Abstract:
Simultaneous localisation and mapping (SLAM) is the process
of estimating the pose of a mobile robot and the locations
of landmarks by using sensors. When SLAM is cast as an
information extraction procedure, its quality can be defined as
the amount of uncertainty contained in the resultant estimation.
Due to the characteristic of the bearing-only sensor and
the geometry of the environment, the estimation uncertainty
relies critically on the amount of information obtained from
measurements and the efficiency of information extraction by
the estimator. These quantities are dependent on the relative
position between the robot and the landmarks, i.e., the path of
the robot motion. Therefore, a well planned path of motion
for the robot can significantly improve the SLAM quality.
A genetic algorithm is adopted in this research to design a
near-optimal one-step-ahead robot path subject to a multiple
of planning objectives. The use of genetic algorithm together
with a Pareto set, is proved to be efficient in reducing the
estimation uncertainty and improving the quality of SLAM by
simulation results.
