This paper investigates bounds on the estimation error of a linear system affected by norm-bounded disturbances and full sensor attacks. The system is equipped with a detector that evaluates the norm of the innovation signal to detect faults, and the attacker wants to avoid detection. We utilize induced L∞ system norms, also called peak-to-peak norms, to compare the estimation error bounds under nominal operations and under attack. This leads to a sufficient condition for when the bound on the estimation error is smaller during an attack than during nominal operation. This condition is independent of the attack strategy and depends only on the attacker's desire to remain undetected and (indirectly) the observer gain. Therefore, we investigate both an observer design method, that seeks to reduce the error bound under attack while keeping the nominal error bound low, and detector threshold tuning. As a numerical illustration, we show how a sensor attack can deactivate a robust safety filter based on control barrier functions if the attacked error bound is larger than the nominal one. We also statistically evaluate our observer design method and the effect of the detector threshold.

With Edge Computing and 5G, industrial mobile robots will be able to offload computationally expensive algorithms such as sensor-based localization to the edge. However, multiple robots streaming large volumes of data over the network simultaneously will create network congestion, leading to high latencies and data loss, which can severely impact the robot operation. In this paper, we address this problem from a safety perspective by looking at how much communication can be reduced before risking safety violations due to increased localization uncertainty. We propose a co-design approach that adjusts communication and control jointly according to a requirement on localization uncertainty and show that by satisfying this requirement, safety can also be achieved. The method leverages a data-driven model of how the uncertainty depends on both communication and control. The performance of the optimization problem is evaluated experimentally on an ABB Mobile YuMi® Research Platform robot in both simulations and on hardware, and the results indicate that communication can be reduced without compromising safety.

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.

Mobila nätverk, som ursprungligen utvecklades för mobiltelefoner, används i allt större utsträckning av autonoma robotar som kräver tillförlitlig uppkoppling föratt kunna utföra komplexa uppgifter. Framsteg inom artificiell intelligens möjliggör nya tillämpningar för obemannade drönare, uppkopplade autonoma fordon och industriella mobila robotar. Många robotar saknar dock den beräkningsförmåga som krävs för avancerade algoritmer och måste därför skicka sin sensordata till edge-servrar som algoritmerna också kan avlastas till. Detta medför nya utmaningar om många robotar använder nätverket samtidigt, eftersom överföring av stora mängder sensordata kan överbelasta nätverket. Ett allt viktigare forskningsområde är därmed samdesign av kommunikation och styrning för mobila robotar. Samdesign kan användes till att anpassa en robots beteende efter nätverkets förutsättningar för att bibehålla säker och effektiv drift med begränsade resurser.

Det övergripande målet med denna avhandling är att föreslå ett nytt ramverk för samdesign för uppkopplade robotar. Specifikt vill vi hitta en avvägning mellankommunikations- och styrprestanda samtidigt som vi garanterar säker navigation med sensorbaserad positionering. Detta är särskilt viktigt för industriella mobila robotar i till exempel lager och fabriker, där satellitbaserad positionering inte är ett alternativ. Det är även relevant för mobila robotar i andra miljöer där satellitsignaler kan blockeras, såsom gruvor, täta skogar och bland höghus i städer. Avhandlingen består av två huvudsakliga bidrag.

I det första bidraget designar vi styrningen av en mobil robot för att möjliggöra säker navigering bland hinder som endast kan upptäckas av externa sensorer. Eftersom roboten inte kan upptäcka dessa hinder med sina egna sensorer, måste den undvika dem baserat på sin uppskattade position i en karta av kända hinder. För att ta hänsyn till osäkerheter i positioneringen inför vi ett säkerhetsfilter baserat på en robust Control Barrier Function. Filtret designas för att undvika kollisioner givet en övre gräns för lokaliseringsfelet. Vi utför hårdvaruexperiment med en Mobile YuMi Research Platform för att bestämma den övre gränsen på lokaliseringsfelet, samt för att demonstrera hur den föreslagna metoden undviker kollisioner.

Därefter, i det andra bidraget, presenterar vi ett ramverk med samdesign för uppkopplade robotar som behöver överföra sensordata över nätverket för att få uppdaterad position från en lokaliseringsalgoritm som avlastats till en edge-server. Eftersom robotens rörelse också påverkar lokaliseringsnoggrannheten samdesignar vi kommunikation och styrning för att uppnå en önskad nivå av lokaliseringsosäkerhet. Genom att härleda ett osäkerhetskrav från ett säkerhetsvillkor baserat på robotens avstånd till kartlagda hinder kan vi verifiera att roboten navigerar säkert. Metoden valideras i hårdvaruexperiment som visar att en avvägning mellan kommunikationoch hastighet kan uppnås utan att kompromissa med säkerheten.

5G networks have the potential to provide external sensor data and offload computations for future industrial mobile robots. To enable these benefits while maintaining safety, we propose a modular architecture, including an onboard safety filter for the velocity control loop. The safety filter leverages robust control barrier functions to guarantee safety from collisions under bounded localization uncertainty. Initial experiments are performed to quantify the localization uncertainty and generate suitable bounds for the safety filter. We then derive the safety filter, and analyze its conservatism numerically. Finally, the method is demonstrated in experiments using an ABB Mobile YuMi® Research Platform robot.

For networked mobile robots, access to edge com-puting enables offloading computationally expensive processessuch as sensor-based localization, leading to relaxed hardwarerequirements and improved performance. Sending sensor dataover a network introduces other challenges, however. Largevolumes of data could cause packet loss or delays due tonetwork congestion, which can impact localization performanceand cause unsafe control decisions. To address this, in this paperwe propose a framework for safe navigation of mobile robotswith offloaded localization. The framework consists of two maincomponents that address localization imperfections in the formof localization uncertainty. The first is a communication-controlco-design module that adjusts communication frequency andnavigation speed according to an uncertainty requirement basedon proximity to obstacles. The second is an uncertainty-awarenavigation controller that adapts the reference control inputsbased on localization uncertainty. Both modules leverage learneduncertainty models to predict future localization uncertaintiesand adjust their decisions accordingly. The proposed frameworkis evaluated in experiments with an ABB Mobile YuMi® ResearchPlatform robot. First, we validate the prediction performance ofthe learned models on data collected in both simulation andhardware experiments. Second, we evaluate the achieved safetyand resource efficiency of the proposed modules in simulationexperiments. We compare using only one of the modules at a timeto compensate for localization uncertainty with the combineduse of both. The results indicate that the combined use of bothmodules results in safer and more efficient navigation.