Wireless sensor and actor networks are used in situations where interaction is required between a network and the environment in which the network is deployed. This research studies the functioning of a single mobile actor deployed in a sparsely connected network. When deployed in a sparsely connected network, an actor has to do more than acting. It has to perform the additional duties of an event collector - collecting events from the naturally occurring clusters - so that it can fulfill its primary obligation as an actor. The path taken by a mobile actor node is generated by a mobility model. The existing random mobility models are non-intelligent mobility models. While they may bring about a chance meeting between an actor and an event, there is no guarantee that these meetings will actually happen. This motivates the development of intelligent mobility models for the actor node, which will generate paths that are reflective of the network in which the actor is deployed. In this thesis, intelligent mobility models for the actor node were developed using the inherent clustering information of a sparsely connected network. These models were applied to an actor node in networks of varying sparseness and the following conclusions were reached: (i) Existing random mobility models are unsuitable for an actor in a sparsely connected network. (ii) High probability of events can be sensed when a sparsely connected network is used. (iii) 100% event detection by the actor node is possible at higher speeds. (iv) When the single actor functioned both as an event collector and as an actor, the number of events acted upon by the actor was very close to the number of events acted upon by an actor in a fully connected network. (v) The Correlation Theory developed in this research suggests using a combination of the intelligent mobility models to obtain the best performance results under all circumstances. (vi) Early detection of events can be supported where it is required. All of the above conclusions justify the deployment of a single actor and a sparsely connected network, either individually or as a combination.

Incremental smoothing and mapping (iSAM) is presented, a novel approach to the simultaneous localization and mapping (SLAM) problem. SLAM is the problem of estimating an observer's position from local measurements only, while creating a consistent map of the environment. The problem is difficult because even very small errors in the local measurements accumulate over time and lead to large global errors. iSAM provides an exact and efficient solution to the SLAM estimation problem while also addressing data association. For the estimation problem, iSAM provides an exact solution by performing smoothing, which keeps all previous poses as part of the estimation problem, and therefore avoids linearization errors. iSAM uses methods from sparse linear algebra to provide an efficient incremental solution. In particular, iSAM deploys a direct equation solver based on QR matrix factorization of the naturally sparse smoothing information matrix. Instead of refactoring the matrix whenever new measurements arrive, only the entries of the factor matrix that actually change are calculated. iSAM is efficient even for robot trajectories with many loops as it performs periodic variable reordering to avoid unnecessary fill-in in the factor matrix. For the data association problem, I present state of the art data association techniques in the context of iSAM and present an efficient algorithm to obtain the necessary estimation uncertainties in real-time based on the factored information matrix. I systematically evaluate the components of iSAM as well as the overall algorithm using various simulated and real-world data sets.

This thesis addresses the problems of design and control of small cooperative unmanned autonomous quadrotor aerial vehicles. A new approach is proposed, for the modeling of the system’s dynamics using linearized Piecewise AffineModels. The Piecewise Affine dynamic–models cover a large part of the quadrotor’s flight envelope while also taking into account the additive effects of environmental disturbances. The effects of aerodynamic forces and moments were also examined. A small quadrotor is designed and developed that emphasizes in the areas of increased on–board computational capabilities, state estimation and modular connectivity. Based on the translational and rotational system’s dynamics: a) a switching model predictive controller, b) an explicitly solved constrained finite time optimal control strategy, and c) a cascade control scheme comprised of classical Proportional Integral Derivative control scheme augmented with angular acceleration feedback, were designed and experimentally tested in order to achieve trajectory tracking under the presence of wind–gusts. The efficiency of the proposed control methods was verified through extended experimental studies. The final quadrotor design utilizes a powerful control unit, a sensor system that provides state estimation based on inertial sensors, ultrasound sonars, GPS and vision chips, and an efficient actuating system. The research effort extended in the field of unmanned aerial vehicles cooperation. Cooperation strategies were proposed in order to address the problems of: a) Forest Fire Monitoring and b) Unknown Area Exploration and Target Acquisition. The Forest FireMonitoring algorithm is formulated based on consensus systems theory formulated as a spatiotemporal rendezvous problem in between the quadrotors. The Area Exploration and Target Acquisition algorithm is formulated based on market–based approaches. / Η συγκεκριμένη διατριβή καταπιάνεται με τα προβλήματα της σχεδίασης και ελέγχου μικρού μεγέθους συνεργαζόμενων μη επανδρωμένων αεροσκαφών με έμφαση στα συστήματα Κάθετης Απογείωσης και Προσγείωσης και ιδιαίτερα στη συστήματα τύπου Quadrotor. Μια νέα προσέγγιση για την μοντελοποίηση της δυναμικής του συστήματος η οποία βασίζεται στη θεωρία των Piecewise Affine συστημάτων προτείνεται. Η μοντελοποίηση με βάση τη θεωρία των Piecewise Affine συστημάτων καλύπτει ένα μεγάλο μέρος του φακέλου πτήσης του αεροσκάφους καθότι συνυπολογίζει μέρος της μη-γραμμικότητας του συστήματος ενώ παράλληλα δίνει τη δυνατότητα να χρησιμοποιηθούν τα ιδιαίτερα ανεπτυγμένα εργαλεία του γραμμικού ελέγχου. Αναπτύσσεται νέα πειραματική πλατφόρμα αεροσκάφους τύπου quadrotor η οποία χαρακτηρίζεται από ιδιαίτερες ικανότητες υπολογιστικής ισχύος, αυτόνομη εκτίμηση κατάστασης, πολλαπλή συνδεσιμότητα και αποδοτικό σύστημα πρόωσης. Η τελική πλατφόρμα quadrotor ελικοπτέρου UPATcopter ενσωματώνει μικρουπολογιστικό σύστημα υψηλών δυνατοτήτων, ειδικά συστήματα εκτίμησης κατάστασης τόσο σε εσωτερικούς όσο και σε εξωτερικούς χώρους μέρος των οποίων αναπτύχθηκε στα πλαίσια της διατριβής και αποδοτικό υποσύστημα πρόωσης. Τρεις διαφορετικοί νόμοι ελέγχου αναπτύχθηκαν και δοκιμάστηκαν πειραματικά. Αρχικά δοκιμάσθηκε ένας Constrained Finite Time Optimal Controller, ο οποίος υπολογίζεται πολύ-παραμετρικά και συνυπολογίζει την επίδραση των περιορισμών εισόδου και κατάστασης. Ο συγκεκριμένος ελεγκτής υπολογίσθηκε με βάση μια οικογένεια Piecewise Affine αναπαραστάσεων του υποσυστήματος προσανατολισμού και δοκιμάσθηκε επιτυγχάνοντας αποδοτικό έλεγχο του προσανατολισμού του σκάφους. Ακολούθως δοκιμάσθηκε ένας Switching Model Predictive Control βασισμένος στην Piecewise Affine μοντελοποίηση του συστήματος ο οποίος επίσης συνυπολογίζει την επίδραση των περιορισμών του συστήματος και του ρόλου των διαταραχών. Με τη χρήση αυτού του ελεγκτή επιτεύχθηκε έλεγχος προσανατολισμού και θέσης του αεροσκάφους τόσο σε άπνοια όσο και υπό την επίδραση ισχυρών διαταραχών ανέμου. Επιπρόσθετα, δοκιμάσθηκε ελεγκτής βασισμένος στη θεωρία PID ελέγχου επαυξημένος με ανάδραση γωνιακής επιτάχυνσης του συστήματος. Τέλος, η έρευνα επεκτάθηκε και στις στρατηγικές συνεργασίας μη επανδρωμένων αεροσκαφών προτείνοντας δύο αλγόριθμους. Συγκεκριμένα προτάθηκε αλγόριθμος για την αντιμετώπιση των προβλημάτων επιθεώρησης δασικής πυρκαγιάς και αλγόριθμος εξερεύνησης μιας άγνωστης περιοχής από ομάδα ετερογενών αεροσκαφών.

