Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberation- and the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in APPEAL, an Architecture for Plan Projection, Execution And Learning, which extends the well known RHINO control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot's intended course of action, a plan. In McDermott's terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution

Planning and Navigation in Dynamic Environments for Mobile Robots and Micro Aerial Vehicles

Reliable and robust navigation planning and obstacle avoidance is key for the autonomous operation of mobile robots. In contrast to stationary industrial robots that often operate in controlled spaces, planning for mobile robots has to take changing environments and uncertainties into account during plan execution. In this thesis, planning and obstacle avoidance techniques are proposed for a variety of ground and aerial robots. Common to most of the presented approaches is the exploitation of the nature of the underlying problem to achieve short planning times by using multiresolution or hierarchical approaches. Short planning times allow for continuous and fast replanning to take the uncertainty in the environment and robot motion execution into account. The proposed approaches are evaluated in simulation and real-world experiments. The first part of this thesis addresses planning for mobile ground robots. One contribution is an approach to grasp and object removal planning to pick objects from a transport box with a mobile manipulation robot. In a multistage process, infeasible grasps are pruned in offline and online processing steps. Collision-free endeffector trajectories are planned to the remaining grasps until a valid removal trajectory can be found. An object-centric local multiresolution representation accelerates trajectory planning. The mobile manipulation components are evaluated in an integrated mobile bin-picking system. Local multiresolution planning is employed for path planning for humanoid soccer robots as well. The used Nao robot is equipped with only relatively low computing power. A resource-efficient path planner including the anticipated movements of opponents on the field is developed as part of this thesis. In soccer games an important subproblem is to reach a position behind the ball to dribble or kick it towards the goal. By the assumption that the opponents have the same intention, an explicit representation of their movements is possible. This leads to paths that facilitate the robot to reach its target position with a higher probability without being disturbed by the other robot. The evaluation for the planner is performed in a physics-based soccer simulation. The second part of this thesis covers planning and obstacle avoidance for micro aerial vehicles (MAVs), in particular multirotors. To reduce the planning complexity, the planning problem is split into a hierarchy of planners running on different levels of abstraction, i.e., from abstract to detailed environment descriptions and from coarse to fine plans. A complete planning hierarchy for MAVs is presented, from mission planners for multiple application domains to low-level obstacle avoidance. Missions planned on the top layer are executed by means of coupled allocentric and egocentric path planning. Planning is accelerated by global and local multiresolution representations. The planners can take multiple objectives into account in addition to obstacle costs and path length, e.g., sensor constraints. The path planners are supplemented by trajectory optimization to achieve dynamically feasible trajectories that can be executed by the underlying controller at higher velocities. With the initialization techniques presented in this thesis, the convergence of the optimization problem is expedited. Furthermore, frequent reoptimization of the initial trajectory allows for the reaction to changes in the environment without planning and optimizing a complete new trajectory. Fast, reactive obstacle avoidance based on artificial potential fields acts as a safety layer in the presented hierarchy. The obstacle avoidance layer employs egocentric sensor data and can operate at the data acquisition frequency of up to 40 Hz. It can slow-down and stop the MAVs in front of obstacles as well as avoid approaching dynamic obstacles. We evaluate our planning and navigation hierarchy in simulation and with a variety of MAVs in real-world applications, especially outdoor mapping missions, chimney and building inspection, and automated stocktaking.Planung und Navigation in dynamischen Umgebungen für mobile Roboter und Multikopter Zuverlässige und sichere Navigationsplanung und Hindernisvermeidung ist ein wichtiger Baustein für den autonomen Einsatz mobiler Roboter. Im Gegensatz zu klassischen Industrierobotern, die in der Regel in abgetrennten, kontrollierten Bereichen betrieben werden, ist es in der mobilen Robotik unerlässlich, Änderungen in der Umgebung und die Unsicherheit bei der Aktionsausführung zu berücksichtigen. Im Rahmen dieser Dissertation werden Verfahren zur Planung und Hindernisvermeidung für eine Reihe unterschiedlicher Boden- und Flugroboter entwickelt und vorgestellt. Den meisten beschriebenen Ansätzen ist gemein, dass die Struktur der zu lösenden Probleme ausgenutzt wird, um Planungsprozesse zu beschleunigen. Häufig ist es möglich, mit abnehmender Genauigkeit zu planen desto weiter eine Aktion in der Zeit oder im Ort entfernt ist. Dieser Ansatz wird lokale Multiresolution genannt. In anderen Fällen ist eine Zerlegung des Problems in Schichten unterschiedlicher Genauigkeit möglich. Die damit zu erreichende Beschleunigung der Planung ermöglicht ein häufiges Neuplanen und somit die Reaktion auf Änderungen in der Umgebung und Abweichungen bei den ausgeführten Aktionen. Zur Evaluation der vorgestellten Ansätze werden Experimente sowohl in der Simulation als auch mit Robotern durchgeführt. Der erste Teil dieser Dissertation behandelt Planungsmethoden für mobile Bodenroboter. Um Objekte mit einem mobilen Roboter aus einer Transportkiste zu greifen und zur Weiterverarbeitung zu einem Arbeitsplatz zu liefern, wurde ein System zur Planung möglicher Greifposen und hindernisfreier Endeffektorbahnen entwickelt. In einem mehrstufigen Prozess werden mögliche Griffe an bekannten Objekten erst in mehreren Vorverarbeitungsschritten (offline) und anschließend, passend zu den erfassten Objekten, online identifiziert. Zu den verbleibenden möglichen Griffen werden Endeffektorbahnen geplant und, bei Erfolg, ausgeführt. Die Greif- und Bahnplanung wird durch eine objektzentrische lokale Multiresolutionskarte beschleunigt. Die Einzelkomponenten werden in einem prototypischen Gesamtsystem evaluiert. Eine weitere Anwendung für die lokale Multiresolutionsplanung ist die Pfadplanung für humanoide Fußballroboter. Zum Einsatz kommen Nao-Roboter, die nur über eine sehr eingeschränkte Rechenleistung verfügen. Durch die Reduktion der Planungskomplexität mit Hilfe der lokalen Multiresolution, wurde die Entwicklung eines Planers ermöglicht, der zusätzlich zur aktuellen Hindernisfreiheit die Bewegung der Gegenspieler auf dem Feld berücksichtigt. Hierbei liegt der Fokus auf einem wichtigen Teilproblem, dem Erreichen einer guten Schussposition hinter dem Ball. Die Tatsache, dass die Gegenspieler vergleichbare Ziele verfolgen, ermöglicht es, Annahmen über mögliche Laufwege zu treffen. Dadurch ist die Planung von Pfaden möglich, die das Risiko, durch einen Gegenspieler passiv geblockt zu werden, reduzieren, so dass die Schussposition schneller erreicht wird. Dieser Teil der Arbeit wird in einer physikalischen Fußballsimulation evaluiert. Im zweiten Teil dieser Dissertation werden Methoden zur Planung und Hindernisvermeidung von Multikoptern behandelt. Um die Planungskomplexität zu reduzieren, wird das zu lösenden Planungsproblem hierarchisch zerlegt und durch verschiedene Planungsebenen verarbeitet. Dabei haben höhere Planungsebenen eine abstraktere Weltsicht und werden mit niedriger Frequenz ausgeführt, zum Beispiel die Missionsplanung. Niedrigere Ebenen haben eine Weltsicht, die mehr den Sensordaten entspricht und werden mit höherer Frequenz ausgeführt. Die Granularität der resultierenden Pläne verfeinert sich hierbei auf niedrigeren Ebenen. Im Rahmen dieser Dissertation wurde eine komplette Planungshierarchie für Multikopter entwickelt, von Missionsplanern für verschiedene Anwendungsgebiete bis zu schneller Hindernisvermeidung. Pfade zur Ausführung geplanter Missionen werden durch zwei gekoppelte Planungsebenen erstellt, erst allozentrisch, und dann egozentrisch verfeinert. Hierbei werden ebenfalls globale und lokale Multiresolutionsrepräsentationen zur Beschleunigung der Planung eingesetzt. Zusätzlich zur Hindernisfreiheit und Länge der Pfade können auf diesen Planungsebenen weitere Zielfunktionen berücksichtigt werden, wie zum Beispiel die Berücksichtigung von Sensorcharakteristika. Ergänzt werden die Planungsebenen durch die Optimierung von Flugbahnen. Diese Flugbahnen berücksichtigen eine angenäherte Flugdynamik und erlauben damit ein schnelleres Verfolgen der optimierten Pfade. Um eine schnelle Konvergenz des Optimierungsproblems zu erreichen, wurde in dieser Arbeit ein Verfahren zur Initialisierung entwickelt. Des Weiteren kommen Methoden zur schnellen Verfeinerung des Optimierungsergebnisses bei Änderungen im Weltzustand zum Einsatz, diese ermöglichen die Reaktion auf neue Hindernisse oder Abweichungen von der Flugbahn, ohne eine komplette Flugbahn neu zu planen und zu optimieren. Die Sicherheit des durch die Planungs- und Optimierungsebenen erstellten Pfades wird durch eine schnelle, reaktive Hindernisvermeidung gewährleistet. Das Hindernisvermeidungsmodul basiert auf der Methode der künstlichen Potentialfelder. Durch die Verwendung dieser schnellen Methode kombiniert mit der Verwendung von nicht oder nur über kurze Zeiträume aggregierte Sensordaten, ermöglicht die Reaktion auf unbekannte Hindernisse, kurz nachdem diese von den Sensoren wahrgenommen wurden. Dabei kann der Multikopter abgebremst oder gestoppt werden, und sich von nähernden Hindernissen entfernen. Die Komponenten der Planungs- und Hindernisvermeidungshierarchie werden sowohl in der Simulation evaluiert, als auch in integrierten Gesamtsystemen mit verschiedenen Multikoptern in realen Anwendungen. Dies sind insbesondere die Kartierung von Innen- und Außenbereichen, die Inspektion von Gebäuden und Schornsteinen sowie die automatisierte Inventur von Lägern

FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified \textit{planning model} of the system. The plan is tracked by the system, represented by a more realistic, higher-dimensional \textit{tracking model}. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs.Comment: Published in the IEEE Transactions on Automatic Contro

A Study of Mobility Models in Mobile Surveillance Systems

This thesis explores the role mobile sensor's mobility model and how it affects surveillance system performance in term of area coverage and detection effectiveness. Several algorithms which are categorized into three types, namely, fully coordinated mobility, fully random mobility and emergent mobility models are discussed with their advantages and limitations. A multi-agent platform to organize mobile sensor nodes, control nodes and actor nodes was implemented. It demonstrated great flexibility and was favourable for its distributed, autonomous and cooperative problem-solving characters. Realistic scenarios which are based on three KheperaIII mobile robots and a model which mimics Waterloo regional airport were used to examine the implementation platform and evaluate performance of different mobility algorithms. Several practical issues related to software configurations and interface library were addressed as by-products. The experimental results from both simulation and real platform show that the area coverage and the detection effectiveness vary with applying different mobility models. Fully coordinated model's super efficiency comes with carefully task planning and high requirements of sensor navigational accuracy. Fully random model is the least efficient in area coverage and detection because of the repetitive searching of each sensor and among sensors. A self-organizing algorithm named anti-flocking which mimics solitary animal's social behaviour was first proposed. It works based on quite simple rules for achieving purposeful coordinated group action without explicit global control. Experimental results demonstrate its attractive target detection efficiency in term of both detection rate and detection time while providing desirable features such as scalability, robustness and adaptivity. From the simulation results, the detection rate of the anti-flocking model increases by 36.5% and average detection time decreases by 46.2% comparing with the fully random motion model. The real platform results also reflect the superior performance improvement

Cognitive Reasoning for Compliant Robot Manipulation

Physically compliant contact is a major element for many tasks in everyday environments. A universal service robot that is utilized to collect leaves in a park, polish a workpiece, or clean solar panels requires the cognition and manipulation capabilities to facilitate such compliant interaction. Evolution equipped humans with advanced mental abilities to envision physical contact situations and their resulting outcome, dexterous motor skills to perform the actions accordingly, as well as a sense of quality to rate the outcome of the task. In order to achieve human-like performance, a robot must provide the necessary methods to represent, plan, execute, and interpret compliant manipulation tasks. This dissertation covers those four steps of reasoning in the concept of intelligent physical compliance. The contributions advance the capabilities of service robots by combining artificial intelligence reasoning methods and control strategies for compliant manipulation. A classification of manipulation tasks is conducted to identify the central research questions of the addressed topic. Novel representations are derived to describe the properties of physical interaction. Special attention is given to wiping tasks which are predominant in everyday environments. It is investigated how symbolic task descriptions can be translated into meaningful robot commands. A particle distribution model is used to plan goal-oriented wiping actions and predict the quality according to the anticipated result. The planned tool motions are converted into the joint space of the humanoid robot Rollin' Justin to perform the tasks in the real world. In order to execute the motions in a physically compliant fashion, a hierarchical whole-body impedance controller is integrated into the framework. The controller is automatically parameterized with respect to the requirements of the particular task. Haptic feedback is utilized to infer contact and interpret the performance semantically. Finally, the robot is able to compensate for possible disturbances as it plans additional recovery motions while effectively closing the cognitive control loop. Among others, the developed concept is applied in an actual space robotics mission, in which an astronaut aboard the International Space Station (ISS) commands Rollin' Justin to maintain a Martian solar panel farm in a mock-up environment. This application demonstrates the far-reaching impact of the proposed approach and the associated opportunities that emerge with the availability of cognition-enabled service robots

Acquisition and distribution of synergistic reactive control skills

Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions. Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning