Survey of Visual and Force/Tactile Control of Robots for Physical Interaction in Spain

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors

Control of Redundant Joint Structures Using Image Information During the Tracking of Non-Smooth Trajectories

Visual information is increasingly being used in a great number of applications in order to perform the guidance of joint structures. This paper proposes an image-based controller which allows the joint structure guidance when its number of degrees of freedom is greater than the required for the developed task. In this case, the controller solves the redundancy combining two different tasks: the primary task allows the correct guidance using image information, and the secondary task determines the most adequate joint structure posture solving the possible joint redundancy regarding the performed task in the image space. The method proposed to guide the joint structure also employs a smoothing Kalman filter not only to determine the moment when abrupt changes occur in the tracked trajectory, but also to estimate and compensate these changes using the proposed filter. Furthermore, a direct visual control approach is proposed which integrates the visual information provided by this smoothing Kalman filter. This last aspect permits the correct tracking when noisy measurements are obtained. All the contributions are integrated in an application which requires the tracking of the faces of Asperger children

Safe cooperation between human operators and visually controlled industrial manipulators

Industrial tasks can be improved substantially by making humans and robots collaborate in the same workspace. The main goal of this chapter is the development of a human-robot interaction system which enables this collaboration and guarantees the safety of the human operator. This system is composed of two subsystems: the human tracking system and the robot control system. The human tracking system deals with the precise real-time localization of the human operator in the industrial environment. It is composed of two systems: an inertial motion capture system and an Ultra-WideBand localization system. The robot control system is based on visual servoing. A safety behaviour which stops the normal path tracking of the robot is performed when the robot and the human are too close. This safety behaviour has been implemented through a multi-threaded software architecture in order to share information between both systems. Thereby, the localization measurements obtained by the human tracking system are processed by the robot control system to compute the minimum human-robot distance and determine if the safety behaviour must be activated.Spanish Ministry of Science and Innovation and the Spanish Ministry of Education through the projects DPI2005-06222 and DPI2008-02647 and the grant AP2005-1458

Overview of some Command Modes for Human-Robot Interaction Systems

Interaction and command modes as well as their combination are essential features of modern and futuristic robotic systems interacting with human beings in various dynamical environments. This paper presents a synthetic overview concerning the most command modes used in Human-Robot Interaction Systems (HRIS). It includes the first historical command modes which are namely tele-manipulation, off-line robot programming, and traditional elementary teaching by demonstration. It then introduces the most recent command modes which have been fostered later on by the use of artificial intelligence techniques implemented on more powerful computers. In this context, we will consider specifically the following modes: interactive programming based on the graphical-user-interfaces, voice-based, pointing-on-image-based, gesture-based, and finally brain-based commands.info:eu-repo/semantics/publishedVersio

Ultra high frequency (UHF) radio-frequency identification (RFID) for robot perception and mobile manipulation

Personal robots with autonomy, mobility, and manipulation capabilities have the potential to dramatically improve quality of life for various user populations, such as older adults and individuals with motor impairments. Unfortunately, unstructured environments present many challenges that hinder robot deployment in ordinary homes. This thesis seeks to address some of these challenges through a new robotic sensing modality that leverages a small amount of environmental augmentation in the form of Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags. Previous research has demonstrated the utility of infrastructure tags (affixed to walls) for robot localization; in this thesis, we specifically focus on tagging objects. Owing to their low-cost and passive (battery-free) operation, users can apply UHF RFID tags to hundreds of objects throughout their homes. The tags provide two valuable properties for robots: a unique identifier and receive signal strength indicator (RSSI, the strength of a tag's response). This thesis explores robot behaviors and radio frequency perception techniques using robot-mounted UHF RFID readers that enable a robot to efficiently discover, locate, and interact with UHF RFID tags applied to objects and people of interest. The behaviors and algorithms explicitly rely on the robot's mobility and manipulation capabilities to provide multiple opportunistic views of the complex electromagnetic landscape inside a home environment. The electromagnetic properties of RFID tags change when applied to common household objects. Objects can have varied material properties, can be placed in diverse orientations, and be relocated to completely new environments. We present a new class of optimization-based techniques for RFID sensing that are robust to the variation in tag performance caused by these complexities. We discuss a hybrid global-local search algorithm where a robot employing long-range directional antennas searches for tagged objects by maximizing expected RSSI measurements; that is, the robot attempts to position itself (1) near a desired tagged object and (2) oriented towards it. The robot first performs a sparse, global RFID search to locate a pose in the neighborhood of the tagged object, followed by a series of local search behaviors (bearing estimation and RFID servoing) to refine the robot's state within the local basin of attraction. We report on RFID search experiments performed in Georgia Tech's Aware Home (a real home). Our optimization-based approach yields superior performance compared to state of the art tag localization algorithms, does not require RF sensor models, is easy to implement, and generalizes to other short-range RFID sensor systems embedded in a robot's end effector. We demonstrate proof of concept applications, such as medication delivery and multi-sensor fusion, using these techniques. Through our experimental results, we show that UHF RFID is a complementary sensing modality that can assist robots in unstructured human environments.PhDCommittee Chair: Kemp, Charles C.; Committee Member: Abowd, Gregory; Committee Member: Howard, Ayanna; Committee Member: Ingram, Mary Ann; Committee Member: Reynolds, Matt; Committee Member: Tentzeris, Emmanoui

