• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.386-392
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• 1990

This paper discribes a sonar-based certainty grid, the probabilistic representation of the uncertain and incomplete sensor knowledge, for autonomous mobile robot navigation. We use sonar sensor range data to build a map of the robot's surroundings. This range data provides information about the location of the objects which may exist in front of the sensor. From this information, we can compute the probability of being occupied and that of being empty for each cell. In this paper, a new method using Bayesian formula is introduced, which enables us to overcome some difficulties of the Ad-Hoc formula that has been the only way of updating the grids. This new formula can be applied to other kinds of sensors as well as sonar sensor. The validity of this formula in the real world is verified through simulation and experiment. This paper also shows that a wide angle sensor such as sonar sensor can be used effectively to identify the empty area, and the simultaneous use of multiple sensors and fusion in a certainty grid can improve the quality of the map.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.119-124
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• 1993

This paper describes a method of producing maps of an indoor environment with an autonomous mobile robot equipped with sonar array. This method uses the certainty grid suitable for accommodation of inaccurate sensor data and real-time navigation. Each grid contains a certainty vale that indicates the measure of confidence that an obstacle exists within th grid area. The scheduled firing method is used to eliminate the crosstalk between ultrasonic sensors. The effectiveness of the method is verified by a series of experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.71.1-71
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• 2001

In this paper, an improved method of environment map building and a region search algorithm for mobile robot are presented. For the environment map building of mobile robot, measurement data of ultrasonic sensors and certainty grid representation is usually used. In this case, inaccuracies due to the uncertainty of ultrasonic data are included in the map. In order to solve this problem, an environment map building method using a Bayesian model was proposed previously[5]. In this study, we present an improved method of probability map building that uses infrared sensors and shift division Gaussian probability distribution with the existing Bayesian update method using ultrasonic sensors. Also, a region search algorithm for ...

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.398-398
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• 2000

Since there is a blind zone in driver's view around the excavator, industrial accidents between the equipment and the workers within the zone have been occurred frequently. The purpose of this paper is to develop a obstacle recognition system which can prevent such an accident by providing the driver with the information on direction and distance of the obstacle within the blind zone. We designed the ultrasonic sensor based obstacle recognition system which consists of sensor arrays and a control unit connected via CAN(controller area network). The Cross-correlation technique and histogramic probability distribution method are used as a reliable obstacle detection algorithms to remove the environmental noise. The experimental results using a real excavator show the effectiveness of the system.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.67-81
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• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.136-144
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• 2010

This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.4
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• pp.675-683
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• 1992

Mapping and navigation system based on certainty grids for an autonomous mobile robt operating in unknown and unstructured environment is described. The system uses sonar range data to build a map of robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world through experiment. This paper also proposes a technique for reducing for reducing specular reflection problem of sonar system which seriousely deteriorates the map quality, and a new path planning method based on weighted distance, which enables the robot to efficiently navigate in an unknown area.