the estimated and the true position using experimental data. We then demonstrate that the proposed method ensures safe navigation in experiments with a Mobile YuMi Research Platform, a real mobile robot. Second, we propose a co-design framework for connected robots that must transmit sensor data over the network to receive updated position estimates from a localization algorithm offloaded to the edge. Since the robot’s motion also influences the accuracy of the localization, we co-design the communication and control strategy to achieve a desired level of localization uncertainty. By deriving this uncertainty requirement from a safety constraint based on the robot’s distance to mapped obstacles, we can verify that the robot navigates safely. The approach is validated in experiments with a real robot, demonstrating that a trade-off between communication and speed can be achieved without compromising safety.