Thesis (MScEng)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: Simultaneous localization and mapping (SLAM) is vital for autonomous robot navigation. The robot must build a map of its environment while tracking its own motion through that map. Although many solutions to this intricate problem have been proposed, one of the most prominent issues that still needs to be resolved is to accurately measure and track landmarks over time. In this thesis we investigate the use of stereo vision for this purpose. In order to find landmarks in images we explore the use of two feature detectors: the scale-invariant feature transform (SIFT) and speeded-up robust features (SURF). Both these algorithms find salient points in images and calculate a descriptor for each point that is invariant to scale, rotation and illumination. By using the descriptors we match these image features between stereo images and use the geometry of the system to calculate a set of 3D landmark measurements. A Taylor approximation of this transformation is used to derive a Gaussian noise model for the measurements. The measured landmarks are matched to landmarks in a map to find correspondences. We find that this process often incorrectly matches ambiguous landmarks. To find these mismatches we develop a novel outlier detection scheme based on the random sample consensus (RANSAC) framework. We use a similarity transformation for the RANSAC model and derive a probabilistic consensus measure that takes the uncertainties of landmark locations into account. Through simulation and practical tests we find that this method is a significant improvement on the standard approach of using the fundamental matrix. With accurately identified landmarks we are able to perform SLAM. We investigate the use of three popular SLAM algorithms: EKF SLAM, FastSLAM and FastSLAM 2. EKF SLAM uses a Gaussian distribution to describe the systems states and linearizes the motion and measurement equations with Taylor approximations. The two FastSLAM algorithms are based on the Rao-Blackwellized particle filter that uses particles to describe the robot states, and EKFs to estimate the landmark states. FastSLAM 2 uses a refinement process to decrease the size of the proposal distribution and in doing so decreases the number of particles needed for accurate SLAM. We test the three SLAM algorithms extensively in a simulation environment and find that all three are capable of very accurate results under the right circumstances. EKF SLAM displays extreme sensitivity to landmark mismatches. FastSLAM, on the other hand, is considerably more robust against landmark mismatches but is unable to describe the six-dimensional state vector required for 3D SLAM. FastSLAM 2 offers a good compromise between efficiency and accuracy, and performs well overall. In order to evaluate the complete system we test it with real world data. We find that our outlier detection algorithm is very effective and greatly increases the accuracy of the SLAM systems. We compare results obtained by all three SLAM systems, with both feature detection algorithms, against DGPS ground truth data and achieve accuracies comparable to other state-of-the-art systems. From our results we conclude that stereo vision is viable as a sensor for SLAM. / AFRIKAANSE OPSOMMING: Gelyktydige lokalisering en kartering (simultaneous localization and mapping, SLAM) is ’n noodsaaklike proses in outomatiese robot-navigasie. Die robot moet ’n kaart bou van sy omgewing en tegelykertyd sy eie beweging deur die kaart bepaal. Alhoewel daar baie oplossings vir hierdie ingewikkelde probleem bestaan, moet een belangrike saak nog opgelos word, naamlik om landmerke met verloop van tyd akkuraat op te spoor en te meet. In hierdie tesis ondersoek ons die moontlikheid om stereo-visie vir hierdie doel te gebruik. Ons ondersoek die gebruik van twee beeldkenmerk-onttrekkers: scale-invariant feature transform (SIFT) en speeded-up robust features (SURF). Altwee algoritmes vind toepaslike punte in beelde en bereken ’n beskrywer vir elke punt wat onveranderlik is ten opsigte van skaal, rotasie en beligting. Deur die beskrywer te gebruik, kan ons ooreenstemmende beeldkenmerke soek en die geometrie van die stelsel gebruik om ’n stel