Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

The increasing integration of robotics into dynamic, safety-critical environments has necessitated the development of frameworks that enable seamless collaboration between humans and multi-robot systems. This thesis investigates the distributed planning and control of multi-robot systems under human presence, focusing on explicit and implicit Human-(multi)Robot Interaction (HRI) across both low-level control and high-level planning tasks. In this context, explicit means interaction through direct commands and implicit means interaction through indirect effects due to shared spaces. The contributions of this work aim to bridge human intuition and robotic precision, ensuring safety and task efficiency while addressing real-world challenges in domains such as precision agriculture and smart construction.

The first part of the thesis addresses explicit low-level human-in-the-loop (HIL) control by developing distributed barrier-function-based approaches that incorporate multiple safety constraints in multi-robot systems. This framework allows human input or overrides while ensuring inter-agent collision avoidance, connectivity maintenance, and adherence to spatial limits. A novel method transforms multiple safety constraints into a collective constraint using a smoothly approximated minimum function, enabling efficient optimization. Experiments demonstrate the integration of HIL scenarios in collaborative multi-robot tasks, highlighting robustness in maintaining system safety despite human interventions. Additionally, a robust Control Barrier Function (CBF)-based visual servoing framework for mobile manipulators is introduced, combining eye-in-hand and eye-to-hand setups. This approach ensures precise object tracking and placement under human supervision, crucial for safety-critical applications such as robotic assembly and construction.

The second part focuses on explicit high-level HIL planning, where Signal Temporal Logic (STL) and Linear Temporal Logic (LTL) are used to define spatiotemporal tasks for multi-robot systems. These tasks are executed using Model Predictive Control (MPC), enabling real-time, collision-free trajectory planning while dynamically incorporating human commands. A task allocation protocol ensures adaptability and safe integration of human directives in scenarios involving complex tasks, such as collaborative harvesting and pruning in precision agriculture. Laboratory and field experiments conducted in vineyards validate the applicability of this framework in dynamic environments. Further contributions include the development of methods for recurring task coordination and synchronization in large-scale multi-robot teams. These methods leverage recurring LTL formulations to address computational challenges associated with scaling, enabling robust synchronization and adaptability in real-world settings with up to ninety robots.

The final part of the thesis explores implicit low-level HIL by focusing on human motion prediction to enable safe and intuitive interactions between humans and robots. A Koopman-based Inverse Optimal Control (IOC) framework is introduced to estimate unknown dynamics and costs for human motion prediction. By reformulating human-related dynamics as bilinear systems, this method provides a robust and tractable alternative to traditional nonlinear IOC approaches. This framework is validated through theoretical analysis, simulation studies, and experiments, demonstrating its efficacy in enhancing human-aware navigation and task execution in multi-robot systems.

The overarching vision of this thesis is to develop scalable, human-centric frameworks that enable robots to dynamically adapt to human inputs while ensuring safety and operational efficiency. By addressing explicit and implicit HRI across low-level control and high-level planning, the work presented here lays the foundation for the seamless integration of multi-robot systems into dynamic, real-world environments, advancing the state of the art in human-robot collaboration.

Den ökande integrationen av robotteknik i dynamiska, säkerhetskritiska miljöer har krävt utveckling av ramverk som möjliggör sömlöst samarbete mellan människor och multirobotsystem. Denna avhandling undersöker distribuerad planering och styrning av multirobotsystem vid mänsklig närvaro, med fokus på explicit och implicit Human-(multi)Robot Interaction (HRI) vid både lågnivåkontroll och även högnivåplaneringsuppgifter. I det här sammanhanget defineras explicit interaktion som interaktioner genom direkta kommandon och implicit interaktion som interaktioner genom indirekta effekter på grund av delade utrymmen. Målet med detta arbete är att integrera mänsklig intuitionen och robotprecision samt säkerställa säkerhet och effektivitet av arbetet, med fokus på verkliga utmaningar inom områden som precisionsjordbruk och smart konstruktion.

Den första delen av avhandlingen behandlar explicit HIL-kontroll (human-in-the-loop) på låg nivå genom att utveckla distribuerade barriärfunktionsbaserade metoder som inför flera säkerhetsbegränsningar i multirobotsystem. Detta ramverk tillåter mänsklig input eller överskridningar samtidigt som det säkerställer kollisionsundvikande mellan agenter, upprätthållande av konnektivitet och efterlevnad av spatiala gränser. En ny metod omvandlar flera säkerhetsbegränsningar till en kollektiv begränsning med hjälp av en smidigt approximerad minimifunktion, vilket möjliggör effektiv optimering. Experiment demonstrerar integrationen av HIL-scenarier i kollaborativa multirobotuppgifter, vilket belyser robustheten i att upprätthålla systemsäkerhet trots mänskliga ingripanden. Dessutom introduceras ett robust CBF-baserat (Control Barrier Function) ramverk för visuell servoing för mobila manipulatorer, som kombinerar ögon-i-hand och öga-till-hand-uppsättningar. Detta tillvägagångssätt säkerställer exakt objektspårning och placering vid mänsklig övervakning, vilket är avgörande för applikationer i säkerhetskritiska miljöer som robotmontering och konstruktion.

Den andra delen av avhandlingen fokuserar på explicit HIL-planering på hög nivå, där Signal Temporal Logic (STL) och Linear Temporal Logic (LTL) används för att definiera spatiotemporala uppgifter för multirobotsystem. Dessa uppgifter utförs med hjälp av MPC (Model Predictive Control), vilket möjliggör kollisionsfri banplanering i realtid samtidigt som mänskliga kommandon införs dynamiskt. Ett protokoll för uppgiftstilldelning säkerställer anpassningsförmåga och säker integrering av mänskliga direktiv i scenarier som involverar komplexa uppgifter, såsom skörd och beskärning i precisionsjordbruk. Laboratorie- och fältexperiment som utförts i vingårdar validerar den praktiska tillämpbarheten av detta ramverk i dynamiska miljöer. Ytterligare bidrag inkluderar utvecklingen av metoder för återkommande uppgiftskoordinering och synkronisering i storskaliga multirobotteam. Dessa metoder utnyttjar upprepande LTL-formuleringar för att hantera beräkningsutmaningar i samband med skalning, vilket möjliggör robust synkronisering och anpassningsförmåga i verkliga miljöer med upp till nittio robotar.

Den sista delen av avhandlingen utforskar implicit HIL på låg nivå genom att fokusera på förutsägbarhet av mänsklig rörelse för att möjliggöra säkra och intuitiva interaktioner mellan människor och robotar. Ett Koopman-baserat IOC-ramverk (Inverse Optimal Control) introduceras för att uppskatta okänd dynamik och kostnader för förutsägbarhet av mänskliga rörelser. Genom att omformulera människorelaterad dynamik som bilinjära system ger denna metod ett robust och hanterbart alternativ till traditionella olinjära IOC-metoder. Detta ramverk valideras genom teoretisk analys, simuleringsstudier och experiment, och visar att ramverket är effektivt för att förbättra navigering och uppgiftsutförande vid mänsklig närvaro i multirobotsystem.

Den övergripande visionen för denna avhandling är att utveckla skalbara och människocentrerade ramverk som gör det möjligt för robotar att anpassa sig till mänskliga ingångar dynamiskt samtidigt som säkerhet och operativ effektivitet säkerställs. Genom att behandla både explicit och implicit HRI med lågnivåkontroll och högnivåplanering, lägger detta arbete grunden för en sömlös integration av multirobotsystem i dynamiska, verkliga miljöer, vilket främjar den senaste tekniken inom samarbete mellan människa och robot.

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottomup approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.

We propose a framework enabling mobile manipulators to reliably complete pick-and-place tasks for assembling structures from construction blocks. The picking uses an eye-in-hand visual servoing controller for object tracking with Control Barrier Functions (CBFs) to ensure fiducial markers in the blocks remain visible. An additional robot with an eye-to-hand setup ensures precise placement, critical for structural stability. We integrate human-in-the-loop capabilities for flexibility and fault correction and analyze robustness to camera pose errors, proposing adapted barrier functions to handle them. Lastly, experiments validate the framework on 6-DoF mobile arms.

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

In this work, we present a coordination strategy tailored for scenarios involving multiple agents and tasks. We devise a range of tasks using signal temporal logic (STL), each earmarked for specific agents. These tasks are then imposed through control barrier function (CBF) constraints to ensure completion. To extend existing methodologies, our framework adeptly manages interactions among multiple agents. This extension is facilitated by leveraging nonlinear model predictive control (NMPC) to compute trajectories that avoid collisions. An integral aspect of our approach is the integration of a human-in-the-loop (HIL) model. This model enables real-time integration of human directives into the coordination process. A novel task allocation protocol is embedded within the frame-work to guide this process. We substantiate our methodology through a series of experiments, which corroborate the viability and relevance of our algorithms.

In this work, we propose a distributed control scheme for multi-robot systems in the presence of multiple constraints using control barrier functions. The proposed scheme expands previous work where only one single constraint can be handled. Here we show how to transform multiple constraints to a collective one using a smoothly approximated minimum function. Additionally, human-in-the-loop control is also incorporated seamlessly to our control design, both through the nominal control in the optimization objective as well as a safety condition in the constraints. Possible failure regions are identified and a suitable fix is proposed. Two types of human-in- the-loop scenarios are tested on real multi-robot systems with multiple constraints, including collision avoidance, connectivity maintenance, and arena range limits.

Optical phenomena in the upper atmosphere, such as northern lights, airglow, noctilucent clouds and thunderstorm-related transient luminous phenomena reveal the complex processes coupling different layers of the atmosphere and the near earth space. Bad weather and lighting conditions, as well as geographical constraints, limit the possibilities of ground based imaging. Therefore, an autonomous high altitude long endurance (HALE) fixed-wing unmanned aerial vehicle (UAV) is proposed for atmospheric imaging, as a joint student-driven research project between the Aeronautics and Vehicle Engineering- and the Space and Plasma Physics departments at KTH Royal Institute of Technology. The Autonomous Light Platform for High Altitude atmospheric imaging (ALPHA) is specifically designed for operations in the environmentally harsh conditions found in Arctic nighttime. This paper presents the conceptual design phase of the aircraft, as well as the initial manufacturing and flight testing methodology of a half-scale prototype.