Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints

Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered. We present a combined allocentric complete planning and trajectory optimization approach taking these sensor visibility constraints into account. The optimized trajectories yield flight paths within the apex angle of a Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to perceive obstacles in the flight direction. Furthermore, the optimized trajectories take the flight dynamics into account and contain the velocities and accelerations along the path. We evaluate our approach with a DJI Matrice 600 MAV and in simulation employing hardware-in-the-loop.Comment: In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 201

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

On-line Path Generation for Small Unmanned Aerial Vehicles using B-Spline Path Templates

Copyright © 2008 by D. Jung and P. Tsiotras. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.In this study we investigate the problem of generating a smooth, planar reference path, given a family of discrete optimal paths. In conjunction with a path representation by a finite sequence of square cells, the generated path is supposed to stay inside a feasible channel, while minimizing certain performance criteria. Constrained optimization problems are formulated subject to geometric (linear) constraints, as well as boundary conditions in order to generate a library of B-spline path templates. As an application to the vehicle motion planning, the path templates are incorporated to represent local segments of the entire path as geometrically smooth curves, which are then joined with one another to generate a reference path to be followed by a closed-loop tracking controller. The on-line path generation algorithm incorporates the path templates such that continuity and smoothness are preserved when switching from one template to another along the path. Combined with the D∗-lite path planning algorithm, the proposed algorithm provides a complete solution to the obstacle-free path generation problem in a computationally efficient manner, suitable for real-time implementation

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Planning for Autonomous Operation of Unmanned Surface Vehicles

The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. The autonomous operations of the USV over long time horizons requires a path planner to compute paths over long distances in complex marine environments consisting of hundreds of islands of complex shapes. The available free space in marine environment changes over time as a result of tides, environmental restrictions, and weather. Secondly, the maximum velocity and energy consumption of the USV is significantly influenced by the fluid medium flows such as strong currents. Finally, the USV have to operate in an unfamiliar, unstructured marine environment with obstacles of variable dimensions, shapes, and motion dynamics such as other unmanned surface vehicles, civilian boats, shorelines, or docks poses numerous planning challenges. The proposed Ph.D. dissertation explores the above mentioned problems by developing computationally efficient path and trajectory planning algorithms that enables the long term autonomous operation of the USVs. We have developed a lattice-based 5D trajectory planner for the USVs operating in the environment with the congested civilian traffic. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. Secondly, we present a computationally efficient and optimal algorithm for long distance path planning in complex marine environments using A* search on visibility graphs defined over quad trees. Finally, we present an A* based path planning algorithm with newly developed admissible heuristics for computing energy efficient paths in environment with significant fluid flows. The effectiveness of the planning algorithms is demonstrated in the simulation environments by using systems identified dynamics model of the wave amplitude modular vessel (WAM-V) USV14

Proceedings of the 2015 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of proceedings of the talks given at the 2015 annual joint workshop of Fraunhofer IOSB and the Vision and Fusion Laboratory (IES) by the doctoral students of both institutions. The topics of individual contributions range from computer vision, optical metrology, and world modelling to data fusion and human-machine interaction

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT