Abstract
In this paper, a new approach is proposed for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM which comprises two main components: 1) Steady state genetic algorithm (SSGA) for self-localization and occupancy grid map building; 2) Bayesian Adaptive Resonance Associative Memory (ARAM) for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid-based, and it is incrementally constructed during the exploration process. During occupancy map building, robot estimated self-position is updated by SSGA. At the same time, robot estimated self position is transmit to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment is to verify the proposed method can be implemented in real world.
Highlights
An autonomous mobile robot is defined as a robot that has the ability to navigate in an environment and execute desired works with little or no human guidance
For the physical robot implementation, the front laser rangefinder data, rear laser rangefinder data, and robot odometry system that was installed on the robot are transmitted to Genetic Bayesian Adaptive Resonance Associative Memory (ARAM) for map building and learning
We input 3 benchmark datasets that gathered in Intel research lab, MIT CSAIL building, and Freiburg indoor building (Kümmerle et al, 2009)
Summary
An autonomous mobile robot is defined as a robot that has the ability to navigate in an environment and execute desired works with little or no human guidance. Previous work in intelligent robotics has proposed multistrategy learning that integrated several inference types and/or computational mechanisms into one learning system (Michalski and Tecuci, 1994). The robot should be able to generate the map of the traversed environment corresponding to its position and posture (mapping) and calculate its position and posture based on the generated map (localization). These two processes are interdependent and usually termed as simultaneous localization and mapping (SLAM) (Tomono and Yuta, 2003; Thrun et al, 2005), which is considered to be an example of a multistrategy learning method
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