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Robots are often used within the industry for automated tasks that are too dangerous, complex, or strenuous for humans, which leads to time and cost benefits. Robots can have an arm and a gripper to manipulate the world and sensors for eyes to be able to perceive the world. Human vision can be seen as an effortless task, but machine vision requires substantial computation in an attempt to be as effective as human vision. Visual object recognition is a common goal for machine vision, and it is often applied using deep learning and generic object detection. This thesis has a focus on robot guidance systems that include a robot with its gripper on the robot arm, a camera that acquires images of the world, boxes to detect in one or more layers, and the software that applies a generic object detection model to detect the boxes. Robot guidance systems’ performance is impacted by many variables such as different environmental, camera, object, and robot gripper aspects. A survey was constructed to receive feedback from professionals on what thresholds that can be defined for detection from the model to be counted as correct, with the aspect of the detection referring to an actual object that needs to be able to be picked up by a robot. This thesis has implemented precision, recall, average precision at a specific threshold, average precision at a range of thresholds, localization-recall-precision error, and a manually constructed counter based on survey results for the robot’s ability to pick up an object from the information provided by the detection, called pickability score. The metrics from this thesis are implemented within a tool intended for analyzing different models’ performance on varying datasets. The values of all the metrics for the applied dataset are presented in the results. The metrics are discussed with regards to what information they portray together with a robot guidance system. The conclusion is to see the metrics for what they are best at by themselves. Use the average precision metrics for the performance evaluation of the models, and the pickability scores with extended features for the robot gripper pickability evaluation.

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Master's thesis deals with the design, assembly, and testing of a camera system for localization of randomly placed and oriented objects on a conveyor belt with the purpose of guiding a robot on those objects. The theoretical part is focused on research in individual components making a camera system and on the field of 2D and 3D localization of objects. The practical part consists of two possible arrangements of the camera system, solution of the chosen arrangement, creating testing images, programming the algorithm for image processing, creating HMI, and testing the complete system.

The industry is moving towards a higher degree of automation and connectivity, where previously manual operations are being adapted for interconnected industrial robots. This thesis focuses specifically on the automation of tightening applications with pre-tightened bolts and collaborative robots. The use of 3D computer vision is investigated for direct localization of bolts, to allow for flexible assembly solutions. A localization algorithm based on 3D data is developed with the intention to create a lightweight software to be run on edge devices. A restrictive use of deep learning classification is therefore included, to enable product flexibility while minimizing the computational load. The cloud-to-edge and cluster-to-edge trade-offs for the chosen application are investigated to identify smart offloading possibilities to cloud or cluster resources. To reduce operational delay, image partitioning to sub-image processing is also evaluated, to more quickly start the operation with a first coordinate and to enable processing in parallel with robot movement. Four different hardware architectures are tested, consisting of two different Single Board Computers (SBC), a cluster of SBCs and a high-end computer as an emulated local cloud solution. All systems but the cluster is seen to perform without operational delay for the application. The optimal hardware architecture is therefore found to be a consumer grade SBC, being optimized on energy efficiency, cost and size. If only the variance in communication time can be minimized, the cluster shows potential to reduce the total calculation time without causing an operational delay. Smart offloading to deep learning optimized cloud resources or a cluster of interconnected robot stations is found to enable increasing complexity and robustness of the algorithm. The SBC is also found to be able to switch between an edge and a cluster setup, to either optimize on the time to start the operation or the total calculation time. This offers a high flexibility in industrial settings, where product changes can be handled without the need for a change in visual processing hardware, further enabling its integration in factory devices. / Industrin rör sig mot en högre grad av automatisering och uppkoppling, där tidigare manuella operationer anpassas för sammankopplade industriella robotar. Denna masteruppsats fokuserar specifikt på automatiseringen av åtdragningsapplikationer med förmonterade bultar och kollaborativa robotar. Användningen av 3D-datorseende undersöks för direkt lokalisering av bultar, för att möjliggöra flexibla monteringslösningar. En lokaliseringsalgoritm baserad på 3Ddata utvecklas med intentionen att skapa en lätt mjukvara för att köras på Edge-enheter. En restriktiv användning av djupinlärningsklassificering är därmed inkluderad, för att möjliggöra produktflexibilitet tillsammans med en minimering av den behövda beräkningskraften. Avvägningarna mellan edge- och moln- eller klusterberäkning för den valda applikationen undersöks för att identifiera smarta avlastningsmöjligheter till moln- eller klusterresurser. För att minska operationell fördröjning utvärderas även bildpartitionering, för att snabbare kunna starta operationen med en första koordinat och möjliggöra beräkningar parallellt med robotrörelser. Fyra olika hårdvaruarkitekturer testas, bestående av två olika enkortsdatorer, ett kluster av enkortsdatorer och en marknadsledande dator som en efterliknad lokal molnlösning. Alla system utom klustret visar sig prestera utan operationell fördröjning för applikationen. Den optimala hårdvaruarkitekturen visar sig därmed vara en konsumentklassad enkortsdator, optimerad på energieffektivitet, kostnad och storlek. Om endast variansen i kommunikationstid kan minskas visar klustret potential för att kunna reducera den totala beräkningstiden utan att skapa operationell fördröjning. Smart avlastning till djupinlärningsoptimerade molnresurser eller kluster av sammankopplade robotstationer visar sig möjliggöra ökad komplexitet och tillförlitlighet av algoritmen. Enkortsdatorn visar sig även kunna växla mellan en edge- och en klusterkonfiguration, för att antingen optimera för tiden att starta operationen eller för den totala beräkningstiden. Detta medför en hög flexibilitet i industriella sammanhang, där produktändringar kan hanteras utan behovet av hårdvaruförändringar för visuella beräkningar, vilket ytterligare möjliggör dess integrering i fabriksenheter.