• International Journal of Control, Automation, and Systems
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• 제5권3호
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• pp.251-268
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• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

• 대한안전경영과학회지
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• 제25권1호
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• pp.15-21
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• 2023

In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a "robot-based cargo loading and unloading system". In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the "robot-based cargo loading system". The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.

• 한국통신학회논문지
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• 제33권11B호
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• pp.1030-1037
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• 2008

본 논문에서는 다중 로봇 (multi-robot)을 활용한 응용분야 중, 미지의 영역에 대한 탐색 (exploration) 능력을 향상시켜서, 주어진 미로 (maze)에서 다중 로봇이 통신을 통해서 협력적으로 출구를 찾아가는 효율적인 방안을 제안하였다. 즉, 미로 형태의 임의의 환경을 생성한 후, 로봇을 무작위로 배치시켜 상호간에 통신을 통하여 출구로 신속히 모두 빠져나오는 문제를 다루고 있다. 미로탐색을 위해 다중 로봇의 지역 탐색에서 사용되었던, 프론티어 셀, 셀 유틸리티등 기존 연구를 활용하였다. 또한 로봇간의 다중홉 무선 통신 (multihop wireless communications)을 위해서 이동성 (mobility)에 강한 일종의 홉기반 (hop-by-hop) 라우팅인, 랜덤 베스킷 볼 라우팅을 채용하였다. 또한, 출구를 찾은 로봇이 일정한 확률에 의거하여 출구 앞에서 정지하거나 혹은, 빠져나가는 의사 결정을 하여, 이 확률적인 결정이 다른 로봇의 행동에 어떻게 영향을 주는지를 실험적으로 조사하였다. 즉, 출구를 찾은 로봇이 현재 위치에서 멈추어서, 통신 중계 지점 (relay)으로 어떻게 활동되어야 최적인지에 대한 문제를 모의 실험을 통해 파악해보았다.

• 대한기계학회논문집
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• 제16권4호
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• pp.675-683
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• 1992

본 연구에서는 저자에 의해 유도된 바 있는 베이지안 업데이트 모델을 초음파 센서를 갖는 실제 로봇에 주입하여 실험하였다. 초음파센서는 비교적 큰 빔(beam)구 경 때문에 단독의 측정치로도 넓은 영역을 감지하는데는 효율적이다. 그러나 실제상 황에서는 거울효과(specular reflection effect)라는 매우 심각한 문제점을 갖고 있으 며, 이는 지도의 질을 매우 저하시킨다. 이 효과를 상당히 줄일 수 있는, 단순하면 서도 실질적인 방법이 제안된다. 또한 본 논문에서는 이동로봇의 실시간 장애물 회 피를 위한 새로운 방법이 소개된다. 이 방법은 로봇의 현재 위치와, 점령영역과 비 점령 여역 사이의 경계선과 목표지점의 교차점까지의 거리를 이용하며, 점들이 실제 상황에서의 실험을 통해 입증된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.59.2-59
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• 2001

This paper presents an evolvable cooperation strategy based on a genetic programming for the interactive robot soccer game. The interactive robot soccer game has been developed to allow a person to join in the game dynamically and to reinforce entertainment characteristics. In this game, a cooperation strategy between humans and autonomous robots is very important in order to make the game more enjoyable. First of all, necessary action sets for the cooperation strategy and its strategy structure are presented. In the first stage, a blocking action that an autonomous robot cut off an enemy robot from disturbing the way of the human controlled robot has been considered. The success probability of the blocking action has beer obtained in ...

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• 로봇학회논문지
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• 제13권2호
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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.

• 한국산업융합학회 논문집
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• 제25권3호
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• pp.341-347
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• 2022

This paper presents a comparison result of two simultaneous localization and mapping (SLAM) algorithms for navigation that have been proposed in literature. The performances of Extended Kalman Filter (EKF) SLAM under Gaussian condition, FastSLAM algorithms using Rao-Blackwellised method for particle filtering are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on indoor environment. The results show that the Particle filter based FastSLAM has the better performance in terms of accuracy of localization and mapping. The experimental results are discussed and compared.

• 로봇학회논문지
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• 제15권4호
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• pp.398-403
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• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.176-180
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• 2003

The paper presents an efficient method of extracting line segment in a local map of a robot's surroundings. The local map is composed of 2-D grids that have both the occupancy and orientation probabilities using sonar sensors. To find the shape of an object in a local map from orientation information, the orientations are clustered into several groups according to their values. The line segment is , then, extracted from the clusters based on Hough transform. The proposed technique is illustrated by experiments in an indoor environment.