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|Title:||Studies on map building and exploration strategies for autonomous mobile robots (AMR)||Authors:||Ip, Ying-leung Johnny||Keywords:||Hong Kong Polytechnic University -- Dissertations
Multisensor data fusion
|Issue Date:||2003||Publisher:||The Hong Kong Polytechnic University||Abstract:||In the last two decades, the problems associated with autonomous exploration of mobile robots operating in unknown environments have attracted the attention of many researchers. To achieve a high degree of autonomy, the mobile robot system must interpret the signals it receives from its sensors in order to obtain an understanding of its surroundings and be able to update its current location. This process involves mapping, localization and simultaneous map building and localization (SLAM) in order to acquire a model of the environment and workspace. In attempting to explore in unknown environments, a truly autonomous mobile robot (AMR) is faced with a fundamental problem: to explore and build maps uncharted region, the robots needs to know its current location relative to the starting position, but in order to know its location, the robot needs a map. In this thesis, the challenging problems of map building, localization and exploration of AMRs are addressed and some solutions are provided. The findings are verified through simulation and real world experimental trials. The studies undertaken and reported in this thesis are arranged into three phases. In phase I, a segment-based map building using Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) is proposed to extract line segments from noisy, ambiguous and spurious sonar measurements. In order to improve the quality of the map, a segment grouping evaluation system via hierarchical fuzzy system is suggested to develop a complete map building technique for static environment. Also, the proposed EAFC is integrated with Bayesian update rule to form a map building technique for dynamic or changing environment. The performances of these algorithms are validated on real-time experimental studies in a real world environment.
In phase II, the robot localization problem is addressed. A fuzzy tried extended Kalman filter (FT-EKF) is proposed for solving the problem of model-based localization without a priori knowledge of state noise model. The state noise model in EKF is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with a standard EKF localization method through simulations and experiments. In addition, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The EAFC in phase I is applied in the SLAM algorithm. 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 model based FT-EKF localization algorithm for the same experiment is also provided for comparison. In the third phase, the use of Hierarchical and Fused Fuzzy System (HFFS) are discussed for designing a reactive navigation approach. A novel autonomous exploration algorithm is proposed for controlling a mobile robot to explore in an unknown environment. The proposed exploration strategy is also combined with the SLAW algorithm in phase II. Finally, a truly AMR is developed and is validated by building a complete and accuracy map with autonomous exploring mobile robot in an unknown indoor environment. All processing is carried out on a Pentium 4 1.6GHz PC computer in a real-time linked with Pioneer 2DX mobile robot via radio modem.
|Description:||1 v. (various pagings) ; ill. ; 30 cm.
PolyU Library Call No.: [THS] LG51 .H577P EE 2003 Ip
|URI:||http://hdl.handle.net/10397/3478||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of Mar 18, 2018
Citations as of Mar 18, 2018
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