40 research outputs found
Legged Robots for Object Manipulation: A Review
Legged robots can have a unique role in manipulating objects in dynamic,
human-centric, or otherwise inaccessible environments. Although most legged
robotics research to date typically focuses on traversing these challenging
environments, many legged platform demonstrations have also included "moving an
object" as a way of doing tangible work. Legged robots can be designed to
manipulate a particular type of object (e.g., a cardboard box, a soccer ball,
or a larger piece of furniture), by themselves or collaboratively. The
objective of this review is to collect and learn from these examples, to both
organize the work done so far in the community and highlight interesting open
avenues for future work. This review categorizes existing works into four main
manipulation methods: object interactions without grasping, manipulation with
walking legs, dedicated non-locomotive arms, and legged teams. Each method has
different design and autonomy features, which are illustrated by available
examples in the literature. Based on a few simplifying assumptions, we further
provide quantitative comparisons for the range of possible relative sizes of
the manipulated object with respect to the robot. Taken together, these
examples suggest new directions for research in legged robot manipulation, such
as multifunctional limbs, terrain modeling, or learning-based control, to
support a number of new deployments in challenging indoor/outdoor scenarios in
warehouses/construction sites, preserved natural areas, and especially for home
robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical
Engineerin
Motion planning using synergies : application to anthropomorphic dual-arm robots
Motion planning is a traditional field in robotics, but new problems are nevertheless incessantly appearing, due to continuous advances in the robot developments. In order to solve these new problems, as well as to improve the existing solutions to classical problems, new approaches are being proposed. A paradigmatic case is the humanoid robotics, since the advances done in this field require motion planners not only to look efficiently for an optimal solution in the classic way, i.e. optimizing consumed energy or time in the plan execution, but also looking for human-like solutions, i.e. requiring the robot movements to be similar to those of the human beings. This anthropomorphism in the robot motion is desired not only for aesthetical reasons, but it is also needed to allow a better and safer human-robot collaboration: humans can predict more easily anthropomorphic robot motions thus avoiding collisions and enhancing the collaboration with the robot. Nevertheless, obtaining a satisfactory performance of these anthropomorphic robotic systems requires the automatic planning of the movements, which is still an arduous and non-evident task since the complexity of the planning problem increases exponentially with the number of degrees of freedom of the robotic system.
This doctoral thesis tackles the problem of planning the motions of dual-arm anthropomorphic robots (optionally with mobile base). The main objective is twofold: obtaining robot motions both in an efficient and in a human-like fashion at the same time. Trying to mimic the human movements while reducing the complexity of the search space for planning purposes leads to the concept of synergies, which could be conceptually defined as correlations (in the joint configuration space as well as in the joint velocity space) between the degrees of freedom of the system. This work proposes new sampling-based motion-planning procedures that exploit the concept of synergies, both in the configuration and velocity space, coordinating the movements of the arms, the hands and the mobile base of mobile anthropomorphic dual-arm robots.La planificaciรณn de movimientos es un campo tradicional de la robรณtica, sin embargo aparecen incesantemente nuevos problemas debido a los continuos avances en el desarrollo de los robots. Para resolver esos nuevos problemas, asรญ como para mejorar las soluciones existentes a los problemas clรกsicos, se estรกn proponiendo nuevos enfoques. Un caso paradigmรกtico es la robรณtica humanoide, ya que los avances realizados en este campo requieren que los algoritmos planificadores de movimientos no sรณlo encuentren eficientemente una soluciรณn รณptima en el sentido clรกsico, es decir, optimizar el consumo de energรญa o el tiempo de ejecuciรณn de la trayectoria; sino que tambiรฉn busquen soluciones con apariencia humana, es decir, que el movimiento del robot sea similar al del ser humano. Este antropomorfismo en el movimiento del robot se busca no sรณlo por razones estรฉticas, sino porque tambiรฉn es necesario para permitir una colaboraciรณn mejor y mรกs segura entre el robot y el operario: el ser humano puede predecir con mayor facilidad los movimientos del robot si รฉstos son antropomรณrficos, evitando asรญ las colisiones y mejorando la colaboraciรณn humano robot. Sin embargo, para obtener un desempeรฑo satisfactorio de estos sistemas robรณticos antropomรณrficos se requiere una planificaciรณn automรกtica de sus movimientos, lo que sigue siendo una tarea ardua y poco evidente, ya que la complejidad del problema aumenta exponencialmente con el nรบmero de grados de libertad del sistema robรณtico. Esta tesis doctoral aborda el problema de la planificaciรณn de movimientos en robots antropomorfos bibrazo (opcionalmente con base mรณvil). El objetivo aquรญ es doble: obtener movimientos robรณticos de forma eficiente y, a la vez, que tengan apariencia humana. Intentar imitar los movimientos humanos mientras a la vez se reduce la complejidad del espacio de bรบsqueda conduce al concepto de sinergias, que podrรญan definirse conceptualmente como correlaciones (tanto en el espacio de configuraciones como en el espacio de velocidades de las articulaciones) entre los distintos grados de libertad del sistema. Este trabajo propone nuevos procedimientos de planificaciรณn de movimientos que explotan el concepto de sinergias, tanto en el espacio de configuraciones como en el espacio de velocidades, coordinando asรญ los movimientos de los brazos, las manos y la base mรณvil de robots mรณviles, bibrazo y antropomรณrficos.Postprint (published version
Motion synthesis for high degree-of-freedom robots in complex and changing environments
The use of robotics has recently seen significant growth in various domains such as
unmanned ground/underwater/aerial vehicles, smart manufacturing, and humanoid
robots. However, one of the most important and essential capabilities required for
long term autonomy, which is the ability to operate robustly and safely in real-world
environments, in contrast to industrial and laboratory setup is largely missing. Designing
robots that can operate reliably and efficiently in cluttered and changing
environments is non-trivial, especially for high degree-of-freedom (DoF) systems, i.e.
