• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1050-1053
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• 1995

A stock removal cycle is developed as a programming tool of the CNC lathe. This cycle support the easiness and swifness of a part program generation in the shopfloor. The cycle contains the function of tool path generation of rough/finish cutting, of avoidance air-cut for non-cylindrical (die-casted, forged) workpiece, and of the tool collisopn detection/aviodance. Consequently the intelligent stock removal cycle results in improving the productivity and safety of CNC lathe.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.113.2-113
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• 2001

In this paper, we present a robot off-Line programming system with collision detection. The collision detection is a very important factor of robot oft-line programming system for collision avoidance, path planning, and so on. The System developed in this paper, basically using an algorithm for the minimum distance calculation between general polyhedra. The proposed system shows an exact and interactive result in static and dynamic environments.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1140-1143
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• 1996

A new approach based on artificial potential function is proposed for the obstacle avoidance of redundant manipulators. Unlike the so-called "global" path planning method, which requires expensive computation for the path search before the manipulator starts to move, this new approach, "local" path planning, researches the path in real-time using the local distance information. Previous use of artificial potential function has exhibited local minima in some complex environments. This thesis proposes a potential function that has no local minima even for a cluttered environment. This potential function has been implemented for the collision avoidance of a redundant robot in Simulation. The simulation also employ an algorithm that eliminates collisions with obstacles by calculating the repulsive potential exerted on links, based on the shortest distance to object.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.726-730
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• 1990

In this paper, assuming that local range information is available, a collision-free path planning algorithm for multiple mobile robots is presented by using Hopfield neural optimization network. The energy function of the network is built using the present position and the goal position of each robot as well as its local range information. The proposed algorithm has several advantages such as the effective passing around obstacles with the directional safety distance, the easy implementation of robot motion planning including its rotation, the real-time path planning capability from the totally localized computations of path for each robot, and the adaptivity on arbitrary environment since any special shape of obstacles is not assumed.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.181-190
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• 2007

This paper presents a collision avoidance system for intelligent ship. Unlike collision avoidance system of other unmanned vehicles, the collision avoidance system for intelligent ship aims at not only deriving a reasonable and safe path to the goal but also keeping COLRECs(International Regulations for Preventing Collisions at Sea). The heuristic search based on the BK-products is adopted to achieve the general purpose of collision avoidance system; deriving a reasonable and safe path. The rule of action to avoid collision is adopted for the other necessary and sufficient condition; keeping the COLREGs. The verification of proposed collision avoidance system is performed with scenarios that represent encounter situations classified in the COLREGs, then it is compared with $A^{\ast}$ search method in view of optimality and safety. The analysis of simulation result revels that the proposed collision avoidance system is practical and effective candidate for real-time collision avoidance system of intelligent ship.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1939-1943
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• 1991

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Neural Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.646-655
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• 2009

This paper presents a new search strategy based on models of evolution in order to solve the problem of collision-free robotic path planning. We designed the robot path planning method with genetic algorithm which has become a well-known technique for optimization, intelligent search. Considering the path points as genes in a chromosome will provide a number of possible solutions on a given map. In this case, path distances that each chromosome creates can be regarded as a fitness measure for the corresponding chromosome. The effectiveness of the proposed genetic algorithm in the path planning was demonstrated by simulation. The proposed search strategy is able to use multiple and static obstacles.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.8
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• pp.563-569
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• 1988

This research presents several velocity modification methods for collision avoidance of two manipulators in a common workspace. Due to the distinct nature of collision avoidance between the two manipulators, a new classification of collision situations is presented and utilized in planning a collision-free path. Concepts of a collision map and velocity modification are applied for realizing collision-free motion planning. An example is shown for velocity modification of a trajectory, which shows the significance of the proposed approaches in collision-free motion planneng of two moving robots.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.79-84
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• 1992

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.