  This paper proposes a method to navigate a mobile robot by estimating its
state over a number of distributed sensor networks (DSNs) such that it can
successively accomplish a sequence of tasks, i.e., its state enters each
targeted set and stays inside no less than the desired time, under a
resource-aware, time-efficient, and computation- and communication-constrained
setting.We propose a new robot state estimation and navigation architecture,
which integrates an event-triggered task-switching feedback controller for the
robot and a two-time-scale distributed state estimator for each sensor. The
architecture has three major advantages over existing approaches: First, in
each task only one DSN is active for sensing and estimating the robot state,
and for different tasks the robot can switch the active DSN by taking resource
saving and system performance into account; Second, the robot only needs to
communicate with one active sensor at each time to obtain its state information
from the active DSN; Third, no online optimization is required. With the
controller, the robot is able to accomplish a task by following a reference
trajectory and switch to the next task when an event-triggered condition is
fulfilled. With the estimator, each active sensor is able to estimate the robot
state. Under proper conditions, we prove that the state estimation error and
the trajectory tracking deviation are upper bounded by two time-varying
sequences respectively, which play an essential role in the event-triggered
condition. Furthermore, we find a sufficient condition for accomplishing a task
and provide an upper bound of running time for the task. Numerical simulations
of an indoor robot's localization and navigation are provided to validate the
proposed architecture.
