• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.203-209
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• 2010

This paper presents a hierarchical fuzzy motion planner for humanoid robots in 3D uneven environments. First, we define both motion primitives and locomotion primitives of humanoid robots. A high-level planner finds a global path from a global navigation map that is generated based on a combination of 2.5 dimensional maps of the workspace. We use a passage map, an obstacle map and a gradient map of obstacles to distinguish obstacles. A mid-level planner creates subgoals that help the robot efficiently cope with various obstacles using only a small set of locomotion primitives that are useful for stable navigation of the robot. We use a local obstacle map to find the subgoals along the global path. A low-level planner searches for an optimal sequence of locomotion primitives between subgoals by using fuzzy motion planning. We verify our approach on a virtual humanoid robot in a simulated environment. Simulation results show a reduction in planning time and the feasibility of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.7-10
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• 2007

This paper presents a fuzzy footstep planner for humanoid robots in complex environments. First, we define locomotion primitives for humanoid robots. A global planner finds a global path from a navigation map that is generated based on a combination of 2.5 dimensional maps of the 3D workspace. A local planner searches for an optimal sequence of locomotion primitives along the global path by using fuzzy footstep planning. We verify our approach on a virtual humanoid robot in a simulated environment. Simulation results show a reduction in planning time and the feasibility of the proposed method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.403-405
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• 1997

In this paper, a navigation system based on fuzzy logic controllers is developed for a mobile robot in an unknown environment. The structure of this fuzzy navigation system features sensor system, fuzzy controllers for motion planning and the motion control system for real-time execution.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.539-541
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• 2006

Biped locomotion is a popular research area in robotics due to the high adaptability of a walking robot in an unstructured environment. When attempting to automate the motion planning process for a biped walking robot, one of the main issues is assurance of dynamic stability of motion. This can be categorized into three general groups: body stability, body path stability, and gait stability. A zero moment point (ZMP), a point where the total forces and moments acting on the robot are zero, is usually employed as a basic component for dynamically stable motion. In this rarer, learning based neuro-fuzzy systems have been developed and applied to model ZMP trajectory of a biped walking robot. As a result, we can provide more improved insight into physical walking mechanisms.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.879-888
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• 2013

This paper addresses the vision based local path planning system for obstacle avoidance. To handle the obstacles which exist beyond the field of view (FOV), we propose a Panoramic Environment Map (PEM) using the MDGHM-SIFT algorithm. Moreover, we propose a Complexity Measure (CM) and Fuzzy logic-based Avoidance Motion Selection (FAMS) system to enable a humanoid robot to automatically decide its own direction and walking motion when avoiding an obstacle. The CM provides automation in deciding the direction of avoidance, whereas the FAMS system chooses the avoidance path and walking motion, based on environment conditions such as the size of the obstacle and the available space around it. The proposed system was applied to a humanoid robot that we designed. The results of the experiment show that the proposed method can be effectively applied to decide the avoidance direction and the walking motion of a humanoid robot.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.8
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• pp.1503-1517
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• 1994

Intelligent Motion Planning System(IMPS) is presented for a robot to achieve an efficient path toward the given target point in two dimensional unknown environment is constructed with unrestricted obstacle shapes. IMPS consists of three components for making intelligent motion. These components are real-time motion planning algorithm based on a discontinous boundary method, fuzzy neural network decision system for heuristic knowledge representation, and world modeling with forgetting and reinforcing memory cells. First of all, in real-time motion planning algorithm, the behavior-based architectural method is used to generate subgoal. A behavior generates a subgoal independently by using the method of discontinuous boundary in sensed area. The discontinuous boundary method is a new proposed fast obstacle avoidance algorithm. The second component is fuzzy neural network decision system for accomplishing the subgoal. The heuristic rules are imbedded on the fuzzy neural network to make an intelligent decision. The last one is a forgetting, reinforcing memory technique for the construction of external world map. The activation values of all activated memory cells in grid space are decreased monotonically and after all they are burned out. Therefore, after sufficient journey, robot can have a stationary world map even if the dynaic obstacles exist. Using the IMPS, several simulations show the efficient achievement of target point in unknown enviroment with obstcles of various shapes.

• Journal of the Korean Institute of Navigation
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• v.20 no.3
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• pp.65-71
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• 1996

Recently, the Track Control System which was separated from the Course Control System so-called Auto-Pilot has been developed for track-keeping in coastal area. From this year, the NAV Sub-committee in IMO commenced to consider the Performance Standard for the Track Control System vigorously. This system will be integrated with ECDIS and IBS so that captain/officers should analysis ship's motion characteristics accurately in the route planning using the electronic nautical charts. In this paper, a new Route Analysis Algorithm using fuzzy reasoning in route planning was proposed for 2, 700 TEU container ship. In order to verify the track-keeping, the author established a ship mathematical model and executed the simulation of the Route Analysis Algorithm at on-line condition with Pentium PC. The results of ship trajectories of the Route Analysis Algorithm were found to be effective to get track control automatically.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1254-1257
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• 1993

Presented in this paper is a newly developed motion planning method of an autonomous mobile robot(MAR) which can be applied to flexible manufacturing systems(FMS). The mobile robot is designed for transporting tools and workpieces between a set-up station and machines according to production schedules of the whole FMS. The proposed method is implemented based on an earlier developed real-time obstacle avoidance method which employs Kohonen network for pattern classification of sonar readings and fuzzy logic for local path planning. Particulary, a novel obstacle avoidance method for moving objects using a collision index, collision possibility measure, is described. Our method has been tested on the SNU mobile robot. The experimental results show that the robot successfully navigates to its target while avoiding moving objects.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.984-990
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• 2005

The aim of this paper is to investigate a control framework for mobile robots, operating in shared environment with humans. The Intelligent Space (iSpace) can sense the whole space and evaluate the situations in the space by distributing sensors. The mobile agents serve the inhabitants in the space utilizes the evaluated information by iSpace. The iSpace evaluates the situations in the space and learns the walking behavior of the inhabitants. The human intelligence manifests in the space as a behavior, as a response to the situation in the space. The iSpace learns the behavior and applies to mobile agent motion planning and control. This paper introduces the application of fuzzy-neural network to describe the obstacle avoidance behavior teamed from humans. Simulation results are introduced to demonstrate the efficiency of this method.

• ICROS
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• v.1 no.1
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.