PhD ThesisDevelopment of behavioural strategies for indoor mobile navigation has become a challenging
and practical issue in a cluttered indoor environment, such as a hospital or factory, where
there are many static and moving objects, including humans and other robots, all of which
trying to complete their own specific tasks; some objects may be moving in a similar direction
to the robot, whereas others may be moving in the opposite direction. The key requirement
for any mobile robot is to avoid colliding with any object which may prevent it from reaching
its goal, or as a consequence bring harm to any individual within its workspace. This challenge
is further complicated by unobserved objects suddenly appearing in the robots path,
particularly when the robot crosses a corridor or an open doorway. Therefore the mobile
robot must be able to anticipate such scenarios and manoeuvre quickly to avoid collisions.
In this project, a hybrid control architecture has been designed to navigate within dynamic
environments. The control system includes three levels namely: deliberative, intermediate
and reactive, which work together to achieve short, fast and safe navigation. The deliberative
level creates a short and safe path from the current position of the mobile robot to its goal
using the wavefront algorithm, estimates the current location of the mobile robot, and extracts
the region from which unobserved objects may appear. The intermediate level links the
deliberative level and the reactive level, that includes several behaviours for implementing
the global path in such a way to avoid any collision.
In avoiding dynamic obstacles, the controller has to identify and extract obstacles from the
sensor data, estimate their speeds, and then regular its speed and direction to minimize the
collision risk and maximize the speed to the goal. The velocity obstacle approach (VO) is
considered an easy and simple method for avoiding dynamic obstacles, whilst the collision
cone principle is used to detect the collision situation between two circular-shaped objects.
However the VO approach has two challenges when applied in indoor environments. The
first challenge is extraction of collision cones of non-circular objects from sensor data, in
which applying fitting circle methods generally produces large and inaccurate collision cones
especially for line-shaped obstacle such as walls. The second challenge is that the mobile
robot cannot sometimes move to its goal because all its velocities to the goal are located
within collision cones. In this project, a method has been demonstrated to extract the colliii
sion cones of circular and non-circular objects using a laser sensor, where the obstacle size
and the collision time are considered to weigh the robot velocities. In addition the principle
of the virtual obstacle was proposed to minimize the collision risk with unobserved moving
obstacles. The simulation and experiments using the proposed control system on a Pioneer
mobile robot showed that the mobile robot can successfully avoid static and dynamic obstacles.
Furthermore the mobile robot was able to reach its target within an indoor environment
without causing any collision or missing the target