Design and implementation of a compliant robot with force feedback and strategy planning software

Force-feedback robotics techniques are being developed for automated precision assembly and servicing of NASA space flight equipment. Design and implementation of a prototype robot which provides compliance and monitors forces is in progress. Computer software to specify assembly steps and makes force feedback adjustments during assembly are coded and tested for three generically different precision mating problems. A model program demonstrates that a suitably autonomous robot can plan its own strategy

Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study

Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms’ research and application on disassembly tasks

Collaborative and Cooperative Robotics Applications using Visual Perception

The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes. Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning. Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection. Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated. Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Survey on model-based manipulation planning of deformable objects

A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time