Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots

Our paper proposes a model predictive controller as a single-task formulation that simultaneously optimizes wheel and torso motions. This online joint velocity and ground reaction force optimization integrates a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality through the simultaneous optimization over the robot's whole-body variables allows for a single set of parameters and makes online gait sequence adaptation possible. Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings, reducing the cost of transport by up to 85%. Our experiments demonstrate dynamic motions on a quadrupedal robot with non-steerable wheels in challenging indoor and outdoor environments. The paper's findings contribute to evaluating a decomposed, i.e., sequential optimization of wheel and torso motion, and single-task motion planner with a novel quantity, the prediction error, which describes how well a receding horizon planner can predict the robot's future state. To this end, we report an improvement of up to 71% using our proposed single-task approach, making fast locomotion feasible and revealing wheeled-legged robots' full potential.Comment: 8 pages, 6 figures, 1 table, 52 references, 9 equation

Architecture for in-space robotic assembly of a modular space telescope

An architecture and conceptual design for a robotically assembled, modular space telescope (RAMST) that enables extremely large space telescopes to be conceived is presented. The distinguishing features of the RAMST architecture compared with prior concepts include the use of a modular deployable structure, a general-purpose robot, and advanced metrology, with the option of formation flying. To demonstrate the feasibility of the robotic assembly concept, we present a reference design using the RAMST architecture for a formation flying 100-m telescope that is assembled in Earth orbit and operated at the Sun–Earth Lagrange Point 2

Planning Hybrid Driving-Stepping Locomotion for Ground Robots in Challenging Environments

Ground robots capable of navigating a wide range of terrains are needed in several domains such as disaster response or planetary exploration. Hybrid driving-stepping locomotion is promising since it combines the complementary strengths of the two locomotion modes. However, suitable platforms require complex kinematic capabilities which need to be considered in corresponding locomotion planning methods. High terrain complexities induce further challenges for the planning problem. We present a search-based hybrid driving-stepping locomotion planning approach for robots which possess a quadrupedal base with legs ending in steerable wheels allowing for omnidirectional driving and stepping. Driving is preferred on sufficiently flat terrain while stepping is considered in the vicinity of obstacles. Steps are handled in a hierarchical manner: while only the connection between suitable footholds is considered during planning, those steps in the resulting path are expanded to detailed motion sequences considering the robot stability. To enable precise locomotion in challenging terrain, the planner takes the individual robot footprint into account. The method is evaluated in simulation and in real-world applications with the robots Momaro and Centauro. The results indicate that the planner provides bounded sub-optimal paths in feasible time. However, the required fine resolution and high-dimensional robot representation result in too large state spaces for more complex scenarios exceeding computation time and memory constraints. To enable the planner to be applicable in those scenarios, the method is extended to incorporate three levels of representation. In the vicinity of the robot, the detailed representation is used to obtain reliable paths for the near future. With increasing distance from the robot, the resolution gets coarser and the degrees of freedom of the robot representation decrease. To compensate this loss of information, those representations are enriched with additional semantics increasing the scene understanding. We further present how the most abstract representation can be used to generate an informed heuristic. Evaluation shows that planning is accelerated by multiple orders of magnitude with comparable result quality. However, manually designing the additional representations and tuning the corresponding cost functions requires a high effort. Therefore, we present a method to support the generation of an abstract representation through a convolutional neural network (CNN). While a low-dimensional, coarse robot representation and corresponding action set can be easily defined, a CNN is trained on artificially generated data to represent the abstract cost function. Subsequently, the abstract representation can be used to generate a similar informed heuristic, as described above. The CNN evaluation on multiple data sets indicates that the learned cost function generalizes well to realworld scenes and that the abstraction quality outperforms the manually tuned approach. Applied to hybrid driving-stepping locomotion planning, the heuristic achieves similar performance while design and tuning efforts are minimized. Since a learning-based method turned out to be beneficial to support the search-based planner, we finally investigate if the whole planning problem can be solved by a learning-based approach. Value Iteration Networks (VINs) are known to show good generalizability and goal-directed behavior, while being limited to small state spaces. Inspired by the above-described results, we extend VINs to incorporate multiple levels of abstraction to represent larger planning problems with suitable state space sizes. Experiments in 2D grid worlds show that this extension enables VINs to solve significantly larger planning tasks. We further apply the method to omnidirectional driving of the Centauro robot in cluttered environments which indicates limitations but also emphasizes the future potential of learning-based planning methods.Planung von Hybrider Fahr-Lauf-Lokomotion für Bodenroboter in Anspruchsvollen Umgebungen Bodenroboter, welche eine Vielzahl von Untergründen überwinden können, werden in vielen Anwendungsgebieten benötigt. Beispielszenarien sind die Katastrophenhilfe oder Erkundungsmissionen auf fremden Planeten. In diesem Kontext ist hybride Fahr-/Lauf-Fortbewegung vielversprechend, da sie die sich ergänzenden Stärken der beiden Fortbewegungsarten miteinander vereint. Um dies zu realisieren benötigen entsprechende Roboter allerdings komplexe kinematische Fähigkeiten, welche auch in adäquaten Ansätzen für die Planung dieser Fortbewegung berücksichtigt werden müssen. Anspruchsvolle Umgebungen mit komplexen Untergründen erhöhen dabei zusätzlich die Anforderungen an die Bewegungsplanung. In dieser Arbeit wird ein suchbasierter Ansatz für kombinierte Fahr-/Lauf-Fortbewegungsplanung vorgestellt. Die adressierten Zielplattformen sind vierbeinige Roboter, deren Beine in lenkbaren Rädern enden, so dass sie omnidirektional fahren und laufen können. Auf ausreichend ebenem Untergrund wird generell Fahren bevorzugt, während der Planer Laufmanöver in der Nähe von Hindernissen in Erwägung zieht. Schritte werden dabei in einer hierarchischen Art undWeise realisiert: Während des Planens werden nur Verbindungen zwischen geeigneten Auftrittsflächen gesucht. Nur solche Schritte, die im Ergebnispfad enthalten sind, werden anschließend zu detaillierten Bewegungsabläufen verfeinert, welche die Balance des Roboters sicherstellen. Um präzise Fortbewegung in anspruchsvollen Umgebungen zu ermöglichen, betrachtet der Planer die spezifischen Aufstandsflächen der vier Füße. Der Ansatz wurde sowohl in simulierten als auch in realen Tests mit den Robotern Momaro und Centauro evaluiert, wobei der Planer in der Lage war, Lösungspfade von ausreichender Qualität in zulässiger Zeit zu generieren. Allerdings ergeben die benötigte feine Planungsauflösung und die hochdimensionale Roboterrepräsentation große Zustandsräumen. Diese würden für komplexere oder größere Planungsprobleme die zulässige Rechenzeit und den verfügbaren Speicher überschreiten. Damit der Planer auch eben diese komplexeren oder größeren Planungsprobleme handhaben kann, wird eine Erweiterung des Ansatzes beschrieben, welche mehrere Repräsentationslevel mit einbezieht. In unmittelbarer Umgebung des Roboters wird die zuvor beschriebene detaillierte Repräsentation genutzt, um hochwertige Pfade für die nahe Zukunft zu erzeugen. Mit zunehmendem Abstand vom Roboter wird die Auflösung gröber und die Anzahl der Freiheitsgrade in der Roboterrepräsentation sinkt. Um den mit dieser Vergröberung einhergehenden Informationsverlust zu kompensieren, werden diese Repräsentationen mit zusätzlicher Semantik ausgestattet, welche das Szenenverständnis erhöht. Darüber hinaus wird beschrieben, wie die Repräsentation mit dem höchsten Abstraktionsgrad zur Berechnung einer effektiven Heuristik genutzt werden kann. Die Evaluation in Simulationsumgebungen zeigt, dass der Planungsprozess um mehrere Größenordnungen beschleunigt werden kann, während die Ergebnisqualität vergleichbar bleibt. Allerdings sind das manuelle Gestalten der zusätzlichen Repräsentationen und das dazugehörige Parametrisieren der Kostenfunktionen sehr arbeitsintensiv. Um diesen Aufwand zu reduzieren, wird daher eine Methode beschrieben, welche die Gestaltung einer abstrakten Repräsentation durch ein Convolutional Neural Network (CNN) unterstützt. Während eine grobe, niedrigdimensionale Roboterrepräsentation und ein dazugehöriges Aktionsset einfach definiert werden können, wird ein CNN auf künstlich erzeugten Daten trainiert, um die abstrakte Kostenfunktion zu lernen. Anschließend kann die so erzeugte abstrakte Repräsentation genutzt werden, um die bereits zuvor erwähnte effektive Heuristik zu berechnen. In der Evaluation des CNNs auf verschiedenen Datensätzen zeigt sich, dass die gelernte Kostenfunktion auch mit Daten aus realen Umgebungen funktioniert und dass die generelle Ergebnisqualität oberhalb der Ergebnisse mit manuell erzeugten Repräsentationen liegt. Die Anwendnung der Methode zur Planung hybrider Fahr-/Lauf-Fortbewegung zeigt, dass die so erzeugte Heuristik gleichwertige Ergebnisse wie die Heuristik auf Basis manuell erzeugter Repräsentation liefert, während der Aufwand zur Gestaltung und Parametrisierung deutlich verringert wurde. Da sich gezeigt hat, dass eine lernbasierte Methode den klassischen suchbasierten Ansatz effektiv unterstützen kann, wird in dieser Arbeit abschließend untersucht, ob das gesamte Planungsproblem durch eine lernbasierte Methode gelöst werden kann. Value Iteration Networks (VINs) sind in diesem Zusammenhang ein vielversprechender Ansatz, da sie bekanntlich ein gutes zielorientiertes Planungsverhalten lernen und das Gelernte auf unbekannte Situationen verallgemeinern können. Allerdings ist ihre bisherige Anwendung auf kleine Zustandsräume begrenzt. Durch die zuvor beschriebenen Ergebnisse motiviert, wird eine Erweiterung von VINs beschrieben, so dass diese auf verschiedenen Abstraktionsleveln planen, um größere Planungsprobleme in Zustandsräumen entsprechender Größe darzustellen. Experimente in 2D-Rasterumgebungen zeigen, dass die beschriebene Methode VINs in die Lage versetzt, deutlich größere Planungsprobleme zu lösen. Darüber hinaus wird die beschriebene Methode benutzt, um omnidirektionale Fahrmanöver für den Centauro-Roboter in anspruchsvollen Umgebungen zu planen. Gleichzeitig werden hier aber auch die momentanen, hardware-bedingten Grenzen rein lernbasierter Ansätze sowie ihr zukünftiges Potential aufgezeigt

Design and development of a hominid robot with local control in its adaptable feet to enhance locomotion capabilities

With increasing mechanization of our daily lives, the expectations and demands in robotic systems increase in the general public and in scientists alike. In recent events such as the Deepwater Horizon''-accident or the nuclear disaster at Fukushima, mobile robotic systems were used, e.g., to support local task forces by gaining visual material to allow an analysis of the situation. Especially the Fukushima example shows that the robotic systems not only have to face a variety of different tasks during operation but also have to deal with different demands regarding the robot's mobility characteristics. To be able to cope with future requirements, it seems necessary to develop kinematically complex systems that feature several different operating modes. That is where this thesis comes in: A robotic system is developed, whose morphology is oriented on chimpanzees and which has the possibility due to its electro-mechanical structure and the degrees of freedom in its arms and legs to walk with different gaits in different postures. For the proposed robot, the chimpanzee was chosen as a model, since these animals show a multitude of different gaits in nature. A quadrupedal gait like crawl allows the robot to traverse safely and stable over rough terrain. A change into the humanoid, bipedal posture enables the robot to move in man-made environments. The structures, which are necessary to ensure an effective and stable locomotion in these two poses, e.g., the feet, are presented in more detail within the thesis. This includes the biological model and an abstraction to allow a technical implementation. In addition, biological spines are analyzed and the development of an active, artificial spine for the robotic system is described. These additional degrees of freedom can increase the robot's locomotion and manipulation capabilities and even allow to show movements, which are not possible without a spine. Unfortunately, the benefits of using an artificial spine in robotic systems are nowadays still neglected, due to the increased complexity of system design and control. To be able to control such a kinematically complex system, a multitude of sensors is installed within the robot's structures. By placing evaluation electronics close by, a local and decentralized preprocessing is realized. Due to this preprocessing is it possible to realize behaviors on the lowest level of robot control: in this thesis it is exemplarily demonstrated by a local controller in the robot's lower leg. In addition to the development and evaluation of robot's structures, the functionality of the overall system is analyzed in different environments. This includes the presentation of detailed data to show the advantages and disadvantages of the local controller. The robot can change its posture independently from a quadrupedal into a bipedal stance and the other way around without external assistance. Once the robot stands upright, it is to investigate to what extent the quadrupedal walking pattern and control structures (like the local controller) have to be modified to contribute to the bipedal walking as well

