| Intelligent robotics, the product of advanced manufacturing technique, modern electronic and information science, has penetrated into almost every domain of our daily-life. Although they have been applied in various domains and some can communicate with us by simple sentences and sign language, as well as face expression, they are not endowed with human intelligence and adaptability. They just carry out routine programs or work reactively in a specified world. Artificial Intelligence (AI) aimed at thinking machine is still far from its ultimate goal after decades' efforts. Some researchers come to the conclusion that intelligence can never be generated from algorithms. Do we miss something in our exploration of AI and intelligent robots? Are we on the wrong direction? After reflection, people begin to look for the outlet from the nature, biology and ethology. The theme of this dissertation is proposed under this circumstance. It is not only a research on techniques and algorithms applied in computer science and control engineering, but also an adventure in the domain of human intelligence and cognitive psychology. Considering that motion ability is critical for intelligent robotic systems and temporal memory also plays an important role in human intelligence, the dissertation is focused on mobile robot navigation problem, synthesizing the spatial and temporal relationships of various data. By probing into the learning and utilizing process of non-symbol represented knowledge in artificial system, it is aimed to exploit a new path towards intelligence.The study in this dissertation is carried out around the spatial cognition problem of mobile robot systems, taking spatio-temporal pattern processing as the core of its methods. The problem is analysed from the viewpoint of the robot, with emphases on the learning and representation of spatial knowledge, the interaction between the environment and the agent, and the coupling of sensing and acting. Its immediate purpose is to find a practical navigation strategy that fit in with the perception and motion capabilities of real robots when there is lack of coordinate information and a priori map. A set of self-contained theory and methods are proposed, including the paradigm of intelligent robot .systems, the implicit cognitive map, temporal sequence processing network, the techniques for sensory data processing, the design and realization of a novel navigation strategy. This dissertation differs from other researches in its objective, viewpoint and approaches. Its main contributions are as follows:1. A set of knowledge-oriented paradigms is established after the comprehensive overview of existing system architectures and traditional paradigms of robot systems that discriminate from each other by the relationship between the Sense, Act and Plan primitives. The new paradigmsare described in the way world knowledge is organized in an intelligent robot system. Three knowledge levels are introduced, i.e. external world model, internal cognitive model and tacit knowledge. The intelligent level of robot systems are completely described and clearly discriminated in the new paradigms. The formation and utilization of implicit knowledge are explicitly stated. As the forth primitive, learning is seamlessly contained in the new framework.2. Based on the analysis of system capabilities, navigation tasks and working process from the standpoint of the robots, it is proposed that a spatio-temporal transformation procedure happens during the learning of spatial knowledge, which clarifies the importance of temporal memory in spatial cognition and elicits an approach to implement cognition maps of mobile robots.3. A non-symmetrical incremental associative network-Temporal Sequence Processing Network (TSPN) is proposed to implement the encoding and retrieval of complex sequences and the delay between sequence items. The analyses and experiments prove that its memory depth, capacity, content accessibility, flexibility, computational complexity and fault tolerance performanc...
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