robots with multiple actuators. On one hand, the dexterity offered by the kinematic
redundancy allows the robot to perform dexterous manipulation tasks in complex
environments, whereas on the other hand, such complex system also makes controlling
and planning very challenging. To address such two interrelated problems, we
exploit robot motion synthesis from three perspectives that feed into each other: end-pose
planning, motion planning and motion adaptation. We propose several novel
ideas in each of the three phases, using which we can efficiently synthesise dexterous
manipulation motion for fixed-base robotic arms, mobile manipulators, as well as
humanoid robots in cluttered and potentially changing environments.
Collision-free inverse kinematics (IK), or so-called end-pose planning, a key prerequisite
for other modules such as motion planning, is an important and yet unsolved
problem in robotics. Such information is often assumed given, or manually provided
in practice, which significantly limiting high-level autonomy. In our research, by using
novel data pre-processing and encoding techniques, we are able to efficiently
search for collision-free end-poses in challenging scenarios in the presence of uneven
terrains.
After having found the end-poses, the motion planning module can proceed. Although
motion planning has been claimed as well studied, we find that existing algorithms
are still unreliable for robust and safe operations in real-world applications,
especially when the environment is cluttered and changing. We propose a novel
resolution complete motion planning algorithm, namely the Hierarchical Dynamic
Roadmap, that is able to generate collision-free motion trajectories for redundant
robotic arms in extremely complicated environments where other methods would fail.
While planning for fixed-base robotic arms is relatively less challenging, we also investigate
into efficient motion planning algorithms for high DoF (30 - 40) humanoid
robots, where an extra balance constraint needs to be taken into account. The result
shows that our method is able to efficiently generate collision-free whole-body trajectories
for different humanoid robots in complex environments, where other methods
would require a much longer planning time.
Both end-pose and motion planning algorithms compute solutions in static environments,
and assume the environments stay static during execution. While human
and most animals are incredibly good at handling environmental changes, the state-of-the-art robotics technology is far from being able to achieve such an ability. To
address this issue, we propose a novel state space representation, the Distance Mesh
space, in which the robot is able to remap the pre-planned motion in real-time and
adapt to environmental changes during execution.
By utilizing the proposed end-pose planning, motion planning and motion adaptation
techniques, we obtain a robotic framework that significantly improves the
level of autonomy. The proposed methods have been validated on various state-of-the-art robot platforms, such as UR5 (6-DoF fixed-base robotic arm), KUKA LWR
(7-DoF fixed-base robotic arm), Baxter (14-DoF fixed-base bi-manual manipulator),
Husky with Dual UR5 (15-DoF mobile bi-manual manipulator), PR2 (20-DoF mobile
bi-manual manipulator), NASA Valkyrie (38-DoF humanoid) and many others, showing
that our methods are truly applicable to solve high dimensional motion planning
for practical problems
Humanoid Robots
For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion
๊ตฌ์กฐ๋ก๋ด์ ์ํ ๊ฐ๊ฑดํ ๊ณ์ธต์ ๋์ ๊ณํ ๋ฐ ์ ์ด
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2021.8. ๋ฐ์ข
์ฐ.Over the last several years, robotics has experienced a striking development, and a new generation of robots has emerged that shows great promise in being able to accomplish complex tasks associated with human behavior. Nowadays the objectives of the robots are no longer restricted to the automaton in the industrial process but are changing into explorers for hazardous, harsh, uncooperative, and extreme environments. As these robots usually operate in dynamic and unstructured environments, they should be robust, adaptive, and reactive under various changing operation conditions.
