Planning and control of robotic manipulation actions for extreme environments

A large societal and economic need arises for advanced robotic capabilities, where we need to perform complex human-like tasks such as tool-use, in environments that are hazardous for human workers. This thesis addresses a collection of problems, which arise when robotic manipulators must perform complex tasks in cluttered and constrained environments. The work is illustrated by example scenarios of robotic tool use, grasping and manipulating, motivated by the challenges of dismantling operations in the extreme environments of nuclear decommissioning Contrary to popular assumptions, legacy nuclear facilities (which can date back three-quarters of a century in the UK) can be highly unstructured and uncertain environments, with insufficient a-priori information available for e.g. conventional pre-programming of robot tasks. Meanwhile, situational awareness and direct teleoperation can be extremely difficult for human operators working in a safe zone that is physically remote from the robot. This engenders a need for significant autonomous capabilities. Robots must use vision and sensory systems to perceive their environment, plan and execute complex actions on complex objects in cluttered and constrained environments. Significant radiation, of different types and intensities, provides further challenges in terms of sensor noise. Perception uncertainty can also result from e.g. vision systems observing shiny featureless metal structures. Robotic actions therefore need to be: i) planned in ways that are robust to uncertainties; and ii) controlled in ways which enable the robust reaction to disturbances. In particular, we investigate motion planning and control in tasks where the robot must: maintain contact while moving over arbitrarily shaped surfaces with end-effector tools; exert forces and withstand perturbations during forceful contact actions; while also avoiding collisions with obstacles; avoiding singularity configurations; and increasing robustness by maximising manipulability during task execution. Furthermore, we consider the issues of robust planning and control with respect to uncertain information, derived from noisy sensors in challenging environments. We explore the Riemannian geometry and robot's manipulability to yield path planners that produce paths for both fixed-based and floating-based robots, whose tools always stay in contact with the object's surface. Our planners overcome disturbances in the perception and account for robot/environment interactions that may demand unexpected forces. The task execution is entrusted to a hybrid force/motion controller whose motion space behaves with compliance to accommodate unexpected stiffness changes throughout the contact. We examine the problem of grasping a tool for performing a task. Firstly, we introduce a method for selecting the grasp candidate onto an object yielding collision-free motion for the robot in the post-grasp movements. Furthermore, we study the case of a dual-arm robot performing full-force tasks on an object and slippage on the grasping is allowed. We account for the slippage throughout the task execution using a novel controller based on the sliding mode controllers

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)

Machine Learning Meets Advanced Robotic Manipulation

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works

On the dynamics of human locomotion and co-design of lower limb assistive devices

Recent developments in lower extremities wearable robotic devices for the assistance and rehabilitation of humans suffering from an impairment have led to several successes in the assistance of people who as a result regained a certain form of locomotive capability. Such devices are conventionally designed to be anthropomorphic. They follow the morphology of the human lower limbs. It has been shown previously that non-anthropomorphic designs can lead to increased comfort and better dynamical properties due to the fact that there is more morphological freedom in the design parameters of such a device. At the same time, exploitation of this freedom is not always intuitive and can be difficult to incorporate. In this work we strive towards a methodology aiding in the design of possible non-anthropomorphic structures for the task of human locomotion assistance by means of simulation and optimization. The simulation of such systems requires state of the art rigid body dynamics, contact dynamics and, importantly, closed loop dynamics. Through the course of our work, we first develop a novel, open and freely available, state of the art framework for the modeling and simulation of general coupled dynamical systems and show how such a framework enables the modeling of systems in a novel way. The resultant simulation environment is suitable for the evaluation of structural designs, with a specific focus on locomotion and wearable robots. To enable open-ended co-design of morphology and control, we employ population-based optimization methods to develop a novel Particle Swarm Optimization derivative specifically designed for the simultaneous optimization of solution structures (such as mechanical designs) as well as their continuous parameters. The optimizations that we aim to perform require large numbers of simulations to accommodate them and we develop another open and general framework to aid in large scale, population based optimizations in multi-user environments. Using the developed tools, we first explore the occurrence and underlying principles of natural human gait and apply our findings to the optimization of a bipedal gait of a humanoid robotic platform. Finally, we apply our developed methods to the co-design of a non-anthropomorphic, lower extremities, wearable robot in simulation, leading to an iterative co-design methodology aiding in the exploration of otherwise hard to realize morphological design