From Deployments Of Elder Care Service Robots To The Design Of Affordable Low-Complexity End-Effectors And Novel Manipulation Techniques

This thesis proposes an investigation on both behavioral and technical aspects of human-robot interaction (HRI) in elder care settings, in view of an affordable platform capable of executing desired tasks. The behavioral investigation combines a qualitative study with focus groups and surveys from not only the elders’ standpoint, but also from the standpoint of healthcare professionals to investigate suitable tasks to be accomplished by a service robot in such environments. Through multiple deployments of various robot embodiments at actual elder care facilities (such as at a low-income Supportive Apartment Living, SAL, and Program of All-Inclusive Care, PACE Centers) and interaction with older adults, design guidelines are developed to improve on both interaction and usability aspects. This need assessment informed the technical investigation of this work, where we initially propose picking and placing objects using end-effectors without internal mobility (or zero degrees-of-freedom, DOF), considering both quasi-static (tipping and regrasping as in-hand manipulation) and dynamic approaches. Maximizing grasping versatility by allowing robots to grasp multiple objects sequentially using a single end-effector and actuator is also proposed. These novel manipulation techniques and end-effector designs focus on minimizing robot hardware usage and cost, while still performing complex tasks and complying with safety constraints imposed by the elder care facilities

Soft Robotic Grippers

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research

Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system

Constructing Geometries for Group Control: Methods for Reasoning about Social Behaviors

Social behaviors in groups has been the subjects of hundreds of studies in a variety of research disciplines, including biology, physics, and robotics. In particular, flocking behaviors (commonly exhibited by birds and fish) are widely considered archetypical social behavioris and are due, in part, to the local interactions among the individuals and the environment. Despite a large number of investigations and a significant fraction of these providing algorithmic descriptions of flocking models, incompleteness and imprecision are readily identifiable in these algorithms, algorithmic input, and validation of the models. This has led to a limited understanding of the group level behaviors. Through two case-studies and a detailed meta-study of the literature, this dissertation shows that study of the individual behaviors are not adequate for understanding the behaviors displayed by the group. To highlight the limitations in only studying the individuals, this dissertation introduces a set of tools, that together, unify many of the existing microscopic approaches. A meta-study of the literature using these tools reveal that there are many small differences and ambiguities in the flocking scenarios being studied by different researchers and domains; unfortunately, these differences are of considerable significance. To address this issue, this dissertation exploits the predictable nature of the group’s behaviors in order to control the given group and thus hope to gain a fuller understanding of the collective. From the current literature, it is clear the environment is an important determinant in the resulting collective behaviors. This dissertation presents a method for reasoning about the effects the geometry of an environment has on individuals that exhibit collective behaviors in order to control them. This work formalizes the problem of controlling such groups by means of changing the environment in which the group operates and shows this problem to be PSPACE-Hard. A general methodology and basic framework is presented to address this problem. The proposed approach is general in that it is agnostic to the individual’s behaviors and geometric representations of the environment; allowing for a large variety in groups, desired behaviors, and environmental constraints to be considered. The results from both the simulations and over 80 robot trials show (1) the solution can automatically generate environments for reliably controlling various groups and (2) the solution can apply to other application domains; such as multi-agent formation planning for shepherding and piloting applications

Design and Development of Assistive Robots for Close Interaction with People with Disabilities

People with mobility and manipulation impairments wish to live and perform tasks as independently as possible; however, for many tasks, compensatory technology does not exist, to do so. Assistive robots have the potential to address this need. This work describes various aspects of the development of three novel assistive robots: the Personal Mobility and Manipulation Appliance (PerMMA), the Robotic Assisted Transfer Device (RATD), and the Mobility Enhancement Robotic Wheelchair (MEBot). PerMMA integrates mobility with advanced bi-manual manipulation to assist people with both upper and lower extremity impairments. The RATD is a wheelchair mounted robotic arm that can lift higher payloads and its primary aim is to assist caregivers of people who cannot independently transfer from their electric powered wheelchair to other surfaces such as a shower bench or toilet. MEBot is a wheeled robot that has highly reconfigurable kinematics, which allow it to negotiate challenging terrain, such as steep ramps, gravel, or stairs. A risk analysis was performed on all three robots which included a Fault Tree Analysis (FTA) and a Failure Mode Effect Analysis (FMEA) to identify potential risks and inform strategies to mitigate them. Identified risks or PerMMA include dropping sharp or hot objects. Critical risks identified for RATD included tip over, crush hazard, and getting stranded mid-transfer, and risks for MEBot include getting stranded on obstacles and tip over. Lastly, several critical factors, such as early involvement of people with disabilities, to guide future assistive robot design are presented

