Expert Systems and Advanced Algorithms in Mobile Robots Path Planning

Metody plánování pohybu jsou významnou součástí robotiky, resp. mobilních robotických platforem. Technicky je realizace plánování pohybu z globální úrovně převedena do posloupnosti akcí na úrovni specifické robotické platformy a definovaného prostředí, včetně omezení. V rámci této práce byla provedena recenze mnoha metod určených pro plánování cest, přičemž hlavním těžištěm byly metody založené na tzv. rychle rostoucích stromech (RRT), prostorovém rozkladu (CD) a využití fuzzy expertních systémů (FES). Dosažené výsledky, resp. prezentované algoritmy, využívají dostupné informace z pracovního prostoru mobilního robotu a jsou aplikovatelné na řešení globální pohybové trajektorie mobilních robotů, resp. k řešení specifických problémů plánování cest s omezením typu úzké koridory či překážky s proměnnou polohou v čase. V práci jsou představeny nové plánovací postupy využívající výhod algoritmů RRT a CD. Navržené metody jsou navíc efektivně rozšířeny s využitím fuzzy expertního systému, který zlepšuje jejich chování. Práce rovněž prezentuje řešení pro plánovací problémy typu identifikace úzkých koridorů, či významných oblastí prostoru řešení s využitím přístupů na bázi dekompozice prostoru. V řešeních jsou částečně zahrnuty sub-optimalizace nalezených cest založené na zkracování nalezené cesty a vyhlazování cesty, resp. nahrazení trajektorie hladkou křivkou, respektující lépe předpokládanou dynamiku mobilního zařízení. Všechny prezentované metody byly implementovány v prostředí Matlab, které sloužilo k simulačnímu ověření efektivnosti vlastních i převzatých metod a k návrhu prostoru řešení včetně omezení (překážky). Získané výsledky byly vyhodnoceny s využitím statistických přístupů v prostředí Minitab a Matlab.Motion planning is an active field in robotics domain, it is responsible for translating high-level specifications of a motion task into low-level sequences of motion commands, which respect the robot and the environments constraints. In this work many path-planning approaches have been reviewed, mainly, the rapidly exploring random tree algorithm (RRT), the cell decomposition approaches (CD), and the application of fuzzy expert system (FES) in motion planning. These approaches have been adapted to solve some of mobile robots motion-planning problems efficiently, i.e. motion planning in small and narrow areas, the global path planning in dynamic workspace, and the improvement of planning efficiency using available information about the working environments. New planning approaches have been introduced based on exploiting and combining the advantages of cell-decomposition, and RRT, in addition to use other tools i.e. fuzzy expert system, to increase the efficiency and completeness of finding a solution. This thesis also proposed solutions for other motion-planning problems, for example the identification of narrow area and the important regions when using sampling-based algorithms, the path shortening for RRT, and the problem of planning a safe path. All proposed methods were implemented and simulated in Matlab to compare them with other methods, in different workspaces and under different conditions. Moreover, the results are evaluated by statistical methods using Matlab and Minitab environments.

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance

An ontology-based approach towards coupling task and path planning for the simulation of manipulation tasks

This work deals with the simulation and the validation of complex manipulation tasks under strong geometric constraints in virtual environments. The targeted applications relate to the industry 4.0 framework; as up-to-date products are more and more integrated and the economic competition increases, industrial companies express the need to validate, from design stage on, not only the static CAD models of their products but also the tasks (e.g., assembly or maintenance) related to their Product Lifecycle Management (PLM). The scientific community looked at this issue from two points of view: - Task planning decomposes a manipulation task to be realized into a sequence of primitive actions (i.e., a task plan) - Path planning computes collision-free trajectories, notably for the manipulated objects. It traditionally uses purely geometric data, which leads to classical limitations (possible high computational processing times, low relevance of the proposed trajectory concerning the task to be performed, or failure); recent works have shown the interest of using higher abstraction level data. Joint task and path planning approaches found in the literature usually perform a classical task planning step, and then check out the feasibility of path planning requests associated with the primitive actions of this task plan. The link between task and path planning has to be improved, notably because of the lack of loopback between the path planning level and the task planning level: - The path planning information used to question the task plan is usually limited to the motion feasibility where richer information such as the relevance or the complexity of the proposed path would be needed; - path planning queries traditionally use purely geometric data and/or “blind” path planning methods (e.g., RRT), and no task-related information is used at the path planning level Our work focuses on using task level information at the path planning level. The path planning algorithm considered is RRT; we chose such a probabilistic algorithm because we consider path planning for the simulation and the validation of complex tasks under strong geometric constraints. We propose an ontology-based approach to use task level information to specify path planning queries for the primitive actions of a task plan. First, we propose an ontology to conceptualize the knowledge about the 3D environment in which the simulated task takes place. The environment where the simulated task takes place is considered as a closed part of 3D Cartesian space cluttered with mobile/fixed obstacles (considered as rigid bodies). It is represented by a digital model relying on a multilayer architecture involving semantic, topologic and geometric data. The originality of the proposed ontology lies in the fact that it conceptualizes heterogeneous knowledge about both the obstacles and the free space models. Second, we exploit this ontology to automatically generate a path planning query associated to each given primitive action of a task plan. Through a reasoning process involving the primitive actions instantiated in the ontology, we are able to infer the start and the goal configurations, as well as task-related geometric constraints. Finally, a multi-level path planner is called to generate the corresponding trajectory. The contributions of this work have been validated by full simulation of several manipulation tasks under strong geometric constraints. The results obtained demonstrate that using task-related information allows better control on the RRT path planning algorithm involved to check the motion feasibility for the primitive actions of a task plan, leading to lower computational time and more relevant trajectories for primitive actions

