Autonomous mobile robots in agriculture have brought digital transformations with new automation technologies. Mundane tasks in agricultural fields involve long-term deployment of scheduled assignments done over long working hours. Hectares of land also require much distance to be traveled by the robot, wherein surviving without stranding powerless amid operation is a key factor towards long-term autonomy. Scheduling tasks are usually required as the mobile robot’s battery life is limited. However, dynamic and unstructured outdoor environments make it practically challenging to abide by a schedule even with fixed charging routines. Most robot systems use hard thresholds and fixed periods to decide when to charge. These measures produce behaviors that ignore the safety risk by not considering current conditions. Instead, the robot should adaptively return to the charger or call for human assistance. Safety was a score in this work that represented the robot’s ability to return to the charger at any time. This thesis proposes a real-time safety watchdog that exploits parameters like charger distance, mileage, and current battery status to calculate safety. Hence, a safety-constraint based approach is followed to flexibly sustain survivability during long-term deployments.This work compared a typical rule-based system to the real-time safety watchdog that monitors multiple parameters based on the battery state. Simulated results empirically showed that the latter approach ensures survivability and maintains a healthy performance that supports long-term autonomy. The concept of a real-time safety watchdog is also showcased on a physical robot, pushing statistical evaluation that uses values of long-term deployment as future work. Although a watchdog ensures survivability to a planned schedule, adapting the experience in the upcoming schedule can be worked upon to continue this work. / Autonoma mobila robotar inom jordbruket har lett till digitala förändringar med ny automationsteknik. Vanliga arbetsuppgifter inom jordbruket innebär långvariga, schemalagda uppdrag som utförs under långa arbetstider. Hektar av mark kräver också att roboten färdas långa sträckor, och att överleva utan att stranda utan ström mitt i arbetet är en nyckelfaktor för långsiktig autonomi. Schemaläggningsuppgifter krävs vanligtvis eftersom den mobila robotens batteritid är begränsad. Dynamiska och ostrukturerade utomhusmiljöer gör det dock praktiskt taget svårt att följa ett schema även med fasta laddningsrutiner. De flesta robotsystem använder hårda tröskelvärden och fasta perioder för att bestämma när laddning ska ske. Dessa åtgärder ger upphov till beteenden som ignorerar säkerhetsrisken genom att de inte tar hänsyn till de aktuella förhållandena. I stället bör roboten adaptivt återvända till laddaren eller kalla på mänsklig hjälp. Säkerheten var ett poäng i detta arbete som representerade robotens förmåga att återvända till laddaren när som helst. I den här avhandlingen föreslås en säkerhetsövervakare i realtid som utnyttjar parametrar som laddningsavstånd, körsträcka och aktuell batteristatus för att beräkna säkerheten. Därför följs en säkerhetsbegränsningsbaserad strategi för att flexibelt upprätthålla överlevnadsförmågan under långvariga utplaceringar.I detta arbete jämfördes ett typiskt regelbaserat system med en säkerhetsövervakningshund i realtid som övervakar flera parametrar baserat på batteriets status. Simulerade resultat visade empiriskt att den senare metoden säkerställer överlevnadsförmåga och upprätthåller en sund prestanda som stöder långsiktig autonomi. Konceptet med en säkerhetsövervakningshund i realtid presenteras också. på en fysisk robot, genom att trycka på statistisk utvärdering som använder värden för långsiktig användning som framtida arbete. Även om en vakthund säkerställer överlevnadsförmåga till en planerad tidtabell, kan det vara svårt att anpassa erfarenheterna till den kommande tidtabellen. kan man arbeta med för att fortsätta detta arbete.

The use of robotics has recently seen significant growth in various domains such as unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid robots. However, one of the most important and essential capabilities required for long term autonomy, which is the ability to operate robustly and safely in real-world environments, in contrast to industrial and laboratory setup is largely missing. Designing robots that can operate reliably and efficiently in cluttered and changing environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e. robots with multiple actuators. On one hand, the dexterity offered by the kinematic redundancy allows the robot to perform dexterous manipulation tasks in complex environments, whereas on the other hand, such complex system also makes controlling and planning very challenging. To address such two interrelated problems, we exploit robot motion synthesis from three perspectives that feed into each other: end-pose planning, motion planning and motion adaptation. We propose several novel ideas in each of the three phases, using which we can efficiently synthesise dexterous manipulation motion for fixed-base robotic arms, mobile manipulators, as well as humanoid robots in cluttered and potentially changing environments. Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite for other modules such as motion planning, is an important and yet unsolved problem in robotics. Such information is often assumed given, or manually provided in practice, which significantly limiting high-level autonomy. In our research, by using novel data pre-processing and encoding techniques, we are able to efficiently search for collision-free end-poses in challenging scenarios in the presence of uneven terrains. After having found the end-poses, the motion planning module can proceed. Although motion planning has been claimed as well studied, we find that existing algorithms are still unreliable for robust and safe operations in real-world applications, especially when the environment is cluttered and changing. We propose a novel resolution complete motion planning algorithm, namely the Hierarchical Dynamic Roadmap, that is able to generate collision-free motion trajectories for redundant robotic arms in extremely complicated environments where other methods would fail. While planning for fixed-base robotic arms is relatively less challenging, we also investigate into efficient motion planning algorithms for high DoF (30 - 40) humanoid robots, where an extra balance constraint needs to be taken into account. The result shows that our method is able to efficiently generate collision-free whole-body trajectories for different humanoid robots in complex environments, where other methods would require a much longer planning time. Both end-pose and motion planning algorithms compute solutions in static environments, and assume the environments stay static during execution. While human and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To address this issue, we propose a novel state space representation, the Distance Mesh space, in which the robot is able to remap the pre-planned motion in real-time and adapt to environmental changes during execution. By utilizing the proposed end-pose planning, motion planning and motion adaptation techniques, we obtain a robotic framework that significantly improves the level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR (7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator), Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing that our methods are truly applicable to solve high dimensional motion planning for practical problems.

Robots are operating for longer times and collecting much more data than just a few years ago. In this setting we are interested in exploring ways of modeling the environment, segmenting out areas of interest and keeping track of the segmentations over time, with the purpose of building 4D models (i.e. space and time) of the relevant parts of the environment. Our approach relies on repeatedly observing the environment and creating local maps at specific locations. The first question we address is how to choose where to build these local maps. Traditionally, an operator defines a set of waypoints on a pre-built map of the environment which the robot visits autonomously. Instead, we propose a method to automatically extract semantically meaningful regions from a point cloud representation of the environment. The resulting segmentation is purely geometric, and in the context of mobile robots operating in human environments, the semantic label associated with each segment (i.e. kitchen, office) can be of interest for a variety of applications. We therefore also look at how to obtain per-pixel semantic labels given the geometric segmentation, by fusing probabilistic distributions over scene and object types in a Conditional Random Field. For most robotic systems, the elements of interest in the environment are the ones which exhibit some dynamic properties (such as people, chairs, cups, etc.), and the ability to detect and segment such elements provides a very useful initial segmentation of the scene. We propose a method to iteratively build a static map from observations of the same scene acquired at different points in time. Dynamic elements are obtained by computing the difference between the static map and new observations. We address the problem of clustering together dynamic elements which correspond to the same physical object, observed at different points in time and in significantly different circumstances. To address some of the inherent limitations in the sensors used, we autonomously plan, navigate around and obtain additional views of the segmented dynamic elements. We look at methods of fusing the additional data and we show that both a combined point cloud model and a fused mesh representation can be used to more robustly recognize the dynamic object in future observations. In the case of the mesh representation, we also show how a Convolutional Neural Network can be trained for recognition by using mesh renderings. Finally, we present a number of methods to analyse the data acquired by the mobile robot autonomously and over extended time periods. First, we look at how the dynamic segmentations can be used to derive a probabilistic prior which can be used in the mapping process to further improve and reinforce the segmentation accuracy. We also investigate how to leverage spatial-temporal constraints in order to cluster dynamic elements observed at different points in time and under different circumstances. We show that by making a few simple assumptions we can increase the clustering accuracy even when the object appearance varies significantly between observations. The result of the clustering is a spatial-temporal footprint of the dynamic object, defining an area where the object is likely to be observed spatially as well as a set of time stamps corresponding to when the object was previously observed. Using this data, predictive models can be created and used to infer future times when the object is more likely to be observed. In an object search scenario, this model can be used to decrease the search time when looking for specific objects. / <p>QC 20171009</p>