Ying-Leung Ip

Segment-Based Map Building Using Enhanced Adaptive Fuzzy Clustering Algorithm for Mobile Robot Applications

In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.

Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications

In this paper, a novel approach for fine-tuning of the grid-based map-building algorithm is reported. The traditional occupancy grid-based map-building algorithm uses a fixed probability distribution function of the sonar readings and disregards the information from the environment. In our approach, the probability distribution function is tuned by fuzzy rules formed from the information obtained from the environment at each sonar data scan. A Bayesian update rule is then used to update the occupancy probabilities of the grid cells. The proposed map-building algorithm is compared with other grid-based map-building methods through simulations and experiments. The simulation and experimental studies suggest that ‘sharp’ grid maps can be obtained by incorporating fuzzy rules during the grid-based map generation. In comparison with other algorithms, improved convergence has also been noted.

Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data

Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.

Concurrent Map Building and Behavior Learning for Navigation in Unknown Environments

In this paper, we report a robust and low-cost navigation algorithm for an unknown environment based on integration of a grid-based map building algorithm with behavior learning. The study focuses on mobile robots that utilize ultrasonic sensors as their prime interface with the outside world. The proposed algorithm takes into account environmental information to augment the readings from the low angular accuracy sonar measurements for behavior learning. The environmental information is obtained by an online grid-based map learning design that is concurrently operating with the behavior learning algorithm. The proposed algorithm is implemented and tested on an in-house-built mobile robot, and its performance is verified through online navigation in an indoor environment.

Map building via integration of fuzzy systems and clustering algorithms

This paper presents a segment detection and grouping scheme that allows incremental and online learning of indoor environment maps by mobile robots. In this study, the modeling is refined by first dividing the world into discrete regions as local models. The line segments in local models are extracted by clustering algorithm. The local models are grouped together by a hierarchical fuzzy system. Adjusting the membership functions that establish the grouping criteria controls the degree of approximation in such combination. The performance of the algorithm is validated in indoor office environments using a Pioneer II mobile robot.

SLAM with MTT: theory and initial results [mobile robot localisation]

To make a robot to work for and with human, the ability to simultaneously localize itself, accurately map its surroundings, and safely detect and track moving objects around it is a key prerequisite for a truly autonomous robot. In this paper, we explore the theoretical framework of this problem, i.e. simultaneous localization and mapping (SLAM) with multiple target tracking (MTT), and propose to employ sequential Monte Carlo methods (SMCM) as robust and computationally efficient algorithm. After mathematically formulating the problem, we apply a Rao-Blackwellized particle filter to solve SLAM which is partitioned into robot pose and feature location estimations and a conditioned particle filter to solve MTT which is partitioned into robot pose and moving object state estimations, both filters conditioned on robot pose. In detail, we propose sampling importance resampling (SIR) method to estimate robot pose, extended Kalman filter (EKF) to estimate feature location, and hybrid independent/coupled sample-based joint probability data association filter (Hyb-SJPDAF) to solve tracking and data association problem. We present some preliminary experimental results to demonstrate the capabilities of our approach.

Integration of Enhanced Adaptive Fuzzy Clustering Algorithm with Probabilistic Technique for Dynamic Map Building

This paper addresses the problem of incremental and on-line learning of indoor dynamic environments by mobile robots. The proposed method further improves the Enhanced Adaptive Fuzzy Clustering (EAFC) algorithm for segment detection by using probabilistic techniques. In this study, the environment boundaries is extracted by the AFC algorithm and the probabilistic technique is used to estimate and update the state of dynamic objects in the mobile robot workplace. The method has been implemented and tested in a Pioneer II mobile robot.

Fault Diagnosis Based on Qualitative Bond Graph and Genetic Algorithms

Abstract Most of the engineering systems now become more complex and sophisticated, reliability and safety turn into an important issues when operating these systems. In this paper, genetic algorithms are proposed to search for possible fault components among a system of qualitative equations. Qualitative bond graph is adopted as the modeling scheme to generate a set of qualitative equations. Results from diagnosing faults in the floating disc system will be presented and discussed. Fault diagnosis is activated by fault detection mechanism when a discrepancy between measured abnormal behavior and predicted system behavior is observed.