4,215 research outputs found
Industrial Robot Collision Handling in Harsh Environments
The focus in this thesis is on robot collision handling systems, mainly collision detection
and collision avoidance for industrial robots operating in harsh environments
(e.g. potentially explosive atmospheres found in the oil and gas sector). Collision
detection should prevent the robot from colliding and therefore avoid a potential
accident. Collision avoidance builds on the concept of collision detection and aims
at enabling the robot to find a collision free path circumventing the obstacle and
leading to the goal position.
The work has been done in collaboration with ABB Process Automation Division
with focus on applications in oil and gas. One of the challenges in this work
has been to contribute to safer use of industrial robots in potentially explosive environments.
One of the main ideas is that a robot should be able to work together
with a human as a robotic co-worker on for instance an oil rig. The robot should
then perform heavy lifting and precision tasks, while the operator controls the steps
of the operation through typically a hand-held interface. In such situations, when
the human works alongside with the robot in potentially explosive environments, it
is important that the robot has a way of handling collisions.
The work in this thesis presents solutions for collision detection in paper A, B
and C, thereafter solutions for collision avoidance are presented in paper D and E.
Paper A approaches the problem of collision avoidance comparing an expert system
and a hidden markov model (HMM) approach. An industrial robot equipped with a
laser scanner is used to gather environment data on arbitrary set of points in the work
cell. The two methods are used to detect obstacles within the work cell and shows a different set of strengths. The expert system shows an advantage in algorithm
performance and the HMM method shows its strength in its ease of learning models
of the environment. Paper B builds upon Paper A by incorporating a CAD model
of the environment. The CAD model allows for a very fast setup of the expert
system where no manual map creation is needed. The HMM can be trained based
on the CAD model, which addresses the previous dependency on real sensor data
for training purposes.
Paper C compares two different world-model representation techniques, namely
octrees and point clouds using both a graphics processing unit (GPU) and a central
processing unit (CPU). The GPU showed its strength for uncompressed point clouds
and high resolution point cloud models. However, if the resolution gets low enough,
the CPU starts to outperform the GPU. This shows that parallel problems containing
large data sets are suitable for GPU processing, but smaller parallel problems are
still handled better by the CPU.
In paper D, real-time collision avoidance is studied for a lightweight industrial
robot using a development platform controller. A Microsoft Kinect sensor is used
for capturing 3D depth data of the environment. The environment data is used
together with an artificial potential fields method for generating virtual forces used
for obstacle avoidance. The forces are projected onto the end-effector, preventing
collision with the environment while moving towards the goal. Forces are also
projected on to the elbow of the 7-Degree of freedom robot, which allows for nullspace
movement. The algorithms for manipulating the sensor data and calculating
virtual forces were developed for the GPU, this resulted in fast algorithms and is the
enabling factor for real-time collision avoidance.
Finally, paper E builds on the work in paper D by providing a framework for
using the algorithms on a standard industrial controller and robot with minimal
modifications. Further, algorithms were specifically developed for the robot controller
to handle reactive movement. In addition, a full collision avoidance system
for an end-user application which is very simple to implement is presented.
The work described in this thesis presents solutions for collision detection and collision avoidance for safer use of robots. The work is also a step towards making
businesses more competitive by enabling easy integration of collision handling for
industrial robots
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
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