Reactive navigation for non-holonomic robots using the ego kinematic space

We address the problem of applying reactive navigation methods to non-holonomic robots. Rather than embedding the motion constraints when designing a navigation method, we propose to introduce the robot’s kinematic constraints directly in the spatial representation. In this space- the Ego-Kinematic Space- the robot moves as a “free-flying object”. Hence, standard reactive navigation methods applied to this space will automatically take into account the robot’s kinematic constraints, without additional modifications. This methodology can be used with a large class of constrained mobile platforms (e.g. differential-driven robots, car-like robots, tri-cycle robots). We show experiments involving non-holonomic robots with two reactive navigation methods whose original formulation does not take the robot kinematic constraints into account (the Nearness Diagram Navigation and a Potential Field method)

Navigation autonome sans collision pour robots mobiles nonholonomes

Cette thèse traite de la navigation autonome en environnement encombré pour des véhicules à roues soumis à des contraintes cinématiques de type nonholonome. Les applications de ces travaux sont par exemple l'automatisation de véhicules ou l'assistance au parking. Notre contribution porte sur le développement de méthodes qui réalisent certaines des fonctionnalités de la navigation autonome et sur l'intégration de ces différentes fonctionnalités au sein d'une architecture générique, en tenant compte des spécificités des systèmes considérés. Nous présentons une méthode d'évitement réactif d'obstacles pour systèmes nonholonomes et nous proposons une méthode de parking référencé sur des amers pour de tels systèmes. Ensuite nous présentons une architecture générique pour l'intégration des fonctionnalités de localisation, d'évitement d'obstacles et de suivi de trajectoire. Enfin nous illustrons l'ensemble de ces travaux par des résultatsexpérimentaux obtenus avec plusieurs robots. ABSTRACT : This work deals with autonomous navigation in cluttered environments for wheeled mobile robots subject to nonholonomic kinematic constraints. The potential applications of this work are for instance the development of autonomous cars and of parking assistance systems. Our contribution lies in the development of original methods to solve some of the functionalities of autonomous navigation and in their integration into a generic software architecture, while taking into account the specificities of the systems we deal with. We present an obstacle avoidance method for nonholonomic systems and we propose a landmark-based parking method for such systems. Then, we present a generic architecture for the integration of the functionalities of localisation, obstacle avoidance and trajectory following. Eventually, we illustrate this work with some experimental results obtained with several robots

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal

Contributions to Localization, Mapping and Navigation in Mobile Robotics

This thesis focuses on the problem of enabling mobile robots to autonomously build world models of their environments and to employ them as a reference to self–localization and navigation. For mobile robots to become truly autonomous and useful, they must be able of reliably moving towards the locations required by their tasks. This simple requirement gives raise to countless problems that have populated research in the mobile robotics community for the last two decades. Among these issues, two of the most relevant are: (i) secure autonomous navigation, that is, moving to a target avoiding collisions and (ii) the employment of an adequate world model for robot self-referencing within the environment and also for locating places of interest. The present thesis introduces several contributions to both research fields. Among the contributions of this thesis we find a novel approach to extend SLAM to large-scale scenarios by means of a seamless integration of geometric and topological map building in a probabilistic framework that estimates the hybrid metric-topological (HMT) state space of the robot path. The proposed framework unifies the research areas of topological mapping, reasoning on topological maps and metric SLAM, providing also a natural integration of SLAM and the “robot awakening” problem. Other contributions of this thesis cover a wide variety of topics, such as optimal estimation in particle filters, a new probabilistic observation model for laser scanners based on consensus theory, a novel measure of the uncertainty in grid mapping, an efficient method for range-only SLAM, a grounded method for partitioning large maps into submaps, a multi-hypotheses approach to grid map matching, and a mathematical framework for extending simple obstacle avoidance methods to realistic robots