• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2142-2148
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• 2015

This paper proposes novel real-time footstep planning and following methods for the navigation of humanoid robots. A footstep command is defined by a walking direction and step lengths for footstep planning. The walking direction is determined by a uni-vector field navigation method, and the allowable yawing range caused by hardware limitation is considered. The lateral step length is determined to avoid collisions between the two legs while walking. The sagittal step length is modified by a binary search algorithm when collision occurs between the robot body and obstacles in a narrow space. If the robot body still collides with obstacles despite the modification of the sagittal step length, the lateral step length is shifted at the next footstep. For footstep following, a walking pattern generator based on a 3-D linear inverted pendulum model is utilized, which can generate modifiable walking patterns using the zero-moment point variation scheme. Therefore, it enables a humanoid robot to follow the footstep command planned for each footstep. The effectiveness of the proposed method is verified through simulation and experiment.

• ETRI Journal
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• v.40 no.4
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• pp.471-482
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• 2018

This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization-based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly-exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.566-571
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• 2008

In this paper, we propose a footstep planning method of biped robot based on the Particle Swarm Optimization(PSO). We define configuration and locomotion primitives for biped robots in the 2 dimensional workspace. A footstep planning method is designed using learning process of PSO that is initialized with a population of random objects and searches for optima by updating generations. The footstep planner searches for a feasible sequence of locomotion primitives between a starting point and a goal, and generates a path that avoids the obstacles. We design a path optimization algorithm that optimizes the footstep number and planning cost based on the path generated in the PSO learning process. The proposed planning method is verified by simulation examples in cluttered environments.

• 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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.4
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• pp.441-447
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• 2015

This paper proposes a novel fuzzy-based multi-criteria decision making method and implements a footstep planner for humanoid robots with it. Humanoid robots require additional footstep planning process in addition to path planning for the autonomous navigation. Moreover, it is necessary to consider safety and energy consumption as well as path efficiency and multi-criteria decision making is indispensable. The proposed method can provide not only well- distributed and non-dominated, but also more preferable solutions for users. The planned footsteps by the proposed method were verified through simulation. The results indicate that the user's preference is properly reflected in optimized solutions maintaining solution quality.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.301-302
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• 2013

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.86-90
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• 2007

본 논문에서는 Particle Swarm Optimization(PSO) 기법을 이용한 이족보행로봇의 보행 계획방법을 제안한다. 이족보행로봇의 보행 프리미티브를 기반으로 PSO의 학습 및 군집 특성을 이용하여 장애물이 있는 작업공간에서 보행 계획을 수행하였다. 먼저 PSO의 탐색알고리즘을 사용하여 장애물을 회피하는 실행 가능한 보행 프리미티브들의 순서를 찾아내고 탐색된 순서를 바탕으로 경로 최적화 알고리즘을 수행하는 보행 계획방법을 제안하였다. 제안된 PSO 기반 이족보행로봇의 보행 계획방법은 모의실험을 통하여 발걸음 탐색 시간이 줄고 최적화된 보행 경로를 생성하는 것을 검증하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1029-1036
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• 2011

An energy-efficient reference walking trajectory generation algorithm is suggested utilizing allowable ZMP (Zero-Moment-Point) region, which maxmizes the energy efficiency for cyclic gaits, based on three-dimensional LIPM (Linear Inverted Pendulum Model) for biped robots. As observed in natural human walking, variable ZMP manipulation is suggested, in which ZMP moves within the allowable region to reduce the joint stress (i.e., rapid acceleration and deceleration of body), and hence to reduce the consumed energy. In addition, opimization of footstep planning is conducted to decide the optimal step-length and body height for a given forward mean velocity to minimize a suitable energy performance - amount of energy required to carry a unit weight a unit distance. In this planning, in order to ensure physically realizable walking trajectory, we also considered geometrical constraints, ZMP stability condition, friction constraint, and yawing moment constraint. Simulations are performed with a 12-DOF 3D biped robot model to verify the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.108-114
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• 2009

In this paper, we presents a behavior planning for humanoid robots using behavior primitive in 3 dimensional workspace. Also, we define behavior primitives that humanoid robot accomplishes various tasks effectively. Humanoid robot obtains information of the outside environment and its inner information from various sensors in complex workspace with various obstacles. We verify our approach on a developed small humanoid robot using embedded vision and sensor system in a experimental environment. The experimental results show that the humanoid robot performs its tasks fast and effectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.50-53
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• 2008

본 논문에서는 복잡한 환경에서 휴머노이드 로봇의 영상기반 운동계획을 제안하였다. 먼저 영상전처리 과정을 통해 작업환경에서 경로 계획으로 최적 경로를 탐색하고, 탐색된 경로의 거리와 방향각에 따라 퍼지규칙을 적용하여 보행 프리미티브를 선택하는 운동계획방법을 제안하였다. 다양한 장애물을 갖는 복잡한 환경에서 로봇의 보행 프리미티브를 사용하여 영상기반의 운동계획이 실시간으로 수행 가능하도록 설계하였다. 제안한 운동계획방법은 임베디드 비전 시스템을 사용한 휴머노이드 로봇을 실제 제작하여 실험을 통해 성능을 검증하였다.