551 research outputs found
Behavioural strategy for indoor mobile robot navigation in dynamic environments
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
Multi-layer approach to motion planning in obstacle rich environment
A widespread use of robotic technology in civilian and military applications has
generated a need for advanced motion planning algorithms that are real-time implementable.
These algorithms are required to navigate autonomous vehicles through
obstacle-rich environments. This research has led to the development of the multilayer
trajectory generation approach. It is built on the principle of separation of
concerns, which partitions a given problem into multiple independent layers, and addresses
complexity that is inherent at each level. We partition the motion planning
algorithm into a roadmap layer and an optimal control layer. At the roadmap layer,
elements of computational geometry are used to process the obstacle rich environment
and generate feasible sets. These are used by the optimal control layer to generate
trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the
dynamics of the system, and the optimal control layer ignores the complexity of the
environment, thus achieving a separation of concern. This decomposition enables
computationally tractable methods to be developed for addressing motion planning
in complex environments. The approach is applied in known and unknown environments.
The methodology developed in this thesis has been successfully applied to a 6
DOF planar robotic testbed. Simulation results suggest that the planner can generate
trajectories that navigate through obstacles while satisfying dynamical constraints
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