This paper proposes a novel algorithm combining Fuzzy logic (FL) and Genetic algorithm (GA) for concurrent mapping and localization (CML) of mobile robot. First, CML is formulated as a multidimensional informed search...
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(纸本)0780389123
This paper proposes a novel algorithm combining Fuzzy logic (FL) and Genetic algorithm (GA) for concurrent mapping and localization (CML) of mobile robot. First, CML is formulated as a multidimensional informed search problem. The search is performed to detect a robot pose which can best accommodate the recent sensor scan in the currently available map. A fuzzy set theoretic approach is used to predict a sample based representation of the state space of possible robot poses and a GA is designed to find out the globally optimal solution from the predicted pose space. The GA evaluates the fitness of poses based on the sensory information and drives the generation gradually towards the globally optimal solution even when the fuzzy prediction is inaccurate. The best fit solution as decided by GA offers the most likely continuation of the currently available map. Experiment on synthetic and real data illustrates the robustness of the algorithm.
This paper describes and evaluates a concurrent mapping and localization (CML) algorithm suitable for localizing an autonomous underwater vehicle. The proposed CML algorithm uses a sidescan sonar to sense the environm...
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This paper describes and evaluates a concurrent mapping and localization (CML) algorithm suitable for localizing an autonomous underwater vehicle. The proposed CML algorithm uses a sidescan sonar to sense the environment. The returns from the sonar are used to detect landmarks in the vehicle's vicinity. These landmarks are used, in conjunction with a vehicle model, by the CML algorithm to concurrently build an absolute map of the environment and to localize the vehicle in absolute coordinates. As the vehicle moves forward, the areas covered by a forward-look sonar overlap, whereas little or no overlap occurs when using sidescan sonar. It has been demonstrated that numerous reobservations by a forward-look sonar of the landmarks can be used to perform CML. Multipass missions, such as sets of parallel and regularly spaced linear tracks, allow a few reobservations of each landmark with sidescan sonar. An evaluation of the CML algorithm using sidescan sonar is made on this type of trajectory. The estimated trajectory provided by the CML algorithm shows significant jerks in the positions and heading brought about by the corrections that occur when a landmark is reobserved. Thus, this trajectory is not useful to mosaic the sea bed. This paper proposes the implementation of an optimal smoother on the CML solution. A forward stochastic map is used in conjunction with a backward Rauch-Tting-Striebel filter to provide the smoothed trajectory. This paper presents simulation and real results and shows that the smoothed CML solution helps to produce a more accurate navigation solution and a smooth navigation trajectory. This paper also shows that the qualitative value of the mosaics produced using CML is far superior to those that do not use it.
In this paper an approach to the field of outdoor robotic navigation with focus on underwater simulataneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results fr...
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In this paper an approach to the field of outdoor robotic navigation with focus on underwater simulataneous localization and mapping (SLAM) is proposed that utilizes ultrasonic scanning images. Experimental results from the implementation of it SLAM algorithm with real data are presented. The projected landmark detection process constructs a map of the environment and generates navigation estimates based on an adaptive delayed nearest-neighbor algorithm. The feature extraction and validation processes are resolved at the sensor level using a simple local maximum-level detection algorithm on the range data. This paper presents experimental results from our research efforts in the above area, using data from water tank trials and a remotely operated vehicle operating in it shallow water environment. (c) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2008
This paper presents a new map specifically designed for robots operating in large environments and possibly in higher dimensions. We call this map the hierarchical atlas because it is a multilevel and multiresolution ...
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This paper presents a new map specifically designed for robots operating in large environments and possibly in higher dimensions. We call this map the hierarchical atlas because it is a multilevel and multiresolution representation. For this paper, the hierarchical atlas has two levels: at the highest level there is a topological map that organizes the free space into submaps at the lower level. The lower-level submaps are simply a collection of features. The hierarchical atlas allows us to perform calculations and run estimation techniques, such as Kalman filtering, in local areas without having to correlate and associate data for the entire map. This provides a means to explore and map large environments in the presence of uncertainty with a process named hierarchical simultaneous localization and mapping. As well as organizing information of the free space, the map also induces well-defined sensor-based control laws and a provably complete policy to explore unknown regions. The resulting map is also useful for other tasks such as navigation, obstacle avoidance, and global localization. Experimental results are presented showing successful map building and subsequent use of the map in large-scale spaces.
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