Quantization, Calibration and Planning for Euclidean Motions in Robotic Systems

The properties of Euclidean motions are fundamental in all areas of robotics research. Throughout the past several decades, investigations on some low-level tasks like parameterizing specific movements and generating effective motion plans have fostered high-level operations in an autonomous robotic system. In typical applications, before executing robot motions, a proper quantization of basic motion primitives could simplify online computations; a precise calibration of sensor readings could elevate the accuracy of the system controls. Of particular importance in the whole autonomous robotic task, a safe and efficient motion planning framework would make the whole system operate in a well-organized and effective way. All these modules encourage huge amounts of efforts in solving various fundamental problems, such as the uniformity of quantization in non-Euclidean manifolds, the calibration errors on unknown rigid transformations due to the lack of data correspondence and noise, the narrow passage and the curse of dimensionality bottlenecks in developing motion planning algorithms, etc. Therefore, the goal of this dissertation is to tackle these challenges in the topics of quantization, calibration and planning for Euclidean motions

Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

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

Méthode interactive et par l'apprentissage pour la generation de trajectoire en conception du produit

The accessibility is an important factor considered in the validation and verification phase of the product design and usually dominates the time and costs in this phase. Defining the accessibility verification as the motion planning problem, the sampling based motion planners gained success in the past fifteen years. However, the performances of them are usually shackled by the narrow passage problem arising when complex assemblies are composed of large number of parts, which often leads to scenes with high obstacle densities. Unfortunately, humans’ manual manipulations in the narrow passage always show much more difficulties due to the limitations of the interactive devices or the cognitive ability. Meanwhile, the challenges of analyzing the end users’ response in the design process promote the integration with the direct participation of designers.In order to accelerate the path planning in the narrow passage and find the path complying with user’s preferences, a novel interactive motion planning method is proposed. In this method, the integration with a random retraction process helps reduce the difficulty of manual manipulations in the complex assembly/disassembly tasks and provide local guidance to the sampling based planners. Then a hypothesis is proposed about the correlation between the topological structure of the scenario and the motion path in the narrow passage. The topological structure refers to the medial axis (2D) and curve skeleton (3D) with branches pruned. The correlation runs in an opposite manner to the sampling based method and provide a new perspective to solve the narrow passage problem. The curve matching method is used to explore this correlation and an interactive motion planning framework that can learn from experience is constructed in this thesis. We highlight the performance of our framework on a challenging problem in 2D, in which a non-convex object passes through a cluttered environment filled with randomly shaped and located non-convex obstacles.L'accessibilitéest un facteur important pris en compte dans la validation et la vérification en phase de conception du produit et augmente généralement le temps et les coûts de cette phase. Ce domaine de recherche a eu un regain d’intérêt ces quinze dernières années avec notamment de nouveaux planificateurs de mouvement. Cependant, les performances de ces méthodes sont généralement très faibles lorsque le problème se caractérise par des passages étroits des assemblages complexes composées d'un grand nombre de pièces. Cela conduit souvent àdes scènes àforte densitéd'obstacles. Malheureusement, les manipulations manuelles des humains dans le passage étroit montrent toujours beaucoup de difficultés en raison des limitations des dispositifs interactifs ou la capacitécognitive. Pendant ce temps, les défis de l'analyse de la réponse finale des utilisateurs dans le processus de conception promeut l'intégration avec la participation directe des concepteurs.Afin d'accélérer la planification dans le passage étroit et trouver le chemin le plus conforme aux préférences de l'utilisateur, une nouvelle méthode de planification de mouvement interactif est proposée. Nous avons soulignéla performance de notre algorithme dans certains scénarios difficiles en 2D et 3D environnement.Ensuite, une hypothèse est proposésur la corrélation entre la structure topologique du scénario et la trajectoire dans le passage étroit. La méthode basée sur les courbures est utilisée pour explorer cette corrélation et un cadre de planification de mouvement interactif qui peut apprendre de l'expérience est construit dans cette thèse. Nous soulignons la performance de notre cadre sur un problème difficile en 2D, dans lequel un objet non-convexe passe à travers un environnement encombrérempli d'obstacles non-convexes de forme aléatoire et situés