We propose online hierarchical optimization-based planning and control methodologies for a rescue robot to execute a given mission in such a highly unstructured environment. A large number of degrees of freedom is provided to robots in order to achieve diverse kinematic and dynamic tasks. However, accomplishing such multiple objectives renders on-line reactive motion planning and control problems more difficult to solve due to the incompatible tasks. To address this problem, we exploit a hierarchical structure to precisely resolve conflicts by creating a priority in which every task is achieved as much as possible according to the levels.
In particular, we concentrate on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face of singularity. As robotic systems with real-time motion planners or controllers often execute unrehearsed missions, a desired task cannot always be driven to a singularity free configuration.
We develop a generic solver for regularized hierarchical quadratic programming without resorting to any off-the-shelf QP solver to take advantage of the null-space projections for computational efficiency. Therefore, the underlying principles are thoroughly investigated. The robust optimal solution is obtained under both equality and inequality tasks or constraints while addressing all problems resulting from the regularization. Especially as a singular value decomposition centric approach is leveraged, all hierarchical solutions and Lagrange multipliers for properly handling the inequality constraints are analytically acquired in a recursive procedure. The proposed algorithm works fast enough to be used as a practical means of real-time control system, so that it can be used for online motion planning, motion control, and interaction force control in a single hierarchical optimization.
Core system design concepts of the rescue robot are presented. The goals of the robot are to safely extract a patient and to dispose a dangerous object instead of humans. The upper body is designed humanoid in form with replaceable modularized dual arms. The lower body is featured with a hybrid tracked and legged mobile platform to simultaneously acquire versatile manipulability and all-terrain mobility. Thus, the robot can successfully execute a driving task, dangerous object manipulation, and casualty extraction missions by changing the pose and modularized equipments in an optimized manner.
Throughout the dissertation, all proposed methods are validated through extensive numerical simulations and experimental tests. We highlight precisely how the rescue robot can execute a casualty extraction and a dangerous object disposal mission both in indoor and outdoor environments that none of the existing robots has performed.์ต๊ทผ์ ๋ฑ์ฅํ ์๋ก์ด ์ธ๋์ ๋ก๋ด์ ๊ธฐ์กด์๋ ์ธ๊ฐ๋ง์ด ํ ์ ์์๋ ๋ณต์กํ ์ผ์ ๋ก๋ด ๋ํ ์ํํ ์ ์์์ ๋ณด์ฌ์ฃผ์๋ค. ํนํ DARPA Robotics Challenge๋ฅผ ํตํด ์ด๋ฌํ ์ฌ์ค์ ์ ํ์ธํ ์ ์์ผ๋ฉฐ, ์ด ๋ก๋ด๋ค์ ๊ณต์ฅ๊ณผ ๊ฐ์ ์ ํํ๋ ํ๊ฒฝ์์ ์๋ํ๋ ์ผ์ ๋ฐ๋ณต์ ์ผ๋ก ์ํํ๋ ์๋ฌด์์ ๋ ๋์๊ฐ ๊ทนํ์ ํ๊ฒฝ์์ ์ธ๊ฐ์ ๋์ ํ์ฌ ์ํํ ์๋ฌด๋ฅผ ์ํํ ์ ์๋ ๋ฐฉํฅ์ผ๋ก ๋ฐ์ ํ๊ณ ์๋ค. ๊ทธ๋์ ์ฌ๋๋ค์ ์ฌ๋ํ๊ฒฝ์์ ์์ ํ๊ณ ์์ ์ ์ ํ๊ฒ ๋์ํ ์ ์๋ ์ฌ๋ฌ ๊ฐ์ง ๋์ ์ค์์ ์คํ ๊ฐ๋ฅ์ฑ์ด ๋์ ๋์ฒ ๋ฐฉ์์ผ๋ก ๋ก๋ด์ ์๊ฐํ๊ฒ ๋์๋ค. ํ์ง๋ง ์ด๋ฌํ ๋ก๋ด์ ๋์ ์ผ๋ก ๋ณํํ๋ ๋น์ ํ ํ๊ฒฝ์์ ์๋ฌด๋ฅผ ์ํํ ์ ์์ด์ผ ํ๊ธฐ ๋๋ฌธ์ ๋ถํ์ค์ฑ์ ๋ํด ๊ฐ๊ฑดํด์ผํ๊ณ , ๋ค์ํ ํ๊ฒฝ ์กฐ๊ฑด์์ ๋ฅ๋์ ์ผ๋ก ๋ฐ์์ ํ ์ ์์ด์ผ ํ๋ค. ๋ณธ ํ์๋
ผ๋ฌธ์์๋ ๋ก๋ด์ด ๋น์ ํ ํ๊ฒฝ์์ ๊ฐ๊ฑดํ๋ฉด์๋ ์ ์์ ์ผ๋ก ๋์ํ ์ ์๋ ์ค์๊ฐ ์ต์ ํ ๊ธฐ๋ฐ์ ๋์ ๊ณํ ๋ฐ ์ ์ด ๋ฐฉ๋ฒ๊ณผ ๊ตฌ์กฐ ๋ก๋ด์ ์ค๊ณ ๊ฐ๋
์ ์ ์ํ๊ณ ์ ํ๋ค.
์ธ๊ฐ์ ๋ง์ ์์ ๋๋ฅผ ๊ฐ์ง๊ณ ์์ผ๋ฉฐ, ํ๋์ ์ ์ ๋์์ ์์ฑํ ๋ ๋ค์ํ ๊ธฐ๊ตฌํ ํน์ ๋์ญํ์ ํน์ฑ์ ๊ฐ์ง๋ ์ธ๋ถ ๋์ ํน์ ์์
์ ์ ์ํ๊ณ , ์ด๋ฅผ ํจ๊ณผ์ ์ผ๋ก ์ข
ํฉํ ์ ์๋ค. ๊ทธ๋ฆฌ๊ณ ํ์ต์ ํตํด ๊ฐ ๋์ ์์๋ค์ ์ต์ ํํ ๋ฟ๋ง ์๋๋ผ ์ํฉ ์ ๋ฐ๋ผ ๊ฐ ๋์ ์์์ ์ฐ์ ์์๋ฅผ ๋ถ์ฌํ์ฌ ์ด๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๊ฒฐํฉํ๊ฑฐ๋ ๋ถ๋ฆฌํ์ฌ ์ค์๊ฐ์ผ๋ก ์ต์ ์ ๋์์ ์์ฑํ๊ณ ์ ์ดํ๋ค. ์ฆ, ์ํฉ์ ๋ฐ๋ผ ์ค์ํ ๋์์์๋ฅผ ์ฐ์ ์ ์ผ๋ก ์ํํ๊ณ ์ฐ์ ์์๊ฐ ๋ฎ์ ๋์์์๋ ๋ถ๋ถ ํน์ ์ ์ฒด์ ์ผ๋ก ํฌ๊ธฐํ๊ธฐ๋ ํ๋ฉด์ ๋งค์ฐ ์ ์ฐํ๊ฒ ์ ์ฒด ๋์์ ์์ฑํ๊ณ ์ต์ ํ ํ๋ค.
์ธ๊ฐ๊ณผ ๊ฐ์ด ๋ค์์ ๋๋ฅผ ๋ณด์ ํ ๋ก๋ด ๋ํ ๊ธฐ๊ตฌํ๊ณผ ๋์ญํ์ ํน์ฑ์ ๊ฐ์ง๋ ๋ค์ํ ์ธ๋ถ ๋์ ํน์ ์์
์ ์์
๊ณต๊ฐ(task space) ํน์ ๊ด์ ๊ณต๊ฐ(configuration space)์์ ์ ์ํ ์ ์์ผ๋ฉฐ, ์ฐ์ ์์์ ๋ฐ๋ผ ์ด๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๊ฒฐํฉํ์ฌ ์ ์ฒด ๋์์ ์ ์ฑํ๊ณ ์ ์ดํ ์ ์๋ค. ์๋ก ์๋ฆฝํ๊ธฐ ์ด๋ ค์ด ๋ก๋ด์ ๋์ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๋์๋ค ์ฌ์ด์ ์ฐ์ ์์๋ฅผ ๋ถ์ฌํ์ฌ ๊ณ์ธต์ ์์ฑํ๊ณ , ์ด์ ๋ฐ๋ผ ๋ก๋ด์ ์ ์ ๋์์ ๊ตฌํํ๋ ๋ฐฉ๋ฒ์ ์ค๋ซ๋์ ์ฐ๊ตฌ๊ฐ ์งํ๋์ด ์๋ค. ์ด๋ฌํ ๊ณ์ธต์ ์ต์ ํ๋ฅผ ์ด์ฉํ๋ฉด ์ฐ์ ์์๊ฐ ๋์ ๋์๋ถํฐ ์์ฐจ์ ์ผ๋ก ์คํํ์ง๋ง, ์ฐ์ ์์๊ฐ ๋ฎ์ ๋์์์๋ค๋ ๊ฐ๋ฅํ ๋ง์กฑ์ํค๋ ์ต์ ์ ํด๋ฅผ ์ฐพ์ ์ ์๋ค.
ํ์ง๋ง ๊ด์ ์ ๊ตฌ๋ ๋ฒ์์ ๊ฐ์ ๋ถ๋ฑ์์ ์กฐ๊ฑด์ด ํฌํจ๋ ๊ณ์ธต์ ์ต์ ํ ๋ฌธ์ ์์ ํน์ด์ ์ ๋ํ ๊ฐ๊ฑด์ฑ๊น์ง ํ๋ณดํ ์ ์๋ ๋ฐฉ๋ฒ์ ๋ํด์๋ ์์ง๊น์ง ๋ง์ ๋ถ๋ถ์ด ๋ฐ ํ์ง ๋ฐ๊ฐ ์๋ค. ๋ฐ๋ผ์ ๋ณธ ํ์๋
ผ๋ฌธ์์๋ ๋ฑ์๊ณผ ๋ถ๋ฑ์์ผ๋ก ํํ๋๋ ๊ตฌ์์กฐ๊ฑด ํน์ ๋์์์๋ฅผ ๊ณ์ธต์ ์ต์ ํ์ ๋์์ ํฌํจ์ํค๊ณ , ํน์ด์ ์ด ์กด์ฌํ๋๋ผ๋ ๊ฐ๊ฑด์ฑ๊ณผ ์๋ ด์ฑ์ ๋ณด์ฅํ๋ ๊ด์ ๊ณต๊ฐ์์์ ์ต์ ํด๋ฅผ ํ๋ณดํ๋๋ฐ ์ง์คํ๋ค. ์๋ํ๋ฉด ๋น์ ํ ์๋ฌด๋ฅผ ์ํํ๋ ๋ก๋ด์ ์ฌ์ ์ ๊ณํ๋ ๋์์ ์ํํ๋ ๊ฒ์ด ์๋ ๋ณํํ๋ ํ๊ฒฝ์กฐ๊ฑด์ ๋ฐ๋ผ ์ค์๊ฐ์ผ๋ก ๋์์ ๊ณํํ๊ณ ์ ์ดํด์ผ ํ๊ธฐ ๋๋ฌธ์ ํน์ด์ ์ด ์๋ ์์ธ๋ก ๋ก๋ด์ ํญ์ ์ ์ดํ๊ธฐ๊ฐ ์ด๋ ต๋ค. ๊ทธ๋ฆฌ๊ณ ์ด๋ ๊ฒ ํน์ด์ ์ ํํผํ๋ ๋ฐฉํฅ์ผ๋ก ๋ก๋ด์ ์ ์ดํ๋ ๊ฒ์ ๋ก๋ด์ ์ด์ฉ์ฑ์ ์ฌ๊ฐํ๊ฒ ์ ํด์ํฌ ์ ์๋ค. ํน์ด์ ๊ทผ๋ฐฉ์์์ ํด์ ๊ฐ๊ฑด์ฑ์ด ๋ณด์ฅ๋์ง ์์ผ๋ฉด ๋ก๋ด ๊ด์ ์ ๊ณผ๋ํ ์๋ ํน์ ํ ํฌ๊ฐ ๋ฐ์ํ์ฌ ๋ก๋ด์ ์๋ฌด ์ํ์ด ๋ถ๊ฐ๋ฅํ๊ฑฐ๋ ํ๊ฒฝ๊ณผ ๋ก๋ด์ ์์์ ์ด๋ํ ์ ์์ผ๋ฉฐ, ๋์๊ฐ ๋ก๋ด๊ณผ ํจ๊ป ์๋ฌด๋ฅผ ์ํํ๋ ์ฌ๋์๊ฒ ์ํด๋ฅผ ๊ฐํ ์๋ ์๋ค.
ํน์ด์ ์ ๋ํ ๊ฐ๊ฑด์ฑ์ ํ๋ณดํ๊ธฐ ์ํด ์ฐ์ ์์ ๊ธฐ๋ฐ์ ๊ณ์ธต์ ์ต์ ํ์ ์ ๊ทํ (regularization)๋ฅผ ํตํฉํ์ฌ ์ ๊ทํ๋ ๊ณ์ธต์ ์ต์ ํ (RHQP: Regularized Hierarchical Quadratic Program) ๋ฌธ์ ๋ฅผ ๋ค๋ฃฌ๋ค. ๋ถ๋ฑ์์ด ํฌํจ๋ ๊ณ์ธต์ ์ต์ ํ์ ์ ๊ทํ๋ฅผ ๋์์ ๊ณ ๋ คํจ์ผ๋ก์จ ์ผ๊ธฐ๋๋ ๋ง์ ๋ฌธ์ ์ ๋ค์ ํด๊ฒฐํ๊ณ ํด์ ์ต์ ์ฑ๊ณผ ๊ฐ๊ฑด์ฑ์ ํ๋ณดํ ์ ์๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ํนํ ์ธ๋ถ์ ์ต์ ํ ํ๋ก๊ทธ๋จ์ ์ฌ์ฉํ์ง ์๊ณ ์์น์ ์ต์ ํ (numerical optimization) ์ด๋ก ๊ณผ ์ฐ์ ์์์ ๊ธฐ๋ฐ์ ๋๋ ์ฌ์ ์์ ๋ ๋ก๋ด์ ํด์ ๊ธฐ๋ฒ์ ์ด์ฉํ์ฌ ๊ณ์ฐ์ ํจ์จ์ฑ์ ๊ทน๋ํํ ์ ์๋ ์ด์ฐจ ํ๋ก๊ทธ๋จ(quadratic programming)์ ์ ์ํ๋ค. ๋ํ ์ด์ ๋์์ ์ ๊ทํ๋ ๊ณ์ธต์ ์ต์ ํ ๋ฌธ์ ์ ์ด๋ก ์ ๊ตฌ์กฐ๋ฅผ ์ฒ ์ ํ๊ฒ ๋ถ์ํ๋ค. ํนํ ํน์ด๊ฐ ๋ถํด (singular value decomposition)๋ฅผ ํตํด ์ต์ ํด์ ๋ถ๋ฑ์ ์กฐ๊ฑด์ ์ฒ๋ฆฌํ๋๋ฐ ํ์ํ ๋ผ๊ทธ๋์ง ์น์๋ฅผ ์ฌ๊ท์ ์ธ ๋ฐฉ๋ฒ์ผ๋ก ํด์์ ํํ๋ก ๊ตฌํจ์ผ๋ก์จ ๊ณ์ฐ์ ํจ์จ์ฑ์ ์ฆ๋์ํค๊ณ ๋์์ ๋ถ๋ฑ์์ ์กฐ๊ฑด์ ์ค๋ฅ ์์ด ์ ํํ๊ฒ ์ฒ๋ฆฌํ ์ ์๋๋ก ํ์๋ค. ๊ทธ๋ฆฌ๊ณ ์ ๊ทํ๋ ๊ณ์ธต์ ์ต์ ํ๋ฅผ ํ์ ์ด๊น์ง ํ์ฅํ์ฌ ํ๊ฒฝ๊ณผ ๋ก๋ด์ ์์ ํ ์ํธ์์ฉ์ ๋ณด์ฅํ์ฌ ๋ก๋ด์ด ์ ์ ํ ํ์ผ๋ก ํ๊ฒฝ๊ณผ ์ ์ดํ ์ ์๋๋ก ํ์๋ค.
๋ถํ์ค์ฑ์ด ์กด์ฌํ๋ ๋น์ ํ ํ๊ฒฝ์์ ๋น์ ํ ์๋ฌด๋ฅผ ์ํํ ์ ์๋ ๊ตฌ์กฐ๋ก๋ด์ ํต์ฌ ์ค๊ณ ๊ฐ๋
์ ์ ์ํ๋ค. ๋น์ ํ ํ๊ฒฝ์์์ ์กฐ์ ์ฑ๋ฅ๊ณผ ์ด๋ ์ฑ๋ฅ์ ๋์์ ํ๋ณดํ ์ ์๋ ํ์์ผ๋ก ๋ก๋ด์ ์ค๊ณํ์ฌ ๊ตฌ์กฐ ๋ก๋ด์ผ๋ก ํ์ฌ๊ธ ์ต์ข
๋ชฉ์ ์ผ๋ก ์ค์ ๋ ์ธ๊ฐ์ ๋์ ํ์ฌ ๋ถ์์๋ฅผ ๊ตฌ์กฐํ๊ณ ์ํ๋ฌผ์ ์ฒ๋ฆฌํ๋ ์๋ฌด๋ฅผ ํจ๊ณผ์ ์ผ๋ก ์ํํ ์ ์๋๋ก ํ๋ค. ๊ตฌ์กฐ ๋ก๋ด์ ํ์ํ ๋งค๋ํฐ๋ ์ดํฐ๋ ๋ถ์์ ๊ตฌ์กฐ ์๋ฌด์ ์ํ๋ฌผ ์ฒ๋ฆฌ ์๋ฌด์ ๋ฐ๋ผ ๊ต์ฒด ๊ฐ๋ฅํ ๋ชจ๋ํ์ผ๋ก ์ค๊ณํ์ฌ ๊ฐ๊ฐ์ ์๋ฌด์ ๋ฐ๋ผ ์ต์ ํ๋ ๋งค๋ํฐ ๋ ์ดํฐ๋ฅผ ์ฅ์ฐฉํ์ฌ ์๋ฌด๋ฅผ ์ํํ ์ ์๋ค. ํ์ฒด๋ ํธ๋๊ณผ ๊ด์ ์ด ๊ฒฐํฉ๋ ํ์ด๋ธ๋ฆฌ๋ ํํ๋ฅผ ์ทจํ๊ณ ์์ผ๋ฉฐ, ์ฃผํ ์๋ฌด์ ์กฐ์์๋ฌด์ ๋ฐ๋ผ ํ์์ ๋ณ๊ฒฝํ ์ ์๋ค. ํ์ ๋ณ๊ฒฝ๊ณผ ๋ชจ๋ํ๋ ๋งค๋ํฐ๋ ์ดํฐ๋ฅผ ํตํด์์กฐ์ ์ฑ๋ฅ๊ณผ ํํ ์งํ์์ ์ด๋ํ ์ ์๋ ์ฃผํ ์ฑ๋ฅ์ ๋์์ ํ๋ณดํ์๋ค.
