NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators

We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadratic programme which, when considering obstacles, joint limits, and manipulability on a 7 degree-of-freedom robot, is generally solved in a few ms. While NEO is not intended to replace state-of-the-art motion planners, our experiments show that it is a viable alternative for scenes with moderate complexity while also being capable of reactive control. For more complex scenes, NEO is better suited as a reactive local controller, in conjunction with a global motion planner. We compare NEO to motion planners on a standard benchmark in simulation and additionally illustrate and verify its operation on a physical robot in a dynamic environment. We provide an open-source library which implements our controller.Comment: IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted January, 2021. The code and videos can be found at https://jhavl.github.io/neo

Robustness to external disturbances for legged robots using dynamic trajectory optimisation

In robotics, robustness is an important and desirable attribute of any system, from perception to planning and control. Robotic systems need to handle numerous factors of uncertainty when they are deployed, and the more robust a method is, the fewer chances there are of something going wrong. In planning and control, being robust is crucial to deal with uncertain contact timings and positions, mismatches in the dynamics model of the system, noise in the sensor readings and communication delays. In this thesis, we focus on the problem of dealing with uncertainty and external disturbances applied to the robot. Reactive robustness can be achieved at the control stage using a variety of control schemes. For example, model predictive control approaches are robust against external disturbances thanks to the online high-frequency replanning of the motion being executed. However, taking robustness into account in a proactive way, i.e., during the planning stage itself, enables the adoption of kinematic configurations that allow the system as a whole to better deal with uncertainty and disturbances. To this end, we propose a novel trajectory optimisation framework for robotic systems, ranging from fixed-base manipulators to legged robots, such as humanoids or quadrupeds equipped with arms. We tackle the problem from a first-principles perspective, and define a robustness metric based on the robot’s capabilities, such as the torques available to the system (considering actuator torque limits) and contact stability constraints. We compare our results with other existing approaches and, through simulation and experiments on the real robot, we show that our method is able to plan trajectories that are more robust against external disturbances

Estimation of objects’ inertial parameters, and their usage in robot grasping and manipulation

The subject of this thesis is the estimation of an object's inertial parameters by a robotic arm, and the exploitation of those parameters in the design of efficient manipulation criteria. The inertial parameters of objects describe the resistance of the object to an applied force, and dictate its motion. Research has shown that humans intuitively exploit them for their everyday manipulations. As humans are very capable of performing efficient manipulations, it is natural that robots should use the inertial parameters as well. Additionally, as the inertial parameters are not straightforward to calculate, there is the need for development of methods that can estimate them online. This thesis focuses on two directions, developing novel methods so that robots can accurately estimate the inertial parameters of an object, as well as developing manipulation criteria that can make robot task completion more efficient. The relevant literature is gathered, categorised and analytically described, and the innovation gaps are identified. The thesis offers novel research solutions on the problem of estimation of the inertial parameters with minimal robot interaction. The paradigm is shifted from the existing literature, and a data-driven estimation algorithm is introduced, that achieves accurate results with both simulated and real data. Additionally, the presented research is offering novel manipulation criteria that are affected by the object's inertial parameters. The results suggest that knowledge of the inertial parameters can make the robot tasks more power-efficient and safe to their surroundings. The core methodology is shown to be versatile to the robotic platform. Though most experiments are performed on a terrestrial robot, a numerical example is also shown for a space robot. The results of the thesis suggest that the developed methods can be used in various environments, with the most suitable being extreme environments where accuracy, efficiency and autonomy is required

Proceedings of the NASA Conference on Space Telerobotics, volume 3

The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Multi-point static dexterous posture manipulation for the stiffness identification of serial kinematic end-effectors.

Masters Degree. University of KwaZulu-Natal, Durban.The low stiffness inherent in serial robots hinders its application to perform advanced operations due to its reduced accuracy imparted through deformations within the links and joints. The high repeatability, extended workspace, and speed of serial manipulators make them appealing to perform precision operations as opposed to its alternative, the CNC machine. However, due to the serial arrangement of the linkages of the system, they lack the accuracy to meet present-day demands. To address the low stiffness problem, this research provided a low-cost dexterous posture identification method. The study investigated the joint stiffness of a Fanuc M10-iA 6 Degree of Freedom (DOF) serial manipulator. The investigation involved a multivariable analysis that focused on the robot’s workspace, kinematic singularity, and dexterity to locate high stiffness areas and postures. The joint stiffness modelling applied the Virtual Joint Method (VJM), which replaced the complicated mechanical robot joints with one-dimensional (1-D) springs. The effects of stress and deflection are linearly related; the highest stress in a robot’s structure is distributed to the higher load-bearing elements such as the robot joints, end-effector, and tool. Therefore, by locating optimal postures, the induced stresses can be better regulated throughout the robot’s structure, thereby reducing resonant vibrations of the system and improving process accuracy and repeatability. These aspects are quantifiably pitched in terms of the magnitude differences in the end-effector deflection. The unique combination of the dexterity and the stiffness analyses aimed to provide roboticists and manufacturers with an easy and systematic solution to improve the stiffness, accuracy, and repeatability of their serial robots. A simple, user-friendly and cost-effective alternative to deflection measurements using accelerometers is provided, which offers an alternative to laser tracking devices that are commonly used for studies of this nature. The first investigation focused on identifying the overall workspace of the Fanuc M-10iA robot. The reachable workspace was investigated to understand the functionality and potential of the Fanuc robot. Most robotic studies stem from analysing the workspace since the workspace is a governing factor of the manipulator and end-effector placement, and its operations, in a manufacturing setting. The second investigation looked at identifying non-reachable areas and points surrounding the robot. This analysis, along with the workspace examination, provided a conclusive testing platform to test the dexterity and stiffness methodologies. Although the research focused on fixing the end-effector at a point (static case), the testing platform was structured precisely to cater for all robotic manufacturing tasks that are subjected to high applied forces and vibrations. Such tasks include, but are not limited to, drilling, tapping, fastening, or welding, and some dynamic and hybrid manufacturing operations. The third investigation was the application of a dexterous study that applied an Inverse Kinematic (IK) method to localise multiple robot configurations about a user-defined point in space. This process was necessary since the study is based on a multi-point dexterous posture identification technique to improve the stiffness of Serial Kinematic Machines (SKMs). The stiffness at various points and configurations were tested, which provided a series of stiff and non-stiff areas and postures within the robot’s workspace. MATLAB®, a technical computing software, was used to model the workspace and singularity of the robot. The dexterity and stiffness analyses were numerically evaluated using Wolfram Mathematica. The multivariable analyses served to improve the accuracy of serial robots and promote their functionality towards high force application manufacturing tasks. Apart from the improved stiffness performance offered, the future benefit of the method could advance the longevity of the robot as well as minimise the regular robot maintenance that is often required due to excessive loading, stress, and strain on the robot motors, joints, and links

Robustness, evidence, and uncertainty: an exploration of policy applications of robustness analysis

Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an environment is to rely on evidence. Despite the recent increase in post-fact figures in politics, evidence-based policymaking takes centre stage in policy-setting institutions. Often, however, policy-makers face large volumes of evidence from different sources. Robustness analysis can, prima facie, handle this evidential diversity. Roughly, a hypothesis is supported by robust evidence if the different evidential sources (such as observations or model results) are in agreement. In this thesis, I strengthen the case for the use of robustness analysis in evidence-based policymaking by answering open research questions about this inference technique. First, I argue that existing taxonomies miss a fruitful category of robustness reasoning, that is predictive stability. Second, I claim that derivational robustness analysis – the investigation of whether the results of different models are in agreement – can yield interesting insights even if not the entire relevant model space is covered by available models or if the model results are only partially in agreement. Third, I claim that expert knowledge is necessary to address questions that arise when one applies measurement robustness analysis – the investigation into whether multiple means of measurement yield the same result. Finally, I argue that, in situations where evidence from different measurements is not in agreement, it can be advisable to no longer take all of the evidence into account. This can be done in a rationally defensible way by choosing the most adequate theory or model underlying parts of the evidence set. I discuss examples from climate, medical, and economic policy-making to establish my claims

Simulation and Optimisation of a Two Degree of Freedom, Planar, Parallel Manipulator

Development in pick-and-place robotic manipulators continues to grow as factory processes are streamlined. One configuration of these manipulators is the two degree of freedom, planar, parallel manipulator (2DOFPPM). A machine building company, RML Engineering Ltd., wishes to develop custom robotic manipulators that are optimised for individual pick-and-place applications. This thesis develops several tools to assist in the design process. The 2DOFPPM’s structure lends itself to fast and accurate translations in a single plane. However, the performance of the 2DOFPPM is highly dependent on its dimensions. The kinematics of the 2DOFPPM are explored and used to examine the reachable workspace of the manipulator. This method of analysis also gives insight into the relative speed and accuracy of the manipulator’s end-effector in the workspace. A simulation model of the 2DOFPPM has been developed in Matlab’s® SimMechanics®. This allows the detailed analysis of the manipulator’s dynamics. In order to provide meaningful input into the simulation model, a cubic spline trajectory planner is created. The algorithm uses an iterative approach of minimising the time between knots along the path, while ensuring the kinematic and dynamic limits of the motors and end-effector are abided by. The resulting trajectory can be considered near-minimum in terms of its cycle-time. The dimensions of the 2DOFPPM have a large effect on the performance of the manipulator. Four major dimensions are analysed to see the effect each has on the cycle-time over a standardised path. The dimensions are the proximal and distal arms, spacing of the motors and the height of the manipulator above the workspace. The solution space of all feasible combinations of these dimensions is produced revealing cycle-times with a large degree of variation over the same path. Several optimisation algorithms are applied to finding the manipulator configuration with the fastest cycle-time. A random restart hill-climber, stochastic hill-climber, simulated annealing and a genetic algorithm are developed. After each algorithm’s parameters are tuned, the genetic algorithm is shown to outperform the other techniques

Simulation and Optimisation of a Two Degree of Freedom, Planar, Parallel Manipulator

Development in pick-and-place robotic manipulators continues to grow as factory processes are streamlined. One configuration of these manipulators is the two degree of freedom, planar, parallel manipulator (2DOFPPM). A machine building company, RML Engineering Ltd., wishes to develop custom robotic manipulators that are optimised for individual pick-and-place applications. This thesis develops several tools to assist in the design process. The 2DOFPPM’s structure lends itself to fast and accurate translations in a single plane. However, the performance of the 2DOFPPM is highly dependent on its dimensions. The kinematics of the 2DOFPPM are explored and used to examine the reachable workspace of the manipulator. This method of analysis also gives insight into the relative speed and accuracy of the manipulator’s end-effector in the workspace. A simulation model of the 2DOFPPM has been developed in Matlab’s® SimMechanics®. This allows the detailed analysis of the manipulator’s dynamics. In order to provide meaningful input into the simulation model, a cubic spline trajectory planner is created. The algorithm uses an iterative approach of minimising the time between knots along the path, while ensuring the kinematic and dynamic limits of the motors and end-effector are abided by. The resulting trajectory can be considered near-minimum in terms of its cycle-time. The dimensions of the 2DOFPPM have a large effect on the performance of the manipulator. Four major dimensions are analysed to see the effect each has on the cycle-time over a standardised path. The dimensions are the proximal and distal arms, spacing of the motors and the height of the manipulator above the workspace. The solution space of all feasible combinations of these dimensions is produced revealing cycle-times with a large degree of variation over the same path. Several optimisation algorithms are applied to finding the manipulator configuration with the fastest cycle-time. A random restart hill-climber, stochastic hill-climber, simulated annealing and a genetic algorithm are developed. After each algorithm’s parameters are tuned, the genetic algorithm is shown to outperform the other techniques