We propose a planning algorithm to automatically generate a robust behavior plan(RBP)with which mobile robots can achive their task goal from any initial states under dynamically changing environments. For this, task description space(TDS)is formulated, where a redundant task configuration space and simulation model of physical space are employed. Successful task episodes are collected, where A algorithm is employed. Interesting TDS state vectors are extracted, where occurrence frequency is used. Clusters of TDS state vectors are found by using state transition tuples and features of state transition tuples. From these operations, characteristics of successfully performed tasks by a simulator are abstracted and generalized. Then, a robust behavior plan is constructed as an ordered tree structure, where nodes of the tree are represented by attentive TDS state vector of each cluster. The validity of our method is tested by real robot's experimentation for a box-pushing-into-a-goal task.

In this paper, we introduce visual contexts in terms of types and utilization methods for robust object recognition with intelligent mobile robots. One of the core technologies for intelligent robots is visual object recognition. Robust techniques are strongly required since there are many sources of visual variations such as geometric, photometric, and noise. For such requirements, we define spatial context, hierarchical context, and temporal context. According to object recognition domain, we can select such visual contextx. We also propose a unified framework which can utilize the whole contexts and validates it in real working environment. Finally, we also discuss the furture research directions of object recognition technologies for intelligent robots.

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home –like environment.

기존 DGPS 기준국용 GPS 수신기의 초기동기 방법은 잡음환경에 대한 강건성 향상을 위해 동기 누적 기법과 비동기 누적 기법을 함께 이용하고 있다. 그러나, 기존 DGP통 기준국용 GPS 초기동기 방법은 잡음환경에서 발생하는 신호획득 손실 중에서 우세한 성분인 비동기 누적 손실이 발생할 뿐만 아니라 잡음세기가 커질수록 비동기 누적 손실도 커지는 문제가 있다. 본 논문에서는 잡음환경에 강인한 DGPS 기준국을 위해 기존 GPS 초기동기의 비동기 누적 손실 문제를 해결한 새로운 GPS 초기동기 방법을 제안하고, 제안하는 GPS 초기동기 방법이 비동기 누적 손실을 억제하는 효과가 있음을 보인다. 그리고, 평균 초기동기 획득시간 측면에서 제안하는 GPS 초기동기 방법이 기존 GPS 초기동기 방법이 검색해야 할 셀의 개수 보다 더 적은 셀을 검색하는 이점이 있음을 보인다. 마지막으로 GPS 시뮬레이터를 이용한 모의실험을 통해 제안하는 GPS 초기동기 방법이 잡음세기가 증가한 환경에서 높은 신호대 잡음비로 GPS 신호를 획득할 수 있음을 확인한다.