1.1 RFID Collision Problem and Research Progress of Anti-Collision
7
complex dynamics. They concluded that the internal neural expression pattern
produced by an external stimulus is the result of complex dynamic behaviors in
the brain (perception) cortex. Freeman further proposed that the dynamic behavior
of the olfactory bulb in the brain can be described by a three-dimensional chaotic
attractor with multiple volumes, and these “volume” structures can be considered
as memory trajectories formed through long-term autonomous learning by the
brain’s nerve tissue [40]. In the absence of external stimuli, the system is in a
high-dimensional iterative search mode, and the search trajectories are different
volumes. But once the excitation signal is received, the dynamic behavior of the
system appears to be restricted to a certain “volume” for periodic vibration, and this
specific volume just reflects the characteristics of the external excitation signal. If
the excitation signal input to the system stops, the system will immediately switch
to the high-dimensional iterative search mode. According to their work, the activity
of the nervous system is maintained in a chaotic state until the sensor interrupts
this behavior. The result of this process is that a strange attractor representing the
newly introduced excitation appears, and the function of chaos is to provide the
system with sufficient flexibility and robustness during the migration process of
different perception states. Based on analyzing and summarizing a large amount
of experimental data, Freeman proposed a dynamic model of the animal olfactory
system, called “K-sets” [37]. The scholars Harter and Kozma of the University
of Memphis in the United States gave the discrete form of the K-sets model in
2005 and applied it in the field of navigation control [41]. The selection of control
parameters in the system is obtained based on evolutionary learning, and at the same
time, an unsupervised learning strategy is adopted. Subsequently, Fukui University
Islam, Italy University of Catania Arena, and American scholar Kozma, etc. also
researched on navigation robots based on the K-sets model from different angles
and different fields [42–44]. It is worth noting that due to the academic frontier and
interdisciplinary nature of the research in the above fields, related research papers
have been published in the top international journals such as Nature and Science
in recent years, which have attracted widespread attention from scholars in many
fields [34, 45–47]. The EU framework program SPARK, NASA’s Mars Exploration
Program, and the Australian Collaborative Research Strategic Project CRC, etc.
all take “intelligent information perception and fusion” related research as a key
support research direction, and apply it to spacecraft, robots, and intelligence. In
transportation and other related fields, other major developed countries have successively carried out exploratory research work in this field. “Intelligent Perception
Technology” and “Intelligent Information Processing” are also listed as frontier
research fields and priority development themes in the outline of my country’s
medium and long-term scientific and technological development plan (2006–2020).
However, the problem of the system’s weak adaptability to the environment has
not been well resolved, which has bothered and affected the substantial progress of
the technology. During his Ph. D. study in Australia, the first author of this book,
under the guidance and cooperation of Prof. Jonathan Mantan from the Department
of Electronic Engineering of the University of Melbourne and Dr. Robert Stewart
from the Australian Commonwealth Scientific and Industrial Research Organization
7
complex dynamics. They concluded that the internal neural expression pattern
produced by an external stimulus is the result of complex dynamic behaviors in
the brain (perception) cortex. Freeman further proposed that the dynamic behavior
of the olfactory bulb in the brain can be described by a three-dimensional chaotic
attractor with multiple volumes, and these “volume” structures can be considered
as memory trajectories formed through long-term autonomous learning by the
brain’s nerve tissue [40]. In the absence of external stimuli, the system is in a
high-dimensional iterative search mode, and the search trajectories are different
volumes. But once the excitation signal is received, the dynamic behavior of the
system appears to be restricted to a certain “volume” for periodic vibration, and this
specific volume just reflects the characteristics of the external excitation signal. If
the excitation signal input to the system stops, the system will immediately switch
to the high-dimensional iterative search mode. According to their work, the activity
of the nervous system is maintained in a chaotic state until the sensor interrupts
this behavior. The result of this process is that a strange attractor representing the
newly introduced excitation appears, and the function of chaos is to provide the
system with sufficient flexibility and robustness during the migration process of
different perception states. Based on analyzing and summarizing a large amount
of experimental data, Freeman proposed a dynamic model of the animal olfactory
system, called “K-sets” [37]. The scholars Harter and Kozma of the University
of Memphis in the United States gave the discrete form of the K-sets model in
2005 and applied it in the field of navigation control [41]. The selection of control
parameters in the system is obtained based on evolutionary learning, and at the same
time, an unsupervised learning strategy is adopted. Subsequently, Fukui University
Islam, Italy University of Catania Arena, and American scholar Kozma, etc. also
researched on navigation robots based on the K-sets model from different angles
and different fields [42–44]. It is worth noting that due to the academic frontier and
interdisciplinary nature of the research in the above fields, related research papers
have been published in the top international journals such as Nature and Science
in recent years, which have attracted widespread attention from scholars in many
fields [34, 45–47]. The EU framework program SPARK, NASA’s Mars Exploration
Program, and the Australian Collaborative Research Strategic Project CRC, etc.
all take “intelligent information perception and fusion” related research as a key
support research direction, and apply it to spacecraft, robots, and intelligence. In
transportation and other related fields, other major developed countries have successively carried out exploratory research work in this field. “Intelligent Perception
Technology” and “Intelligent Information Processing” are also listed as frontier
research fields and priority development themes in the outline of my country’s
medium and long-term scientific and technological development plan (2006–2020).
However, the problem of the system’s weak adaptability to the environment has
not been well resolved, which has bothered and affected the substantial progress of
the technology. During his Ph. D. study in Australia, the first author of this book,
under the guidance and cooperation of Prof. Jonathan Mantan from the Department
of Electronic Engineering of the University of Melbourne and Dr. Robert Stewart
from the Australian Commonwealth Scientific and Industrial Research Organization