Space Exploration Robotic Systems - Orbital Manipulation Mechanisms

In the future, orbital space robots will assist humans in space by constructing and maintaining space modules and structures. Robotic manipulators will play essential roles in orbital operations. This work is devoted to the implemented designs of two different orbital manipulation mechanical grippers developed in collaboration with Thales Alenia Space Italy and NASA Jet Propulsion Laboratory – California Institute of Technology. The consensus to a study phase for an IXV (Intermediate eXperimental Vehicle) successor, a preoperational vehicle called SPACE RIDER (Space Rider Reusable Integrated Demonstrator for European Return), has been recently enlarged, as approved during last EU Ministerial Council. One of the main project task consists in developing SPACE RIDER to conduct on orbit servicing activity with no docking. SPACE RIDER would be provided with a robotic manipulator system (arm and gripper) able to transfer cargos, such as scientific payloads, from low Earth orbiting platforms to SPACE RIDER cargo bay. The platform is a part of a space tug designed to move small satellites and other payloads from Low Earth Orbit (LEO) to Geosynchronous Equatorial Orbit (GEO) and viceversa. The assumed housing cargo bay requirements in terms of volume (<100l) and mass (<50kg) combined with the required overall arm dimensions (4m length), and mass of the cargo (5-30kg) force to developing an innovative robotic manipulator with the task-oriented end effector. It results in a seven degree-of-freedom arm to ensure a high degree of dexterity and a dedicate end-effector designed to grasp the cargo interface. The gripper concept developed consists in a multi-finger hand able to lock both translational and rotational cargo degrees of freedom through an innovative underactuation strategy to limit its mass and volume. A configuration study on the cargo handle interface was performed together with some computer aided design models and multibody analysis of the whole system to prove its feasibility. Finally, the concept of system control architecture, the test report and the gripper structural analysis were defined. In order to be able to accurately analyze a sample of Martian soil and to determine if life was present on the red planet, a lot of mission concepts have been formulating to reach Mars and to bring back a terrain sample. NASA JPL has been studying such mission concepts for many years. This concept is made up of three intermediate mission accomplishments. Mars 2020 is the first mission envisioned to collect the terrain sample and to seal it in sample tubes. These sealed sample tubes could be inserted in a spherical envelope named Orbiting Sample (OS). A Mars Ascent Vehicle (MAV) is the notional rocket designed to bring this sample off Mars, and a Rendezvous Orbiting Capture System (ROCS) is the mission conceived to bring this sample back to Earth through the Earth Entry Vehicle (EEV). MOSTT is the technical work study to create new concepts able to capture and reorient an OS. This maneuver is particularly important because we do not know an OS incoming orientation and we need to be able to capture, to reorient it (2 rotational degrees of freedom), and to retain an OS (3 translational degrees of freedom and 2 rotational ones). Planetary protection requirements generate a need to enclose an OS in two shells and to seal it through a process called Break-The-Chain (BTC). Considering the EEV would return back to Earth, the tubes orientation and position have to be known in detail to prevent any possible damage during the Earth hard landing (acceleration of ∼1300g). Tests and analysis report that in order for the hermetic seals of the sample tubes to survive the impact, they should be located above an OS equator. Due to other system uncertainties an OS presents the potential requirement to be properly reoriented before being inserted inside the EEV. Planetary protection issues and landing safety are critical mission points and provide potential strict requirements to MOSTT system configuration. This task deals with the concept, design, and testbed realization of an innovative electro-mechanical system to reorient an OS consistent with all the necessary potential requirements. One of these electro-mechanical systems consists of a controlled-motorized wiper that explores all an OS surface until it engages with a pin on an OS surface and brings it to the final home location reorienting an OS. This mechanism is expected to be robust to the incoming OS orientation and to reorient it to the desired position using only one degree of freedom rotational actuator

Heterogeneous Robot Swarm – Hardware Design and Implementation

Swarm robotics is one the most fascinating, new research areas in the field of robotics, and one of it's grand challenge is the design of swarm robots that are both heterogeneous and self-sufficient. This can be crucial for robots exposed to environments that are unstructured or not easily accessible for a human operator, such as a collapsed building, the deep sea, or the surface of another planet. In Swarm robotics; self-assembly, self-reconfigurability and self-replication are among the most important characteristics as they can add extra capabilities and functionality to the robots besides the robustness, flexibility and scalability. Developing a swarm robot system with heterogeneity and larger behavioral repertoire is addressed in this work. This project is a comprehensive study of the hardware architecture of the homogeneous robot swarm and several problems related to the important aspects of robot's hardware, such as: sensory units, communication among the modules, and hardware components. Most of the hardware platforms used in the swarm robot system are homogeneous and use centralized control architecture for task completion. The hardware architecture is designed and implemented for UB heterogeneous robot swarm with both decentralized and centralized control, depending on the task requirement. Each robot in the UB heterogeneous swarm is equipped with different sensors, actuators, microcontroller and communication modules, which makes them distinct from each other from a hardware point of view. The methodology provides detailed guidelines in designing and implementing the hardware architecture of the heterogeneous UB robot swarm with plug and play approach. We divided the design module into three main categories - sensory modules, locomotion and manipulation, communication and control. We conjecture that the hardware architecture of heterogeneous swarm robots implemented in this work is the most sophisticated and modular design to date

