Motion planning is an essential component of an autonomous system. This project aims to design a motion planning module to automate the screwing process of radio units. The goal is to choose and implement a motion planner that provides the speed, precision, and efficiency required for the screwing task on a radio filter with a large number of holes located close to each other. Four control-based motion planners were investigated on a 6 Degrees Of Freedom (DOF) robot arm in Robot Operating System (ROS). The investigated motion planners are Rapidly-exploring Random Trees, The Kinodynamic Motion Planning by Interior-Exterior Cell Exploration (KPIECE), The Path- Directed subdivision Trees (PDST), Expansive Space Trees (EST). All these planners are available in The Open Motion Planning Library (OMPL). The motion planners were implemented on a simulated version of a UR5 robot arm. This simulated model is generated by the MoveIt Setup Assistant, which is the primary tool for creating configuration files for kinematics chains in MoveIt. ROS is the chosen platform to design various control methods and motion planning algorithms. Hence two primary workspaces have been created. These workspaces contain several packages and nodes with multiple tasks such as motion planning, visualization, and data extraction. All the nodes communicate using ROS communication tools such as massages services and action client services. Furthermore, this project covers also test and benchmarking all the mentioned planners to determine which planner provides optimal performance in different environments. The planner’s performance is evaluated by designing two experiments in three benchmarking scenarios. The first test is intended to determine how the planners perform a motion planning task similar to an actual screwing process of a radio filter. The purpose of the second experiment is to investigate how the planners perform as the density of the obstacles increase. The performances of the planners have been analyzed and compared with each other using different benchmarking tools such as the planner arena. Result of this project indicates, KPIECE and EST can outperform the state-of-the- art planner, RRT-Connect in some and metrics, especially in an environment with a low obstacles density. However, RRT-Connect is still superior in more dense and complicated settings. / Rörelseplanering är en viktig komponent i ett automatiserat system. Detta projekt syftar till att designa en rörelseplaneringsmodul för att automatisera skruvningen av radioenheter. Målet är att implementera en rörelseplanerare som kan frambringa den hastighet, noggrannhet och effektivitet som krävs för en automatiserad skruvdragare. Skruvdragarens uppgift är att skruva ett antal hål placerade nära varandra på en radiofilter. Denna upsats har undersökt fyra kontrollbaserade rörelseplanerare på en 6 Degrees Of Freedom (DOF) robotarm med hjälp av Robot Operating System (ROS). De undersökta rörelseplanerarna är Rapidly-Exploring Random Trees, The Kinodynamic Motion Planning by Interior-Exterior Cell Exploration (KPIECE), The Path- Directed subdivision Trees (PDST) och Expansive Space Trees (EST)som är tillgängliga i Open Motion The Open Motion Planning Library (OMPL). Planerarna implementeras på en simulerad UR5-robotarm, genererad av MoveIt Setup Assistant, som är det primära verktyget för att skapa konfigurationsfiler för kinematikkedjor i MoveIt. ROS är den valda plattformen för att designa styrmetoder och rörelseplaneringens algoritmer vilket medför att två arbetsytor har skapats. Dessa arbetsytor innehåller flera paket och noder med flera uppgifter bland annat rörelseplanering, visualisering och dataextraktion. Alla noder kommunicerar med varandra genom ROS kommunikationsverktyg liksom massagetjänster och action-client tjänster. Detta projekt omfattar även benchmarkingäv alla ovannämnda planerare för att avgöra vilken of dessa planerare kan åstadkomma en optimal prestanda i olika miljöer. Planerarens prestanda utvärderas genom att designa två experiment i tre benchmarking-scenarier. Det första testet är avsett att bestämma hur en planerare utför en rörelseplaneringsuppgift vilket liknar en verklig skruvprocess för en radioenhet. Andra experimentet är att undersöka hur planerarna presterade när tätheten av hindren ökar. Planerarnas prestationer har analyserats och jämförts med varandra med hjälp av olika benchmarkingverktyger, till exemple Planer Arena. Enligt resultatet av detta projekt kan KPIECE och EST prestera bättre jämfort med den senaste planeraren RRT-Connect i vissa områden, särskilt i ett miljö med låg hindertäthet. RRT-Connect är dock fortfarande överlägsen i mer täta och komplicerade miljöer.

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.