Page 272 - Introduction to Autonomous Mobile Robots

6 Planning and Navigation

6.1 Introduction

This book has focused on the elements of a mobile robot that are critical to robust mobility:
the kinematics of locomotion; sensors for determining the robot’s environmental context;
and techniques for localizing with respect to its map. We now turn our attention to the
robot’s cognitive level. Cognition generally represents the purposeful decision-making and
execution that a system utilizes to achieve its highest-order goals.
In the case of a mobile robot, the specific aspect of cognition directly linked to robust
mobility is navigation competence. Given partial knowledge about its environment and a
goal position or series of positions, navigation encompasses the ability of the robot to act
based on its knowledge and sensor values so as to reach its goal positions as efficiently and
as reliably as possible. The focus of this chapter is how the tools of the previous chapters
can be combined to solve this navigation problem.
Within the mobile robotics research community, a great many approaches have been
proposed for solving the navigation problem. As we sample from this research background
it will become clear that in fact there are strong similarities between all of these approaches
even though they appear, on the surface, quite disparate. The key difference between vari-
ous navigation architectures is the manner in which they decompose the problem into
smaller subunits. In section 6.3 below, we describe the most popular navigation architec-
tures, contrasting their relative strengths and weaknesses.
First, however, in section 6.2 we discuss two key additional competences required for
mobile robot navigation. Given a map and a goal location, path planning involves identi-
fying a trajectory that will cause the robot to reach the goal location when executed. Path
planning is a strategic problem-solving competence, as the robot must decide what to do
over the long term to achieve its goals.
The second competence is equally important but occupies the opposite, tactical extreme.
Given real-time sensor readings, obstacle avoidance means modulating the trajectory of the
robot in order to avoid collisions. A great variety of approaches have demonstrated compe-
tent obstacle avoidance, and we survey a number of these approaches as well.