Wolfgang Rencken

Concurrent localisation and map building for mobile robots using ultrasonic sensors

Successful autonomous navigation by a mobile robot depends on its ability to map its environment and to accurately determine its position within this environment. In complex environments, where no additional navigational aids are present, the localization and map-building processes are not independent of each other. This paper presents an approach where the boot-strapping problem of concurrent localization and map building is solved by estimating the respective errors introduced by each of the processes and correcting them accordingly. The algorithms were tested in simulation, and results showed that the robot stably navigated in its environment.

Autonomous sonar navigation in indoor, unknown and unstructured environments

A mobile robot operating autonomously in unknown, unstructured environments has to be able to map its environment while at the same time determining its own position accurately within this environment. This paper presents an approach where the bootstrapping problem of concurrent localisation and map building is solved by estimating the respective errors introduced by each of the processes and correcting them accordingly. The success of this approach also hinges on the ability to determine which measurement originates from which feature. A heuristic multiple hypothesis data association framework is developed to deal with this problem. The problems encountered with the implementation of the algorithms on the mobile robot ROAMER are discussed. Real experiments in typical office environments have shown that the robot is able to navigate autonomously in such indoor environments. >

Learning Systems Behavior For The Automatic Correction And Optimization Of Off-line Robot Programs

Robot program generation by means of graphical 08lane simulation tools has become a widely accepted alternative (or additional technique) to on-line teach-in methods. Unfortunately, the generated programs have to be adjusted (re-taught) manually to the production cell’s environment before they finally can be used in the factory plant. The positioning errors resulting from the execution of the generated programs are due to the inaccurate modeling of the robot (geometry, kinematics and esp. the dynamics), the parts and the tools in the graphical simulation package. This paper presents an approach for correcting and optimizing inaccurate robot programs automatically. Sensors measure the di

Relocalisation by Partial Map Matching

erence between the robot’s intended and actual path. The errors are used to learn the robot’s system model, without explicitly modeling factors such as damping, backlash, friction etc. After the robot model has been learned, the positioning errors can be compensated for by adjusting the coordinates of the MOVE STRAIGHT commands of the robot programs. The paper describes approaches where lanear and non-linear neural networks are used for the forward and inverse robot system models. Finally simulation results are presented and discussed.

Large Consistent Geometric Landmark Maps

The autonomous operation of an intelligent service robot in practical applications requires that the robot builds up a map of the environment by itself. A prerequisite for building large scale consistent maps is that the robot is able to recognise previously mapped areas and relocalise within these areas. The recognition is based on constructing partial maps of geometric landmarks which are then compared to yield the optimal correspondence between these landmarks. For each landmark a signature is constructed which contains additional information about its immediate environment and its non-geometric properties. It is ensured that the signatures are robust with respect to missing landmarks, rotation and translation of landmarks and varying landmark lengths. Both simulation and experiments on real robots have shown that the approach is capable of recognising previously mapped areas robustly in real-timel.

Method of determining the position of a landmark in the environment map of a self-propelled unit, the distance of the landmark from the unit being determined dynamically by the latter

The autonomous operation of an intelligent service robot in practical applications requires that the robot builds up a map of the environment by itself, even for large environments like supermarkets. This paper presents a solution to the problem of building large consistent maps consisting of geometric landmarks. This solution consists of three basic steps: - incremental extraction of geometric landmarks from range data - recognition of previously mapped parts of the environment and identification of landmarks originating from the same structure and finally - removing the inconsistencies by unifying those landmarks while retaining local relations between the other landmarks. The recognition is based on comparing partial maps of geometric landmarks. This is done by enhancing an individual landmark with features derived from its environment. Care is taken that these features are invariant with respect to missing landmarks, rotation and translation of the map and varying landmark lengths. Based on this set of features, different landmarks originating from the same real world object can be identified. For the purpose of correcting these inconsistencies the geometric relations between landmarks are modeled by links of variable length and variable angles between a link and the adjacent landmarks forming a flexible truss. Replacing two identified landmarks with their mean modifies length and angles of the related links, thus introducing energy into the truss. The overall energy in the truss is minimized by means of numerical optimization resulting in a consistent map. Experience in the field with about 20 robots has shown that it is possible to build up maps of large environments robustly in real-time.