Improving detection of surface discontinuities in visual-force control systms

In this paper, a new approach to detect surface discontinuities in a visual–force control task is described. A task which consists in tracking a surface using visual–force information is shown. In this task, in order to reposition the robot tool with respect to the surface it is necessary to determine the surface discontinuities. This paper describes a new method to detect surface discontinuities employing sensorial information obtained from a force sensor, a camera and structured light. This method has proved to be more robust than previous systems even in situations where high frictions occur

Visual guidance of unmanned aerial manipulators

The ability to fly has greatly expanded the possibilities for robots to perform surveillance, inspection or map generation tasks. Yet it was only in recent years that research in aerial robotics was mature enough to allow active interactions with the environment. The robots responsible for these interactions are called aerial manipulators and usually combine a multirotor platform and one or more robotic arms. The main objective of this thesis is to formalize the concept of aerial manipulator and present guidance methods, using visual information, to provide them with autonomous functionalities. A key competence to control an aerial manipulator is the ability to localize it in the environment. Traditionally, this localization has required external infrastructure of sensors (e.g., GPS or IR cameras), restricting the real applications. Furthermore, localization methods with on-board sensors, exported from other robotics fields such as simultaneous localization and mapping (SLAM), require large computational units becoming a handicap in vehicles where size, load, and power consumption are important restrictions. In this regard, this thesis proposes a method to estimate the state of the vehicle (i.e., position, orientation, velocity and acceleration) by means of on-board, low-cost, light-weight and high-rate sensors. With the physical complexity of these robots, it is required to use advanced control techniques during navigation. Thanks to their redundancy on degrees-of-freedom, they offer the possibility to accomplish not only with mobility requirements but with other tasks simultaneously and hierarchically, prioritizing them depending on their impact to the overall mission success. In this work we present such control laws and define a number of these tasks to drive the vehicle using visual information, guarantee the robot integrity during flight, and improve the platform stability or increase arm operability. The main contributions of this research work are threefold: (1) Present a localization technique to allow autonomous navigation, this method is specifically designed for aerial platforms with size, load and computational burden restrictions. (2) Obtain control commands to drive the vehicle using visual information (visual servo). (3) Integrate the visual servo commands into a hierarchical control law by exploiting the redundancy of the robot to accomplish secondary tasks during flight. These tasks are specific for aerial manipulators and they are also provided. All the techniques presented in this document have been validated throughout extensive experimentation with real robotic platforms.La capacitat de volar ha incrementat molt les possibilitats dels robots per a realitzar tasques de vigilància, inspecció o generació de mapes. Tot i això, no és fins fa pocs anys que la recerca en robòtica aèria ha estat prou madura com per començar a permetre interaccions amb l’entorn d’una manera activa. Els robots per a fer-ho s’anomenen manipuladors aeris i habitualment combinen una plataforma multirotor i un braç robòtic. L’objectiu d’aquesta tesi és formalitzar el concepte de manipulador aeri i presentar mètodes de guiatge, utilitzant informació visual, per dotar d’autonomia aquest tipus de vehicles. Una competència clau per controlar un manipulador aeri és la capacitat de localitzar-se en l’entorn. Tradicionalment aquesta localització ha requerit d’infraestructura sensorial externa (GPS, càmeres IR, etc.), limitant així les aplicacions reals. Pel contrari, sistemes de localització exportats d’altres camps de la robòtica basats en sensors a bord, com per exemple mètodes de localització i mapejat simultànis (SLAM), requereixen de gran capacitat de còmput, característica que penalitza molt en vehicles on la mida, pes i consum elèctric son grans restriccions. En aquest sentit, aquesta tesi proposa un mètode d’estimació d’estat del robot (posició, velocitat, orientació i acceleració) a partir de sensors instal·lats a bord, de baix cost, baix consum computacional i que proporcionen mesures a alta freqüència. Degut a la complexitat física d’aquests robots, és necessari l’ús de tècniques de control avançades. Gràcies a la seva redundància de graus de llibertat, aquests robots ens ofereixen la possibilitat de complir amb els requeriments de mobilitat i, simultàniament, realitzar tasques de manera jeràrquica, ordenant-les segons l’impacte en l’acompliment de la missió. En aquest treball es presenten aquestes lleis de control, juntament amb la descripció de tasques per tal de guiar visualment el vehicle, garantir la integritat del robot durant el vol, millorar de l’estabilitat del vehicle o augmentar la manipulabilitat del braç. Aquesta tesi es centra en tres aspectes fonamentals: (1) Presentar una tècnica de localització per dotar d’autonomia el robot. Aquest mètode està especialment dissenyat per a plataformes amb restriccions de capacitat computacional, mida i pes. (2) Obtenir les comandes de control necessàries per guiar el vehicle a partir d’informació visual. (3) Integrar aquestes accions dins una estructura de control jeràrquica utilitzant la redundància del robot per complir altres tasques durant el vol. Aquestes tasques son específiques per a manipuladors aeris i també es defineixen en aquest document. Totes les tècniques presentades en aquesta tesi han estat avaluades de manera experimental amb plataformes robòtiques real

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles