Rigid Body Dynamics Simulation for Robot Motion Planning

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Rigid Body Dynamics Simulation for Robot Motion Planning RIGID BODY DYNAMICS SIMULATION FOR ROBOT MOTION PLANNING THÈSE NO 3663 (2006) PRÉSENTÉE LE 17 noveMBRE 2006 À LA faculté DES SCIENCES ET techniques DE L'INGÉNIEUR Laboratoire de systèmes robotiques 1 PROGRAMME DOCTORAL EN SYSTÈMES DE PRODUCTION ET ROBOTIQUE ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR Alan ETTLIN ingénieur informaticien diplômé EPF de nationalité suisse et originaire de Kerns (OW) acceptée sur proposition du jury: Prof. A. Billard, présidente du jury Prof. H. Bleuler, directeur de thèse Prof. G. A. de Paula Caurin, rapporteur Prof. D. Thalmann, rapporteur Dr R. Vuillemin, rapporteur Suisse 2006 ABSTRACT The development of robot motion planning algorithms is inherently a challeng- ing task. This is more than ever true when the latest trends in motion planning are considered. Some motion planners can deal with kinematic and dynamic con- straints induced by the mechanical structure of the robot. Another class of motion planners fulfill various types of optimality conditions, yet others include means of dealing with uncertainty about the robot and its environment. Sensor-based motion planners gather information typically afflicted with errors about a partially known environment in order to plan a trajectory therein. In another research area it is investigated how multiple robots best cooperate to solve a common task. In order to deal with the complexity of developing motion planning algorithms, it is proposed in this document to resort to a simulation environment. The advan- tages of doing so are outlined and a system named Ibex presented which is well suited to support motion planner development. The developed framework makes use of rigid body dynamics algorithms as simulation kernel. Further, various com- ponents are included which integrate the simulation into existing engineering en- vironments. Simulation content can be conveniently developed through extensions of well-established 3D modelling tools. The co-simulation with components from other domains of physics is provided by the integration into a leading dynamic mod- elling environment. Robotic actuator models can be combined with a rigid body dynamics simulation using this mechanism. The same configuration also allows to conveniently develop control algorithms for a rigid body dynamics setup and offers powerful tools for handling and analysing simulation data. The developed simu- lation framework also offers physics-based models for simulating various sensors, most prominently a model for sensor types based on wave propagation, such as laser range finding devices. Application examples of the simulation framework are presented from the mo- bile robotics rough-terrain motion planning domain. Three novel rough-terrain planning algorithms are presented which are extensions of known approaches. To quantify the navigational difficulty on rough terrain, a new generic measure named “obstacleness” is proposed which forms the basis of the proposed algorithms. The first algorithm is based on Randomised Potential Field Planners (RPP) and consequently is a local algorithm. The second proposed planner extends ii RRTconnect, a bi-directional Rapidly Exploring Random Tree (RRT) algorithm and biases exploration of the search space towards easily traversable regions. The third planner is an extension of the second approach and uses the same heuristic to grow a series of additional local RRTs. This allows it to plan trajectories through complex distributions of navigational difficulty benefitting from easy regions throughout the motion plan. A complete example is shown in which the proposed algorithms form the basis for sensor-based dynamic re-planning simulated in the presented framework. In the scenario, a simulated planetary rover navigates a long distance over rough ter- rain while gathering sensor data about the terrain topography. Where obstacles are sensed which interfere with the original motion plan, dynamic re-planning routines are applied to circumnavigate the hindrances. In the course of this document a complete simulation environment is presented by means of a theoretical background and application examples which can signif- icantly support the development of robot motion planning algorithms. The frame- work is capable of simulating setups which fulfil the requirements posed by state- of-the-art motion planning algorithm development. KEYWORDS rigid body dynamics, robot motion planning, rough-terrain navigation, sensor simulation, simulation content tool-chain ZUSAMMENFASSUNG Die Entwicklung von Pfadplanungsalgorithmen fur¨ Roboter ist an sich schon ei- ne schwierige Aufgabe. Dies ist erst recht wahr, wenn die neuesten Entwicklun- gen in der Pfadplanung berucksichtigt¨ werden. Einige Pfadplaner konnen¨ mit ki- nematischen und dynamische Einschrankungen¨ umgehen, die von der mechani- schen Struktur des Roboters erzeugt werden. Eine andere Kategorie von Pfadpla- nern erfullt¨ diverse Optimalitatskriterien,¨ wieder andere Planer beinhalten Metho- den um mit Ungewissheiten uber¨ den Roboter sowie dessen Umgebung umgehen zu konnen.¨ Sensorbasierte Pfadplaner erfassen typischerweise fehlerbehaftete Da- ten uber¨ eine teilweise bekannte Umgebung um darin eine Trajektorie zu errechnen. In einem anderen Forschungsfeld wird untersucht wie mehrere Roboter am Besten miteinander kooperieren konnen¨ um eine gemeinsame Aufgabe zu erfullen.¨ Um die Komplexitat¨ in der Entwicklung von Pfadplanungsalgorithmen besser handhaben zu konnen¨ wird in diesem Dokument vorgeschlagen auf eine Simu- lationsumgebung zuruckzugreifen.¨ Die Vorteile eines solchen Vorgehens werden erlautert¨ und ein System namens Ibex vorgestellt, welches gut geeignet ist um die Pfadplanungsalgorithmenentwicklung zu unterstutzen.¨ Die entwickelte Simu- lationsumgebung verwendet Starrkorperdynamikalgorithmen¨ als Simulationskern. Ferner sind weitere Komponenten entwickelt worden, welche die Simulation in bestehende Entwicklungsumgebungen integrieren. Das Erstellen von Simulations- inhalten wird mittels Erweiterungen von etablierten 3D Modellierungswerkzeugen ermoglicht.¨ Die Ko-simulation mit Komponenten aus anderen Gebieten der Physik wird durch die Integration in eine fuhrende¨ Entwicklungsumgebung zum erstel- len dynamischer Modelle sichergestellt. Modelle von Robotik-Aktoren konnen¨ auf diese weise einfach mit einer Starrkorperdynamiksimulation¨ kombiniert werden. Dieselbe Konfiguration bietet auch eine machtige¨ Moglichkeit¨ Regelalgorithmen fur¨ einen Starrkorperdynamikaufbau¨ zu entwickeln sowie die anfallenden Simula- tionsdaten zu handhaben und auszuwerten. Die entwickelte Simulationsumgebung bietet ebenfalls physikbasierte Modelle zur Simulation verschiedener Sensortypen an, z.B. ein Modell fur¨ Sensoren welche auf Wellenausbreitung basieren, wie etwa ein Laser-Distanzmessgerat.¨ Es werden Anwendungsbeispiele der Simulationsumgebung aus dem Gebiet der mobilen Roboter-Pfadplanung auf unebenem Gelande¨ prasentiert.¨ Drei neuarti- iv ge Pfadplanungsalgorithmen zur Navigation auf unebenem Gelande¨ werden vorge- stellt, die Erweiterungen von existierenden Ansatzen¨ sind. Zur Quantifizierung der Navigationsschwierigkeit auf unebenem Gelande¨ wird ein neues generisches Mass namens “Obstacleness” vorgestellt welches die Basis fur¨ die vorgeschlagenen Al- gorithmen bildet. Der erste Algorithmus basiert auf dem “Randomised Potential Field Planner” (RPP) Ansatz und ist daher ein lokaler Algorithmus. Der zweite vorgeschlagene Pfadplaner erweitert RRTconnect, ein bidirektionaler “Rapidly Exploring Random Tree” (RRT) Algorithmus, durch eine gewichtete Erkundung des Suchraums zu- gunsten von einfach traversierbaren Regionen. Der dritte Planer ist eine Erweite- rung des Zweiten und verwendet dieselbe Heuristik um eine Anzahl zusatzlicher,¨ lokaler RRT-Baume¨ zu erstellen. Dies ermoglicht¨ es ihm, Trajektorien durch kom- plexe Verteilungen der Navigationsschwierigkeit zu errechnen und dabei Vorteile aus einfachen Regionen entlang des gesamten Pfades zu ziehen. Es wird ein komplettes Beispiel gezeigt, in welchem die vorgeschlagenen Algorithmen die Basis fur¨ sensor-basierte dynamische Neuplanung von Tra- jektorien in der Simulationsumgebung bilden. Im gezeigten Szenario legt ein simulierter gelandeg¨ angiger¨ Roboter eine lange Strecke auf einer unebenen planetarischen Oberflache¨ zuruck¨ und sammelt fortwahrend¨ Sensordaten uber¨ die Gelandetopographie.¨ Detektierte Hindernisse welche das Verfolgen der ur- sprunglichen¨ Trajektorie verunmoglichen¨ werden mittels dynamischer Neuplanung umfahren. Im Verlauf dieses Dokuments wird anhand einer theoretischen Einfuhrung¨ so- wie von Anwendungsbeispielen eine komplette Simulationsumgebung vorgestellt welche die Entwicklung von Pfadplanungsalgorithmen fur¨ Roboter bedeutend un- terstutzen¨ kann. Die Simulationsumgebung kann Szenen simulieren welche den Anforderungen der Entwicklung aktueller Pfadplanungsalgorithmen genugen.¨ SCHLUSSELW¨ ORTER¨ Starrkorperdynamik¨ , Roboter-Pfadplanung, Navigation auf unebenem Gelande¨ , Sensorsimulation, Entwicklungsumgebung fur¨ Simulationsinhalt ACKNOWLEDGEMENTS I would like to thank my advisor, Hannes Bleuler for having supervised this the- sis and for having hosted me as an external PhD student at the Robotics Systems Laboratory 1. Without him, all of this would not have been possible. Further, I would like to express my gratitude to Ronald Vuillemin, my boss and mentor at the University of Applied Sciences of Central Switzerland. He has always given me an exceptionally high degree of freedom
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