Unified Closed Form Inverse Kinematics for the KUKA youBot

Mobile manipulators are of high interest to industry because of the increased flexibility and effectiveness they offer. The combination and coordination of the mobility provided by a mobile platform and of the manipulation capabilities provided by a robot arm leads to complex analytical problems for research. These problems can be studied very well on the KUKA youBot, a mobile manipulator designed for education and research applications. Issues still open in research include solving the inverse kinematics problem for the unified kinematics of the mobile manipulator, including handling the kinematic redundancy introduced by the holonomic platform of the KUKA youBot. As the KUKA youBot arm has only 5 degrees of freedom, a unified platform and manipulator system is needed to compensate for the missing degree of freedom. We present the KUKA youBot as an 8 degree of freedom serial kinematic chain, suggest appropriate redundancy parameters, and solve the inverse kinematics for the 8 degrees of freedom. This enables us to perform manipulation tasks more efficiently. We discuss implementation issues, present example applications and some preliminary experimental evaluation along with discussion about redundancies

An Investigation into a Combined Visual Servoing and Vision-Based Navigation System Robot for the Aerospace Manufacturing Industry

High-Value manufacturing, such as aerospace manufacturing, has been less impacted by the mainstream use of robotic automation compared to other manufacturing industries. This is due to the cost factor required when creating robotic systems which can successfully interact with such high tolerance work�pieces. This research aims to investigate the gap of robotics within high-value aerospace manufacturing, with the goal of creating a generic robotic algorithm which can effectively and optimally detect and trace a variety of aerostructure inspired workpieces. This goal was achieved by firstly developing a vision system for detecting and tracing particular features of partially-known workpieces. These workpieces var�ied in size and spatial profile, having both obtuse and acute edges. Once an effective vision system was developed, a variety of distribution-of-labour algorithms were developed, with the aim of dividing the task of tracing a work�piece between the kinematic arm and mobile base. The results showed that different distribution-of-labour algorithms performed differently, depending on the type of detected feature, specifically how vertically inclined the feature was. These results were used to develop an optimal distribution-of-labour algorithm, which could dynamically and optimally switch between different distribution-of-labour systems, to trace a workpiece both quickly and accurately. Results showed that an optimal distribution-of-labour algorithm decreased tracing time and increased accuracy in realistic aerostructure-inspired workpieces compared to just using one major algorithm, and could dynamically trace workpieces regardless of previous knowledge or spatial profile

High-DOF Motion Planning in Dynamic Environments using Trajectory Optimization

Motion planning is an important problem in robotics, computer-aided design, and simulated environments. Recently, robots with a high number of controllable joints are increasingly used for different applications, including in dynamic environments with humans and other moving objects. In this thesis, we address three main challenges related to motion planning algorithms for high-DOF robots in dynamic environments: 1) how to compute a feasible and constrained motion trajectory in dynamic environments; 2) how to improve the performance of realtime computations for high-DOF robots; 3) how to model the uncertainty in the environment representation and the motion of the obstacles. We present a novel optimization-based algorithm for motion planning in dynamic environments. We model various constraints corresponding to smoothness, as well as kinematics and dynamics bounds, as a cost function, and perform stochastic trajectory optimization to compute feasible high-dimensional trajectories. In order to handle arbitrary dynamic obstacles, we use a replanning framework that interleaves planning with execution. We also parallelize our approach on multiple CPU or GPU cores to improve the performance and perform realtime computations. In order to deal with the uncertainty of dynamic environments, we present an efficient probabilistic collision detection algorithm that takes into account noisy sensor data. We predict the future obstacle motion as Gaussian distributions, and compute the bounded collision probability between a high-DOF robot and obstacles. We highlight the performance of our algorithms in simulated environments as well as with a 7-DOF Fetch arm.Doctor of Philosoph