Distributed framework for a multi-purpose household robotic arm

Projecte final de carrera fet en col.laboració amb l'Institut de Robòtica i Informàtica IndustrialThe concept of household robotic servants has been in our mind for ages, and domestic appliances are far more robotised than they used to be. At present, manufacturers are starting to introduce small, household human-interactive robots to the market. Any human-interactive device has safety, endurability and simplicity constraints, which are especially strict when it comes to robots. Indeed, we are still far from a multi-purpose intelligent household robot, but human-interactive robots and arti cial intelligence research has evolved considerably, demonstration prototypes are a proof of what can be done. This project contributes to the research in humaninteractive robots, as the robotic arm and hand used are specially designed for human-interactive applications. The present study provides a distributed framework for an arm and a hand devices based on the robotics YARP protocol using the WAMTM arm and the BarrettHandTM as well as a basic modular client application complemented with vision. Firstly, two device drivers and a network interface are designed and implemented to control the WAMTM arm and the BarrettHandTM from the network. The drivers allow abstract access to each device, providing three ports: command requests port, state requests port and asynchronous replies port. Secondly, each driver is then encapsulated by YARP devices publishing realtime monitoring feedback and motion control to the network through what is called a Network wrapper. In particular, the network wrapper for the WAMTM arm and BarrettHandTM provides a state port, command port, Remote Procedure Call (RPC) port and an asynchronous noti cations port. The state port provides the WAMTM position and orientation feedback at 50 Hz, which represents a maximum blindness of one centimetre. This rst part of the project sets the foundations of a distributed, complete robot, whose design enables processing and power payload to be shared by di erent workstations. Moreover, users are able to work with the robot remotely over Ethernet and Wireless through a clear, understandable local interface within YARP. In addition to the distributed robotic framework provided, a client software framework with vision is also supplied. The client framework establishes a general software shell for further development and is organized in the basic, separate robotic branches: control, vision and plani cation. The vision module supports distributed image grabbing on mobile robotics, and shared-memory for xed, local vision. In order to incorporate environment interaction and robot autonomy with the planner, hand-eye transformation matrices have been obtained to perform object grasping and manipulation. The image processing is based on OpenCV libraries and provides object recognition with Scale Invariant Feature Transform (SIFT) features matching, Hough transform and polygon approximation algorithms. Grasping and path planning use pre-de ned grasps which take into account the size, shape and orientation of the target objects. The proof-of-concept applications feature a household robotic arm with the ability to tidy randomly distributed common kitchen objects to speci ed locations, with robot real-time monitoring and basic control. The device modularity introduced in this project philosophy of decoupling communication, device local access and the components, was successful. Thanks to the abstract access and decoupling, the demonstration applications provided were easily deployed to test the arm's performance and its remote control and monitorization. Moreover, both resultant frameworks are arm-independent and the design is currently being adopted by other projects' devices within the IRI

Tightly-coupled manipulation pipelines: Combining traditional pipelines and end-to-end learning

Traditionally, robot manipulation tasks are solved by engineering solutions in a modular fashion --- typically consisting of object detection, pose estimation, grasp planning, motion planning, and finally run a control algorithm to execute the planned motion. This traditional approach to robot manipulation separates the hard problem of manipulation into several self-contained stages, which can be developed independently, and gives interpretable outputs at each stage of the pipeline. However, this approach comes with a plethora of issues, most notably, their generalisability to a broad range of tasks; it is common that as tasks get more difficult, the systems become increasingly complex. To combat the flaws of these systems, recent trends have seen robots visually learning to predict actions and grasp locations directly from sensor input in an end-to-end manner using deep neural networks, without the need to explicitly model the in-between modules. This thesis investigates a sample of methods, which fall somewhere on a spectrum from pipelined to fully end-to-end, which we believe to be more advantageous for developing a general manipulation system; one that could eventually be used in highly dynamic and unpredictable household environments. The investigation starts at the far end of the spectrum, where we explore learning an end-to-end controller in simulation and then transferring to the real world by employing domain randomisation, and finish on the other end, with a new pipeline, where the individual modules bear little resemblance to the "traditional" ones. The thesis concludes with a proposition of a new paradigm: Tightly-coupled Manipulation Pipelines (TMP). Rather than learning all modules implicitly in one large, end-to-end network or conversely, having individual, pre-defined modules that are developed independently, TMPs suggest taking the best of both world by tightly coupling actions to observations, whilst still maintaining structure via an undefined number of learned modules, which do not have to bear any resemblance to the modules seen in "traditional" systems.Open Acces