์ต์ข
์ ์ผ๋ก ๊ตฌ์กฐ๋ก๋ด์ ์ค๊ณ์ ์ค์๊ฐ ๊ณ์ธต์ ์ ์ด๋ฅผ ์ด์ฉํ์ฌ ๋น์ ํ ์ค๋ด์ธ ํ๊ฒฝ์์ ๊ตฌ์กฐ๋ก๋ด์ด ์ฃผํ์๋ฌด, ์ํ๋ฌผ ์กฐ์์๋ฌด, ๋ถ์์ ๊ตฌ์กฐ ์๋ฌด๋ฅผ ์ฑ๊ณต์ ์ผ๋ก ์ ํํ ์ ์์์ ํด์๊ณผ ์คํ์ ํตํ์ฌ ์
์ฆํจ์ผ๋ก์จ ๋ณธ ํ์๋
ผ๋ฌธ์์ ์ ์ํ ์ค๊ณ์ ์ ๊ทํ๋ ๊ณ์ธต์ ์ต์ ํ ๊ธฐ๋ฐ์ ์ ์ด ์ ๋ต์ ์ ์ฉ์ฑ์ ๊ฒ์ฆํ์๋ค.1 Introduction 1
1.1 Motivations 1
1.2 Related Works and Research Problems for Hierarchical Control 3
1.2.1 Classical Approaches 3
1.2.2 State-of-the-Art Strategies 4
1.2.3 Research Problems 7
1.3 Robust Rescue Robots 9
1.4 Research Goals 12
1.5 Contributions of ThisThesis 13
1.5.1 Robust Hierarchical Task-Priority Control 13
1.5.2 Design Concepts of Robust Rescue Robot 16
1.5.3 Hierarchical Motion and ForceControl 17
1.6 Dissertation Preview 18
2 Preliminaries for Task-Priority Control Framework 21
2.1 Introduction 21
2.2 Task-Priority Inverse Kinematics 23
2.3 Recursive Formulation of Null Space Projector 28
2.4 Conclusion 31
3 Robust Hierarchical Task-Priority Control 33
3.1 Introduction 33
3.1.1 Motivations 35
3.1.2 Objectives 36
3.2 Task Function Approach 37
3.3 Regularized Hierarchical Optimization with Equality Tasks 41
3.3.1 Regularized Hierarchical Optimization 41
3.3.2 Optimal Solution 45
3.3.3 Task Error and Hierarchical Matrix Decomposition 49
3.3.4 Illustrative Examples for Regularized Hierarchical Optimization 56
3.4 Regularized Hierarchical Optimization with Inequality Constraints 60
3.4.1 Lagrange Multipliers 61
3.4.2 Modified Active Set Method 66
3.4.3 Illustrative Examples of Modified Active Set Method 70
3.4.4 Examples for Hierarchical Optimization with Inequality Constraint 72
3.5 DLS-HQP Algorithm 79
3.6 Concluding Remarks 80
4 Rescue Robot Design and Experimental Results 83
4.1 Introduction 83
4.2 Rescue Robot Design 85
4.2.1 System Design 86
4.2.2 Variable Configuration Mobile Platform 92
4.2.3 Dual Arm Manipulators 95
4.2.4 Software Architecture 97
4.3 Performance Verification for Hierarchical Motion Control 99
4.3.1 Real-Time Motion Generation 99
4.3.2 Task Specifications 103
4.3.3 Singularity Robust Task Priority 106
4.3.4 Inequality Constraint Handling and Computation Time 111
4.4 Singularity Robustness and Inequality Handling for Rescue Mission 117
4.5 Field Tests 122
4.6 Concluding Remarks 126
5 Hierarchical Motion and Force Control 129
5.1 Introduction 129
5.2 Operational Space Control 132
5.3 Acceleration-Based Hierarchical Motion Control 134
5.4 Force Control 137
5.4.1 Force Control with Inner Position Loop 141
5.4.2 Force Control with Inner Velocity Loop 144
5.5 Motion and Force Control 145
5.6 Numerical Results for Acceleration-Based Motion and Force Control 148
5.6.1 Task Specifications 150
5.6.2 Force Control Performance 151
5.6.3 Singularity Robustness and Inequality Constraint Handling 155
5.7 Velocity Resolved Motion and Force Control 160
5.7.1 Velocity-Based Motion and Force Control 161
5.7.2 Experimental Results 163
5.8 Concluding Remarks 167
6 Conclusion 169
6.1 Summary 169
6.2 Concluding Remarks 173
A Appendix 175
A.1 Introduction to PID Control 175
A.2 Inverse Optimal Control 176
A.3 Experimental Results and Conclusion 181
Bibliography 183
Abstract 207๋ฐ
Nonverbal Communication During Human-Robot Object Handover. Improving Predictability of Humanoid Robots by Gaze and Gestures in Close Interaction
Meyer zu Borgsen S. Nonverbal Communication During Human-Robot Object Handover. Improving Predictability of Humanoid Robots by Gaze and Gestures in Close Interaction. Bielefeld: Universitรคt Bielefeld; 2020.This doctoral thesis investigates the influence of nonverbal communication on human-robot object handover. Handing objects to one another is an everyday activity where two individuals cooperatively interact. Such close interactions incorporate a lot of nonverbal communication in order to create alignment in space and time. Understanding and transferring communication cues to robots becomes more and more important as e.g. service robots are expected to closely interact with humans in the near future. Their tasks often include delivering and taking objects. Thus, handover scenarios play an important role in human-robot interaction. A lot of work in this field of research focuses on speed, accuracy, and predictability of the robotโs movement during object handover. Still, robots need to be enabled to closely interact with naive users and not only experts. In this work I present how nonverbal communication can be implemented in robots to facilitate smooth handovers. I conducted a study on people with