• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.722-725
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• 1990

This paper proposes a new path planning method for obstacle avoidance of mobile robot. In order to achieve easy planning of the path, a simple representation of the empty space is achieved based on thinning algorithm. The proposed Planning technique facilitates the direct use of information obtained by camera. Comparing to the V-graph method, the task of determining the shortest path from the resulting skeleton of empty space is optimized in terms of number of computation steps. The usefulness of the proposed method is ascertained by simulation.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.121-128
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• 2018

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.715-718
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• 1998

There are a lot of paths which connect between the mobile robot and the goat point. To make a mobile robot arrive at the goal point fastly, The optimal path is needed and a path palnning is necessary. In this paper, we propose a new method of path planning to find a path for mobile robot. It is based on Ginetic Algorithm for serching the optimal grobal path planning. Simulations show the efficiency for the grobal path planning.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.627-629
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• 1999

The research fields of mobile robot consist of three parts. The first is path planning, the second is the application of new sensors, and the last is a combination of the communication technology and mobile robot. In this paper we treat the path-planning. We use a Bayesian probability map, Distance Transform and Fuzzy logic for a path-planning. DT and Fuzzy logic algorithms search for path in entire, continuous free space and unifies global path planning and local path planning. It is efficient and effective method when compared with navigators using traditional approaches.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.256-261
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• 2005

In this research, we propose a path-planning algorithm for an autonomous mobile robot using geographical information, under the condition that the robot moves in unknown environment. All image inputted by camera at every sampling time are analyzed and geographical elements are recognized, and the geographical information is embedded in environmental map. The geographical information was transformed into 1-dimensional evaluation value that expressed the difficulty of movement for the robot. The robot goes toward the goal searching for path that minimizes the evaluation value at every sampling time. Then, the path is updated by integrating the exploited information and the prediction on unexploited environment. We used a sensor fusion method for improving the mobile robot dead reckoning accuracy. The experiment results that confirm the effectiveness of the proposed algorithm on the robot's reaching the goal successfully using geographical information are presented.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.122-127
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• 1996

The achievement of the optimal condition for the task of an industrial articulated robot used in many fields is an important problem to improve productivity. In this paper, a minimum-time trajectory for an articulated robot along the specified path is studied and simulated with a proper example. A general dynamic model of manipulator is represented as a function of path distance. Using this model, the velocity is produced as fast as possible at each point along the path. This minimum-time trajectory planning module together with the existing collision-free path planning modules is utilized to design the optimal path planning of robot in cases where obstacles present.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.210-217
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• 2010

This paper presents integrated path planning and collision avoidance for an omni-directional mobile robot. In this scheme, the autonomous mobile robot finds the shortest path by the descendent gradient of a navigation function to reach a goal. In doing so, the robot based on the proposed approach attempts to overcome some of the typical problems that may pose to the conventional robot navigation. In particular, this paper presents a set of analysis for an omni-directional mobile robot to avoid trapped situations for two representative scenarios: 1) Ushaped deep narrow obstacle and 2) narrow passage problem between two obstacles. The proposed navigation scheme eliminates the nonfeasible area for the two cases by the help of the descendent gradient of the navigation function and the characteristics of an omni-directional mobile robot. The simulation results show that the proposed navigation scheme can effectively construct a path-planning system in the capability of reaching a goal and avoiding obstacles despite possible trapped situations under uncertain world knowledge.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1601-1604
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• 1997

The most important topic in research of mobile robot is path planning in order to avoid with obstacle. In this study the path planning algorithm using a neural network model is proposed. The inputs of neural network are range data which are acquired form laser range finderm and weights are based on difference with goal direction. The thresholds are made by consdiering the marginal distance between mobile robot and obstacle. Consequently the outputs are obtained by multiplying input and weight. The obtained heading directiion enables the mobile robot to approach the goal, without any collision with obstacles around. The effectiveness of the this method of real-time navigation of a mobile robot is estimated by computer simulation in complex environment.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.593-599
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• 1999

A mobile robot in FMS environment should be able to nevigate itself. Therefore, path planning is necessary for the mobile robot to perform its tasks without being lost. Path planning using a network model gives oprimal paths to every pair of nodes but building this model demands accurate information of environments. In this paper, a method to build a network model using sonar sensors is presented. The main idea is to build a quad tree model by using sonar sensors and convert the model to a network model for path planning. The new method has been implemented on a mobile robot. Experimental results show that the mobile robot constructs an accurate network model using inaccurate sonar data.