Architecture for in-space robotic assembly of a modular space telescope

An architecture and conceptual design for a robotically assembled, modular space telescope (RAMST) that enables extremely large space telescopes to be conceived is presented. The distinguishing features of the RAMST architecture compared with prior concepts include the use of a modular deployable structure, a general-purpose robot, and advanced metrology, with the option of formation flying. To demonstrate the feasibility of the robotic assembly concept, we present a reference design using the RAMST architecture for a formation flying 100-m telescope that is assembled in Earth orbit and operated at the Sun–Earth Lagrange Point 2

四肢ロボットの安定かつ桟の認識が可能な垂直はしご昇降の運動生成

早大学位記番号:新8018早稲田大

Actuation-Aware Simplified Dynamic Models for Robotic Legged Locomotion

In recent years, we witnessed an ever increasing number of successful hardware implementations of motion planners for legged robots. If one common property is to be identified among these real-world applications, that is the ability of online planning. Online planning is forgiving, in the sense that it allows to relentlessly compensate for external disturbances of whatever form they might be, ranging from unmodeled dynamics to external pushes or unexpected obstacles and, at the same time, follow user commands. Initially replanning was restricted only to heuristic-based planners that exploit the low computational effort of simplified dynamic models. Such models deliberately only capture the main dynamics of the system, thus leaving to the controllers the issue of anchoring the desired trajectory to the whole body model of the robot. In recent years, however, we have seen a number of new approaches attempting to increase the accuracy of the dynamic formulation without trading-off the computational efficiency of simplified models. In this dissertation, as an example of successful hardware implementation of heuristics and simplified model-based locomotion, I describe the framework that I developed for the generation of an omni-directional bounding gait for the HyQ quadruped robot. By analyzing the stable limit cycles for the sagittal dynamics and the Center of Pressure (CoP) for the lateral stabilization, the described locomotion framework is able to achieve a stable bounding while adapting to terrains of mild roughness and to sudden changes of the user desired linear and angular velocities. The next topic reported and second contribution of this dissertation is my effort to formulate more descriptive simplified dynamic models, without trading off their computational efficiency, in order to extend the navigation capabilities of legged robots to complex geometry environments. With this in mind, I investigated the possibility of incorporating feasibility constraints in these template models and, in particular, I focused on the joint torques limits which are usually neglected at the planning stage. In this direction, the third contribution discussed in this thesis is the formulation of the so called actuation wrench polytope (AWP), defined as the set of feasible wrenches that an articulated robot can perform given its actuation limits. Interesected with the contact wrench cone (CWC), this yields a new 6D polytope that we name feasible wrench polytope (FWP), defined as the set of all wrenches that a legged robot can realize given its actuation capabilities and the friction constraints. Results are reported where, thanks to efficient computational geometry algorithms and to appropriate approximations, the FWP is employed for a one-step receding horizon optimization of center of mass trajectory and phase durations given a predefined step sequence on rough terrains. For the sake of reachable workspace augmentation, I then decided to trade off the generality of the FWP formulation for a suboptimal scenario in which a quasi-static motion is assumed. This led to the definition of the, so called, local/instantaneous actuation region and of the global actuation/feasible region. They both can be seen as different variants of 2D linear subspaces orthogonal to gravity where the robot is guaranteed to place its own center of mass while being able to carry its own body weight given its actuation capabilities. These areas can be intersected with the well known frictional support region, resulting in a 2D linear feasible region, thus providing an intuitive tool that enables the concurrent online optimization of actuation consistent CoM trajectories and target foothold locations on rough terrains