Riistvarapaindlik ROSi tarkvarapakett tööstuslike robotite mööndlikuks juhtimiseks

Industrial robotics today is moving towards using lightweight collaborative robots to make it possible for small and medium sized enterprises to integrate robots in their manufacturing environment. However, there is still very few collaborative robots seen in the industry and the main reason is that programming of the robot is still too expensive and time-consuming, since there are too few ready solutions available today for controlling co-robots. The solution would be more available open source, maintainable, extendable and usable high-quality code for controlling co-robots. This thesis concentrates on developing such complete software bundle on ROS for compliant control for industrial collaborative manipulators

Motion Primitives and Planning for Robots with Closed Chain Systems and Changing Topologies

When operating in human environments, a robot should use predictable motions that allow humans to trust and anticipate its behavior. Heuristic search-based planning offers predictable motions and guarantees on completeness and sub-optimality of solutions. While search-based planning on motion primitive-based (lattice-based) graphs has been used extensively in navigation, application to high-dimensional state-spaces has, until recently, been thought impractical. This dissertation presents methods we have developed for applying these graphs to mobile manipulation, specifically for systems which contain closed chains. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees-of-freedom but adds complexity in terms of constraints in the high-dimensional state-space. We exploit the dimensionality reduction inherent in closed kinematic chains to get efficient search-based planning. Our planner handles changing topologies (switching between open and closed-chains) in a single plan, including what transitions to include and when to include them. Thus, we can leverage existing results for search-based planning for open chains, combining open and closed chain manipulation planning into one framework. Proofs regarding the framework are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We apply this framework to planning for opening and navigating through non-spring and spring-loaded doors using a Willow Garage PR2. The framework motivates our approaches to the Atlas humanoid robot from Boston Dynamics for both stationary manipulation and quasi-static walking, as a closed chain is formed when both feet are on the ground

Design and implementation of robotic devices for physical therapy of distal upper extremity

According to statistics of World Health Organization, hand injuries count for 1/3 of all injuries with more than one million emergency cases annually. Physical rehabilitation accounts for most of the recovery experienced by patients suffering from hand injury. Robotic devices decrease the cost of therapy while providing repetitive exercises with quantitative measurements. In this study, we present the design and implementation of two robotic devices for hand therapy. After kinematic type selection ensuring safety, ergonomics and adjustability; both of the devices are optimally dimensioned to achieve best kinematic and dynamic performance. The primary use for the first device is to assist flexion/extension motions of a finger within its full range, in a natural and coordinated manner, while keeping the tendon tension within acceptable limits to avoid rupture of the suture. The second device is designed for forearm/wrist and grasp therapy of a neurologically injured human arm and hand. Emphasizing the importance of coordinated movements of the wrist and the hand while performing activities of daily living (ADL) tasks, the device possesses 3 degrees of freedom and is designed to assist abduction/adduction and palmar/dorsal flexion of the wrist or pronation/supination of the forearm, concurrently with the grasping and releasing movements of the hand. Thanks to its modular, interchangeable end effectors, the device supports ADL exercises. Both devices are built and experimentally characterized. Human subject experiments and usability tests have been conducted for the devices and the efficacy of devices to deliver desired wrist and hand therapies have been demonstrated

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

Design and Experimental Evaluation of a Hybrid Wheeled-Leg Exploration Rover in the Context of Multi-Robot Systems

With this dissertation, the electromechanic design, implementation, locomotion control, and experimental evaluation of a novel type of hybrid wheeled-leg exploration rover are presented. The actively articulated suspension system of the rover is the basis for advanced locomotive capabilities of a mobile exploration robot. The developed locomotion control system abstracts the complex kinematics of the suspension system and provides platform control inputs usable by autonomous behaviors or human remote control. Design and control of the suspension system as well as experimentation with the resulting rover are in the focus of this thesis. The rover is part of a heterogeneous modular multi-robot exploration system with an aspired sample return mission to the lunar south pole or currently hard-to-access regions on Mars. The multi-robot system pursues a modular and reconfigurable design methodology. It combines heterogeneous robots with different locomotion capabilities for enhanced overall performance. Consequently, the design of the multi-robot system is presented as the frame of the rover developments. The requirements for the rover design originating from the deployment in a modular multi-robot system are accentuated and summarized in this thesis

Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

Belief-Space Planning for Resourceful Manipulation and Mobility

Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use of series elastic actuators (SEAs) to improve manipulation capabilities and support safer operation around humans. The resourcefulness of these robots is complemented with a belief-space planning framework that enables task-driven action selection in the context of the partially observable environment. The framework uses a compact but expressive state representation based on object models. We extend an existing affordance-based object model, called an aspect transition graph (ATG), with geometric information. This enables object-centric modeling of features and actions, making the model much more expressive without increasing the complexity. A novel task representation enables the belief-space planner to perform general object-centric tasks ranging from recognition to manipulation of objects. The approach supports the efficient handling of multi-object scenes. The combination of the physical platform and the planning framework are evaluated in two novel, challenging, partially observable planning domains. The ARcube domain provides a large population of objects that are highly ambiguous. Objects can only be differentiated using multi-modal sensor information and manual interactions. In the dexterous mobility domain, a robot can employ multiple mobility modes to complete navigation tasks under a variety of possible environment constraints. The performance of the proposed approach is evaluated using experiments in simulation and on a real robot