driedimensionele landmerkmetings te bereken. Ons gebruik ’n Taylor- benadering van hierdie transformasie om ’n Gaussiese ruis-model vir die metings te herlei. Die gemete landmerke se beskrywers word dan vergelyk met dié van landmerke in ’n kaart om ooreenkomste te vind. Hierdie proses maak egter dikwels foute. Om die foutiewe ooreenkomste op te spoor het ons ’n nuwe uitskieterherkenningsalgoritme ontwikkel wat gebaseer is op die RANSAC-raamwerk. Ons gebruik ’n gelykvormigheidstransformasie vir die RANSAC-model en lei ’n konsensusmate af wat die onsekerhede van die ligging van landmerke in ag neem. Met simulasie en praktiese toetse stel ons vas dat die metode ’n beduidende verbetering op die standaardprosedure, waar die fundamentele matriks gebruik word, is. Met ons akkuraat geïdentifiseerde landmerke kan ons dan SLAM uitvoer. Ons ondersoek die gebruik van drie SLAM-algoritmes: EKF SLAM, FastSLAM en FastSLAM 2. EKF SLAM gebruik ’n Gaussiese verspreiding om die stelseltoestande te beskryf en Taylor-benaderings om die bewegings- en meetvergelykings te lineariseer. Die twee FastSLAM-algoritmes is gebaseer op die Rao-Blackwell partikelfilter wat partikels gebruik om robottoestande te beskryf en EKF’s om die landmerktoestande af te skat. FastSLAM 2 gebruik ’n verfyningsproses om die grootte van die voorstelverspreiding te verminder en dus die aantal partikels wat vir akkurate SLAM benodig word, te verminder. Ons toets die drie SLAM-algoritmes deeglik in ’n simulasie-omgewing en vind dat al drie onder die regte omstandighede akkurate resultate kan behaal. EKF SLAM is egter baie sensitief vir foutiewe landmerkooreenkomste. FastSLAM is meer bestand daarteen, maar kan nie die sesdimensionele verspreiding wat vir 3D SLAM vereis word, beskryf nie. FastSLAM 2 bied ’n goeie kompromie tussen effektiwiteit en akkuraatheid, en presteer oor die algemeen goed. Ons toets die hele stelsel met werklike data om dit te evalueer, en vind dat ons uitskieterherkenningsalgoritme baie effektief is en die akkuraatheid van die SLAM-stelsels beduidend verbeter. Ons vergelyk resultate van die drie SLAM-stelsels met onafhanklike DGPS-data, wat as korrek beskou kan word, en behaal akkuraatheid wat vergelykbaar is met ander toonaangewende stelsels. Ons resultate lei tot die gevolgtrekking dat stereo-visie ’n lewensvatbare sensor vir SLAM is.

The fourth industrial revolution, also known as Industry 4.0, is based on digital industrial technology developments. The purpose of Industry 4.0 is to transform industrial manufacturing through digitalization and new technologies. The introduction of Industry 4.0 has led companies to consider digitalization as essential for company strategies. For decades, companies have aspired to increase performance by using advanced production practices, such as different operation methods and advanced manufacturing technologies. The intention of introducing these practices is to advance companies towards high performance by implementing advanced techniques. One way of utilizing the potential of new technologies is by an adoption of the concept Industry 4.0. This research studies the impact of Industry 4.0 on companies current manufacturing strategies and operational performance. The study is carried out by conducting case studies on two companies with connections to the concept of Industry 4.0 and implementations of Industry 4.0 technologies such as big data, Internet of things, the Cloud concepts, simulations, and autonomous robots. Through theoretical and comparative analysis and discussion, the study found that an adoption to Industry 4.0 as a concept is a long and stepwise process. Successful Industry 4.0 adaption requires integration of Industry 4.0 with current manufacturing strategies. Implementation of certain Industry 4.0 technologies can have an impact on operational performance if these are integrated with advancements of manufacturing practices. The practices of manufacturing strategies are significantly associated with operational performance. In other cases, the impact of implementing new technologies linger, and is not directly noticeable. With this in consideration, the value of these new technologies should not be limited to operational measurements such as financial measures.

The goal of the research activities presented in this thesis is the design of intelligent behaviours for a complex robotic system, which is composed of a swarm of autonomous units. Inspired by the organisational skills of social insects, we are particularly interested in the study of collective behaviours based on self-organisation.<p><p>The problem of designing self-organising behaviours for a swarm of robots is tackled resorting to artificial evolution, which proceeds in a bottom-up direction by first defining the controllers at the individual level and then testing their effect at the collective level. In this way, it is possible to bypass the difficulties encountered in the decomposition of the global behaviour into individual ones, and the further encoding of the individual behaviours into the controllers' rules. In the experiments presented in this thesis, we show that this approach is viable, as it produces efficient individual controllers and robust self-organising behaviours. To the best of our knowledge, our experiments are the only example of evolved self-organising behaviours that are successfully tested on a physical robotic platform.<p><p>Besides the engineering value, the evolution of self-organising behaviours for a swarm of robots also provides a mean for the understanding of those biological processes that were a fundamental source of inspiration in the first place. In this perspective, the experiments presented in this thesis can be considered an interesting instance of a synthetic approach to the study of collective intelligence and, more in general, of Cognitive Science.<p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Dans cette thèse, nous proposons d'intégrer la notion d'habitude comportementale au sein d'une architecture de contrôle robotique, et d'étudier son interaction avec les mécanismes générant le comportement planifié. Les architectures de contrôle robotiques permettent à ce dernier d'être utilisé efficacement dans le monde réel et au robot de rester réactif aux changements dans son environnement, tout en étant capable de prendre des décisions pour accomplir des buts à long terme (Kortenkamp et Simmons, 2008). Or, ces architectures sont rarement dotées de capacités d'apprentissage leur permettant d'intégrer les expériences précédentes du robot. En neurosciences et en psychologie, l'étude des différents types d'apprentissage montre pour que ces derniers sont une capacité essentielle pour adapter le comportement des mammifères à des contextes changeants, mais également pour exploiter au mieux les contextes stables (Dickinson, 1985). Ces apprentissages sont modélisés par des algorithmes d'apprentissage par renforcement direct et indirect (Sutton et Barto, 1998), combinés pour exploiter leurs propriétés au mieux en fonction du contexte (Daw et al., 2005). Nous montrons que l'architecture proposée, qui s'inspire de ces modèles du comportement, améliore la robustesse de la performance lors d'un changement de contexte dans une tâche simulée. Si aucune des méthodes de combinaison évaluées ne se démarque des autres, elles permettent d'identifier les contraintes sur le processus de planification. Enfin, l'extension de l'étude de notre architecture à deux tâches (dont l'une sur robot réel) confirme que la combinaison permet l'amélioration de l'apprentissage du robot. / In this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior.

The live production sector of the poultry industry has a growing interest in robotics. Robotics have the possibility to monitor environmental conditions, assess bird welfare, and reduce labor for farm workers and owners. However, interactions of poultry with robotic systems in commercial poultry house environments is largely unknown. Therefore, the goal of this research was to assess the effect of ground and aerial robots on bird stress using avoidance distance (AD) and fleeing speed (FS) as indirect indicators. A low-cost, autonomous robot was also developed to aid in collecting data on environmental conditions in commercial broiler houses. AD and FS were measured for multiple breeds (broilers, brown hens, and white hens) at different bird ages. Poultry-robot AD was greater than poultry-human AD for both broilers and laying hens, indicating that birds tended to avoid the ground robot more than humans. However, birds did become accustomed to the ground robot as reflected by decreasing AD and FS over the trial periods. Aerial drones operated in a commercial broiler house were found to induce a larger AD and higher FS than a moveable sensor package attached to a fixed, overhead rail system. No significant difference was found in the performance of the low-cost, autonomous robot when tested on different substrates (hard tile and litter). However, some differences were found when the robot was operated at different speeds. Results from these studies have provided useful insight into the operation of ground and aerial robots in commercial poultry settings.

