• 로봇학회논문지
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• 제14권2호
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• pp.104-113
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• 2019

Object manipulation in cluttered environments remains an open hard problem. In cluttered environments, grasping objects often fails for various reasons. This paper proposes a novel task and motion planning scheme to grasp objects obstructed by other objects in cluttered environments. Task and motion planning (TAMP) aims to generate a sequence of task-level actions where its feasibility is verified in the motion space. The proposed scheme contains an open-loop consisting of three distinct phases: 1) Generation of a task-level skeleton plan with pose references, 2) Instantiation of pose references by motion-level search, and 3) Re-planning task based on the updated state description. By conducting experiments with simulated robots, we show the high efficiency of our scheme.

• 전기전자학회논문지
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• 제19권2호
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• pp.160-167
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• 2015

We present an experimental implementation of the inexpensive microwave security sensor that can detect both static and slowly moving objects in cluttered environment. The prototype consists of a frequency-modulated continuous wave radar sensor, control board or computer and software. The prototype was tested in a cluttered indoor environment. In case of intrusion or change of environment the sensor will give an alarm, determine the location of new object, change in its location and can detect a slowly moving target. To make a low-cost unit we use commercially available automotive radar and own signal processing techniques for object detection and tracking. The intruder detection is based on a comparison between current 'image' in memory and 'no-intrusion' reference image. The main challenge is to develop a reliable technique for detection of a relatively low-magnitude object signals hidden in multipath clutter echo signals. Various experimental measurements and computations have shown the feasibility and performance of the system.

• 한국정밀공학회지
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• 제28권10호
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.

• 로봇학회논문지
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• 제17권3호
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• pp.255-263
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• 2022

3D object detection pipelines often incorporate RGB-based object detection methods such as YOLO, which detects the object classes and bounding boxes from the RGB image. However, in complex environments where objects are heavily cluttered, bounding box approaches may show degraded performance due to the overlapping bounding boxes. Mask based methods such as Mask R-CNN can handle such situation better thanks to their detailed object masks, but they require much longer time for data preparation compared to bounding box-based approaches. In this paper, we present a 3D object recognition pipeline which uses either the YOLO or Mask R-CNN real-time object detection algorithm, K-nearest clustering algorithm, mask reduction algorithm and finally Principal Component Analysis (PCA) alg orithm to efficiently detect 3D poses of objects in a complex environment. Furthermore, we also present an improved YOLO based 3D object detection algorithm that uses a prioritized heightmap clustering algorithm to handle overlapping bounding boxes. The suggested algorithms have successfully been used at the Artificial-Intelligence Robot Challenge (ARC) 2021 competition with excellent results.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• Transactions on Control, Automation and Systems Engineering
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• 제2권4호
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• pp.235-243
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• 2000

The probabilistic data association filter(PDAF) is known to provide better tracking performance than the standard Kalman filter(KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al[7], in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom[7], is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2348-2350
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• 1998

The hybrid control system for a wheeled mobile robot with nonholonomic constraints to perform a cluttered environment maneuver is proposed. The proposed hybrid control system consists of a continuous state system for the trajectory control, a discrete state system for the motion and orientation control, and an interface control system for the interaction process between the continuous dynamics and the discrete dynamics The continuous control systems are modeled by the switched systems with the control of driving wheels, and the digital automata for motion control are modeled and implemented by the abstracted motion of mobile robot. The motion control tasks such as path generation, motion planning, and trajectory control for a cluttered environment are investigated as the applications by simulation studies.

• 한국음향학회지
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• 제40권2호
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• pp.109-120
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• 2021

본 논문은 고밀도 클러터 환경 비선형 표적에 대해서 수동소나 자동표적추적 자료결합 게이트를 강인하게 적용하기 위한 H∞ 놈 기반의 자료결합 게이트 기법을 제안한다. 표적추적을 위한 자료결합 기법은 유효 측정 범위인 유효 게이트 내에 있는 측정치를 자료결합의 후보대상으로 선택한다. 자료결합에서의 유효 게이트 범위가 적정하지 않거나 고밀도 클러터 환경에서 자료결합이 수행되면, 클러터 측정치의 간섭을 더욱 받게 되어 표적추적의 강인성을 유지하기 어렵다. 이러한 문제를 해결하기 위해서, 본 논문은 가우시안 분포 가정 및 추적 오차 공분산 기반의 기존 3-σ 게이트 기법에 H∞ 놈 기반의 이분법 알고리즘을 결합하여 적용한 새로운 게이팅 기법을 제안한다. 제안 기법은 클러터의 간섭을 완화시키고, 비선형 기동표적을 견실하게 추적하게 한다. 해석적인 분석 방법과 수평방위 및 수직방위의 측정치를 모의한 신호를 활용한 시뮬레이션을 통해 자료결합의 강인함이 기존 기법에 대비하여 향상됨을 확인하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권9호
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• pp.401-408
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• 2006

In a multi-sensor multi-target tracking systems, the local sensors have the role of tracking the target and transferring the measurements to the fusion center. The measurements from the same target can arrive out of sequence called the out-of-sequence measurements(OOSMs). Out-of-sequence measurements can arise at the fusion center due to communication delay and varying preprocessing time for different sensor platforms. In general, the track fusion occurs to enhance the tracking performance of the sensors using the measurements from the sensors at the fusion center. The target informations can wive at the fusion center with the clutter informations in cluttered environment. In this paper, the OOSM update step with MPDA(Most Probable Data Association) is introduced and tested in several cases with the various clutter density through the Monte Carlo simulation. The performance of the MPDA with OOSM update step is compared with the existing NN, PDA, and PDA-AI for the air target tracking in cluttered and out-of-sequence measurement environment. Simulation results show that MPDA with the OOSM has compatible root mean square errors with out-of-sequence PDA-AI filter and the MPDA is sufficient to be used in out-of-sequence environment.

• 제어로봇시스템학회논문지
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• 제16권11호
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• pp.1125-1135
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• 2010

A fundamental characteristic of PMHT (Probabilistic Multi-Hypothesis Tracker) is that the number of targets and initial states of targets in the surveillance area must be a priori known. This requirement is impossible to fulfil in almost every realistic scenario. In the paper, we present two track initiation methods to solve the problem. The proposed track initiation methods are 2-point track initiation and Hough transform track initiation, and they are used to evaluate track initial states and weights for FTD (False Track Discrimination) of the PMHT algorithm. Also suggested as automatic target detection for tracking systems that combines track initiation for target detection with the PMHT algorithm for target tracking in a cluttered environment. A series of Monte-Carlo simulation runs is employed to evaluate the overall system performance with the two track initiation methods and the results are compared and analyzed.