different levels of experience exchanging objects with a humanoid robot. It became clear that especially users with only little experience in regard to interaction with robots rely heavily on the communication cues they are used to on the basis of former interactions with humans. I added different gestures with the second arm, not directly involved in the transfer, to analyze the influence on synchronization, predictability, and human acceptance. Handing an object has a special movement trajectory itself which has not only the purpose of bringing the object or hand to the position of exchange but also of socially signalizing the intention to exchange an object. Another common type of nonverbal communication is gaze. It allows guessing the focus of attention of an interaction partner and thus helps to predict the next action. In order to evaluate handover interaction performance between human and robot, I applied the developed concepts to the humanoid robot Meka M1. By adding the humanoid robot head named Floka Head to the system, I created the Floka humanoid, to implement gaze strategies that aim to increase predictability and user comfort. This thesis contributes to the field of human-robot object handover by presenting study outcomes and concepts along with an implementation of improved software modules resulting in a fully functional object handing humanoid robot from perception and prediction capabilities to behaviors enhanced and improved by features of nonverbal communication
Bio-Inspired Robotics
Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensoryโmotor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
Adaptive Robot Framework: Providing Versatility and Autonomy to Manufacturing Robots Through FSM, Skills and Agents
207 p.The main conclusions that can be extracted from an analysis of the current situation and future trends of the industry,in particular manufacturing plants, are the following: there is a growing need to provide customization of products, ahigh variation of production volumes and a downward trend in the availability of skilled operators due to the ageingof the population. Adapting to this new scenario is a challenge for companies, especially small and medium-sizedenterprises (SMEs) that are suffering first-hand how their specialization is turning against them.The objective of this work is to provide a tool that can serve as a basis to face these challenges in an effective way.Therefore the presented framework, thanks to its modular architecture, allows focusing on the different needs of eachparticular company and offers the possibility of scaling the system for future requirements. The presented platform isdivided into three layers, namely: interface with robot systems, the execution engine and the application developmentlayer.Taking advantage of the provided ecosystem by this framework, different modules have been developed in order toface the mentioned challenges of the industry. On the one hand, to address the need of product customization, theintegration of tools that increase the versatility of the cell are proposed. An example of such tools is skill basedprogramming. By applying this technique a process can be intuitively adapted to the variations or customizations thateach product requires. The use of skills favours the reuse and generalization of developed robot programs.Regarding the variation of the production volumes, a system which permits a greater mobility and a faster reconfigurationis necessary. If in a certain situation a line has a production peak, mechanisms for balancing the loadwith a reasonable cost are required. In this respect, the architecture allows an easy integration of different roboticsystems, actuators, sensors, etc. In addition, thanks to the developed calibration and set-up techniques, the system canbe adapted to new workspaces at an effective time/cost.With respect to the third mentioned topic, an agent-based monitoring system is proposed. This module opens up amultitude of possibilities for the integration of auxiliary modules of protection and security for collaboration andinteraction between people and robots, something that will be necessary in the not so distant future.For demonstrating the advantages and adaptability improvement of the developed framework, a series of real usecases have been presented. In each of them different problematic has been resolved using developed skills,demonstrating how are adapted easily to the different casuistic