g., how do we reposition the robots to increase the communication
bandwidth).
The analysis of MWSN can make use of many results from prior robotics
research on cooperative mobile robots [7, 30, 34, 46] including methods to
estimate robot position (localization) using SLAM [12, 15, 33, 49], attain area
coverage [50], environment mapping [47], and flight formation [13]. Many of
these algorithms are based on stochastic estimation techniques using the Excollection
of sensors, it also expands the coverage and fault tolerance of a
1 Introduction
Arlington, TX 76118
35
Dan O. Popa and Frank L. Lewis
tended Kalman Filter that integrates navigational information with external
sensor information in a recursive algorithm [28]. Examples of robot team tasks
have been investigated on a variety of hardware platforms, for example foraging,
ant colony behavior, robotic soccer, map making, area searching, mine
sweeping, etc [2]. Examples of relevant sensor deployment scenarios considered
in the past are the distributed algorithm for odor localization in [20], battery
charging behaviors for a mobile robot team in [32], object tracking [8, 56, 57],
target classification [30], distributed control [48], and sensor validation [1].
While multiple vehicle localization and sensor fusion are classic problems
in robotics, the problem of distributed field variable estimation is typically relevant
to charting and prediction in oceanography and meteorology [4, 13, 14,
17].
Pages:
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96