This project proposes an adaptation of the dRRT* algorithm, a samplingbased multi-robot planner, for real-time industrial automation scenarios. The main objectives include optimizing computationally expensive sections of the algorithm, solving partially specified multi-robot problems, and evaluating the performance of the resulting method in various industry-like scenarios. The proposed algorithm, called Fast-dRRT*, aims to achieve highquality collision-free paths within strict time constraints. To accomplish this, the project introduces modifications to the dRRT* algorithm, such as the utilization of pre-computed swept volumes for efficient collision detection. The performance of four multi-robot planners, namely dRRT, ao-dRRT, dRRT*, and Fast-dRRT*, is evaluated through experiments on toy scenarios and industrial use cases. The results show that the proposed Fast-dRRT* algorithm outperforms the other planners in terms of finding solutions within the given time limits. It demonstrates improved efficiency, speed, and resilience to increased search spaces and the number of robots. The algorithm’s performance is further evaluated in real-world scenarios, including automotive assembly lines and warehouse automation, where it consistently outperforms dRRT* in terms of search speed and total planning time. Additionally, the algorithm successfully handles partially specified multi-robot problems, optimizing the overall movements’ cost. The study concludes that Fast-dRRT* is a promising option for real-time planning in industrial automation, offering reduced computation time and feasible solutions to complex multi-robot motion planning problems. / Detta projekt föreslår en anpassning av dRRT*-algoritmen, en samplingsbaserad multirobotplanerare, för realtidsscenarier inom industriell automation.. De huvudsakliga målen inkluderar optimering av beräkningskrävande delar av algoritmen, lösning av delvis specificerade multirobotproblem och utvärdering av den resulterande metodens prestanda i olika industriliknande scenarier. Den föreslagna algoritmen, kallad Fast-dRRT*, syftar till att uppnå högkvalitativa kollisionsfria banor inom strikta tidsbegränsningar. För att uppnå detta introducerar projektet modifieringar av dRRT*-algoritmen, såsom användning av förberäknade svepta volymer för effektiv kollisionsdetektering. Prestandan hos fyra multirobotplanerare, nämligen dRRT, ao-dRRT, dRRT* och Fast-dRRT*, utvärderas genom experiment på leksaksscenarier och industriella användningsfall. Resultaten visar att den föreslagna Fast-dRRT*- algoritmen överträffar de andra planerarna när det gäller att hitta lösningar inom de givna tidsgränserna. Den visar förbättrad effektivitet, hastighet och motståndskraft mot ökade sökutrymmen och antalet robotar. Algoritmens prestanda utvärderas vidare i verkliga scenarier, inklusive monteringslinjer för bilar och lagerautomation, där den konsekvent överträffar dRRT* när det gäller sökhastighet och total planeringstid. Dessutom hanterar algoritmen framgångsrikt delvis specificerade multirobotproblem och optimerar den totala rörelsekostnaden. Studien drar slutsatsen att Fast-dRRT* är ett lovande alternativ för realtidsplanering inom industriell automation, eftersom den erbjuder kortare beräkningstid och genomförbara lösningar på komplexa problem med rörelseplanering för multirobotar.

Autonomous robots have the potential to accomplisha wide variety of assignments. For this to work in reality, therobots need to be able to perform specific tasks while safety forboth them and their environment is ensured. Signal temporallogic (STL) was used to define timed tasks for the agents toperform and control barrier functions (CBFs) were used to designa controller for their movements. In this paper, a set of STL taskswere considered, which two robots were instructed to satisfy in asimulation of a warehouse environment. The two agents startednext to each other, then the set of tasks instructed them to move totwo separate areas, then meet up again and move in a formationback towards their starting area. Control barrier functions wereemployed to ensure the satisfaction of the set of STL tasks.The agents designed their actions towards satisfying the giventasks without considering a safety distance to the other robot atfirst. To later ensure safety, a collision avoidance mechanism wasintroduced. The scenario without collision avoidance proved moreeffective paths for the agents. They moved to satisfy the tasks withless disturbance than the scenario where collision avoidance wasconsidered. However, the scenario with the collision avoidancemechanism proved successful and the agents satisfied their taskswithout colliding with each other. / Autonoma robotar har potential att utföra en stor mängd olika uppgifter. För att detta ska fungera i verkligheten, behöver robotarna kunna genomföra specifika uppgifter medans både deras egen och omgivningens säkerhet är säkerställd. Signal temporal logic (STL) användes för att definiera tidsinställda uppgifter åt robotarna att utföra och control barrier functions (CBFs) användes för att designa en controller för deras rörelser. I den här rapporten betraktades en uppsättning av STL-uppgifter, vilka två robotar instruerades att uppfylla i en simulering av en lagermiljö. De två robotarna startade bredvid varandra, sen instruerade STL-uppgifterna dem att röra sig till två separata områden, sen mötas upp igen och röra sig i formation tillbaka mot sitt startområde. Control barrier functions användes för att garantera uppfyllandet av STL-uppgifterna. Robotarna anpassade sina rörelser till att uppfylla de givna uppgifterna, först utan hänsyn till någon säkerhetsmarginal till den andra roboten. För att senare garantera säkerhet introducerades en extra mekanism för att undvika kollision. Scenariot utan att undvika kollision visade på effektivare rörelsebanor hos robotarna. De rörde sig mot att uppfylla uppgifterna med färre störningar än scenariot då kollision aktivt undveks. Scenariot med mekanismen för att dock framgångsrikt och robotarna e sina uppgifter utan att kollidera med varandra. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm