No / This chapter describes a test-generation approach that takes as input a model S of the expected behavior of a robotic system and seeds faults into S, leading to a set of mutants of S. Given a mutant M of S, we check whether M is a valid implementation of S, and, if it is not, we find a test case that demonstrates this: a test case that reveals the seeded fault. In order to automate this approach, we used the Wodel tool to seed faults and a combination of two tools, RoboTool and FDR, to generate tests that detect the seeded faults. The result is an overall test-generation technique that can be automated and that derives test cases that are guaranteed to find certain faults. / EPSRC grant EP/R025134/2 RoboTest: Systematic Model-Based Testing and Simulation of Mobile Autonomous Robots, Spanish MINECO-FEDER grant FAME RTI2018-093608-B-C31 and the Comunidad de Madrid project FORTE-CM S2018/TCS-4314.

This thesis develops and describes methods for real-time tracking, segmentation and 3-dimensional (3D) model acquisition, in the context of developing games for stroke patients that are rehabilitating at home. Real-time tracking and reconstruction of a stroke patient's feet, hands and the control objects that they are touching can enable not only the graphical visualization of the virtual avatar in the rehabilitation games, but also permits measurement of the patient's performs. Depth or combined colour and depth imagery from a Kinect sensor is used as input data. The 3D signed distance function (SDF) is used as implicit shape representation, and a series of probabilistic graphical models are developed for the problem of model-based 3D tracking, simultaneous 3D tracking and reconstruction and 3D tracking of multiple objects with identical appearance. The work is based on the assumption that the observed imagery is generated jointly by the pose(s) and the shape(s). The depth of each pixel is randomly and independently sampled from the likelihood of the pose(s) and the shape(s). The pose(s) tracking and 3D shape reconstruction problems are then cast as the maximum likelihood (ML) or maximum a posterior (MAP) estimate of the pose(s) or 3D shape. This methodology first leads to a novel probabilistic model for tracking rigid 3D objects with only depth data. For a known 3D shape, optimization aims to find the optimal pose that back projects all object region pixels onto the zero level set of the 3D shape, thus effectively maximising the likelihood of the pose. The method is extended to consider colour information for more robust tracking in the presence of outliers and occlusions. Initialised with a coarse 3D model, the extended method is also able to simultaneously reconstruct and track an unknown 3D object in real time. Finally, the concept of `shape union' is introduced to solve the problem of tracking multiple 3D objects with identical appearance. This is formulated as the minimum value of all SDFs in camera coordinates, which (i) leads to a per-pixel soft membership weight for each object thus providing an elegant solution for the data association in multi-target tracking and (ii) it allows for probabilistic physical constraints that avoid collisions between objects to be naturally enforced. The thesis also explore the possibility of using implicit shape representation for online shape learning. We use the harmonics of 2D discrete cosine transform (DCT) to represent 2D shapes. High frequency harmonics are decoupled from low ones to represent the coarse information and the details of the 2D shape. A regression model is learnt online to model the relationship between the high and low frequency harmonics using Locally Weighted Projection Regression (LWPR). We have demonstrated that the learned regression model is able to detect occlusion and recover them to the complete shape.

Edge computing emerged as an attractive method of distributing computational resources in a network. When compared with cloud computing, edge computing presents a number of key benefits which include improved response times, scalability, privacy, and redundancy. This makes edge computing desirable for use in mobile robotics, in which low response times and redundancy are key issues. This thesis work will cover the design and implementation of a general-purpose edge orchestrator, that can support a wide range of domains due to being built around the concept of modularity. An edge orchestrator is a program that manages an edge network by analyzing the edge network and the requirements of devices within that network, then optimizing how the computational resources are distributed within the devices in the network. Modules have been designed and implemented on top of the orchestrator that allow for optimizations specific to mobile robotics. A proof of concept module was designed to optimize for latency which was compared with an external algorithm that seeks to optimize for latency as well. Both were implemented on the orchestrator and an evaluation was performed to compare both approaches. It was found that the module designed in this thesis is better suited for optimizing for latency. LXD was chosen to be used for software packaging which is a container-based software packaging solution. A software packaging solution is used to package software which would be deployed by the orchestrator. The choice of LXD is analyzed through an evaluation procedure that compares it with Docker, which is another container-based software packaging solution. It was found that LXD produces containers of smaller size but required more time to generate those containers, when compared with Docker. It was also found that LXD container images exhibited better performance than the Docker ones for software which is not I/O heavy. It was decided through this evaluation that LXD was a better choice for the orchestrator. / Edge computing är en attraktiv metod för distribution av beräkningsresurser i ett nätverk. Jämfört med molnberäkningar har edge computing ett antal viktiga fördelar som inkluderar förbättrade svarstider, skalbarhet, integritet och redundans. Detta gör edge computing önskvärt för användning i mobil robotik, där låga svarstider och redundans är viktiga frågor. Detta examensarbete täcker min design och implementering av en generell edge-orkestrerare, som kan stödja ett brett spektrum av domäner eftersom den är byggd på ett modulärt sätt. En edge-orkestrerare är ett program som hanterar ett edge-nätverk genom att analysera edge-nätverket och kraven på enheter inom det nätverket, för att sedan optimera hur beräkningsresurserna fördelas över enheterna i nätverket. Jag har utformat och implementerat moduler ovanpå orkestratorn som möjliggör optimeringar specifika för mobil robotik. Jag designade också en koncepttest-modul för att optimera för latens, vilken jag jämförde med en extern algoritm som även den försöker optimera för latens. Jag implementerade båda på orkestratorn och utförde en utvärdering för att jämföra båda metoderna. Resultaten visar att modulen utformad i detta examensarbete är bättre lämpad för att optimera för latens. För mjukvarupaketering valde jag att använda LXD, vilket är en containerbaserad mjukvarupaketeringslösning. Dess syfte är att paketera programvara som ska distribueras av orkestratorn. Jag analyserade valet av LXD genom ett utvärderingsförfarande som jämför det med Docker, som är en annan containerbaserad mjukvarupaketeringslösning. Jag fann att LXD producerar mindre containrar, men krävde mer tid för att generera dessa containrar jämfört med Docker. Jag fann också att LXD-containerbilder visade bättre prestanda än Docker-bilderna för programvara som inte är I/O-intensiv. Jag fann genom denna utvärdering att LXD var ett bättre val för orkestratorn.