Model Simplification for Efficient Collision Detection in Robotics

Motion planning for industrial robots is a computationally intensive task due to the massive number of potential motions between any two configurations. Calculating all possibilities is generally not feasible. Instead, many motion planners sample a sub-set of the available space until a viable solution is found. Simplifying models to improve collision detection performance, a significant component of motion planning, results in faster and more capable motion planners. Several approaches for simplifying models to improve collision detection performance have been presented in the literature. However, many of them are sub-optimal for an industrial robotics application due to input model limitations, accuracy sacrifices, or the probability of increasing false negatives during collision queries. This thesis focuses on the development of model simplification approaches optimised for industrial robotics applications. Firstly, a new simplification approach, the Bounding Sphere Simplification (BSS), is presented that converts triangle-mesh inputs to a collection of spheres for efficient collision and distance queries. Additionally, BSS removes small features and generates an output model less prone to false negatives

Efficient Computation of K-Nearest Neighbor Graphs for Large High-Dimensional Data Sets on GPU Clusters

The k-Nearest Neighbor Graph (k-NNG) and the related k-Nearest Neighbor (k-NN) methods have a wide variety of applications in areas such as bioinformatics, machine learning, data mining, clustering analysis, and pattern recognition. Our application of interest is manifold embedding. Due to the large dimensionality of the input data (\u3c15k), spatial subdivision based techniques such OBBs, k-d tree, BSP etc., are not viable. The only alternative is the brute-force search, which has two distinct parts. The first finds distances between individual vectors in the corpus based on a pre-defined metric. Given the distance matrix, the second step selects k nearest neighbors for each member of the query data set. This thesis presents the development and implementation of a distributed exact k-Nearest Neighbor Graph (k-NNG) construction method. The proposed method uses Graphics Processing Units (GPUs) and exploits multiple levels of parallelism for distributed computational systems using GPUs. It is scalable for different cluster sizes, with each compute node in the cluster containing multiple GPUs. The distance computation is formulated as a basic matrix multiplication and reduction operation. The optimized CUBLAS matrix multiplication library is used for this purpose. Various distance metrics such as Euclidian, cosine, and Pearson are supported. For k-NNG construction, two different methods are presented. The first is based on an approach called batch index sorting to build the k-NNG with three sorting operations. This method uses the optimized radix sort implementation in the Thrust library for GPU. The second is an efficient implementation using the latest GPU functionalities of a variant of the quick select algorithm. Overall, the batch index sorting based k-NNG method is approximately 13x faster than a distributed MATLAB implementation. The quick select algorithm itself has a 5x speedup over state-of-the art GPU methods. This has enabled the processing of k-NNG construction on a data set containing 20 million image vectors, each with dimension 15,000, as part of a manifold embedding technique for analyzing the conformations of biomolecules

Efficient configuration space construction and optimization

The configuration space is a fundamental concept that is widely used in algorithmic robotics. Many applications in robotics, computer-aided design, and related areas can be reduced to computational problems in terms of configuration spaces. In this dissertation, we address three main computational challenges related to configuration spaces: 1) how to efficiently compute an approximate representation of high-dimensional configuration spaces; 2) how to efficiently perform geometric, proximity, and motion planning queries in high dimensional configuration spaces; and 3) how to model uncertainty in configuration spaces represented by noisy sensor data. We present new configuration space construction algorithms based on machine learning and geometric approximation techniques. These algorithms perform collision queries on many configuration samples. The collision query results are used to compute an approximate representation for the configuration space, which quickly converges to the exact configuration space. We highlight the efficiency of our algorithms for penetration depth computation and instance-based motion planning. We also present parallel GPU-based algorithms to accelerate the performance of optimization and search computations in configuration spaces. In particular, we design efficient GPU-based parallel k-nearest neighbor and parallel collision detection algorithms and use these algorithms to accelerate motion planning. In order to extend configuration space algorithms to handle noisy sensor data arising from real-world robotics applications, we model the uncertainty in the configuration space by formulating the collision probabilities for noisy data. We use these algorithms to perform reliable motion planning for the PR2 robot.Doctor of Philosoph

Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs

Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots' internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, voxels) or as a collection of objects. This paper attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D Dynamic Scene Graph(DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatio-temporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual-inertial data. Kimera includes state-of-the-art techniques for visual-inertial SLAM, metric-semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves state-of-the-art performance in visual-inertial SLAM, estimates an accurate 3D metric-semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution shows how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera are open-source.Comment: 34 pages, 25 figures, 9 tables. arXiv admin note: text overlap with arXiv:2002.0628

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

A survey of real-time crowd rendering

In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft