Xinhan Huang

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Robot perception is becoming more and more popular with the development of artificial intelligence in computer science. Many sensors are usually involved to get a better perception of the surrounding unknown environment, especially multi-sonar sensors because of their low cost, simplicity and convenience. However the information acquired from multi-sonar(More)
This paper proposes a new solution for reducing the number of sources of evidence to be combined in order to diminish the complexity of the fusion process required in some applications where the real-time constraint and strong computing resource limitation are of prime importance. The basic idea consists in selecting, among the whole set of sources of(More)
– This paper deals with enriched qualitative belief functions for reasoning under uncertainty and for combining information expressed in natural language through linguistic labels. In this work, two possible enrich-ments (quantitative and/or qualitative) of linguistic labels are considered and operators (addition, multiplication, division, etc) for dealing(More)
We consider in this work evidential sources of information and propose a very general Evidence Supporting Measure of Similarity (ESMS) for selecting the most coherent subset of sources to combine among all sources available at each instant. The methodology proposed here coupled with a DSmT-based fusion machine is tested in robotics for the automatic(More)
This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of(More)