112,795 research outputs found
Neural Networks in Mobile Robot Motion
This paper deals with a path planning and intelligent control of an
autonomous robot which should move safely in partially structured environment.
This environment may involve any number of obstacles of arbitrary shape and
size; some of them are allowed to move. We describe our approach to solving the
motion-planning problem in mobile robot control using neural networks-based
technique. Our method of the construction of a collision-free path for moving
robot among obstacles is based on two neural networks. The first neural network
is used to determine the "free" space using ultrasound range finder data. The
second neural network "finds" a safe direction for the next robot section of
the path in the workspace while avoiding the nearest obstacles. Simulation
examples of generated path with proposed techniques will be presented.Comment: 9 Page
Determining robot actions for tasks requiring sensor interaction
The performance of non-trivial tasks by a mobile robot has been a long term objective of robotic research. One of the major stumbling blocks to this goal is the conversion of the high-level planning goals and commands into the actuator and sensor processing controls. In order for a mobile robot to accomplish a non-trivial task, the task must be described in terms of primitive actions of the robot's actuators. Most non-trivial tasks require the robot to interact with its environment; thus necessitating coordination of sensor processing and actuator control to accomplish the task. The main contention is that the transformation from the high level description of the task to the primitive actions should be performed primarily at execution time, when knowledge about the environment can be obtained through sensors. It is proposed to produce the detailed plan of primitive actions by using a collection of low-level planning components that contain domain specific knowledge and knowledge about the available sensors, actuators, and sensor/actuator processing. This collection will perform signal and control processing as well as serve as a control interface between an actual mobile robot and a high-level planning system. Previous research has shown the usefulness of high-level planning systems to plan the coordination of activities such to achieve a goal, but none have been fully applied to actual mobile robots due to the complexity of interacting with sensors and actuators. This control interface is currently being implemented on a LABMATE mobile robot connected to a SUN workstation and will be developed such to enable the LABMATE to perform non-trivial, sensor-intensive tasks as specified by a planning system
Dynamic Modelling and Adaptive Traction Control for Mobile Robots
Mobile robots have received a great deal of research in recent years. A
significant amount of research has been published in many aspects related to
mobile robots. Most of the research is devoted to design and develop some
control techniques for robot motion and path planning. A large number of
researchers have used kinematic models to develop motion control strategy for
mobile robots. Their argument and assumption that these models are valid if the
robot has low speed, low acceleration and light load. However, dynamic
modelling of mobile robots is very important as they are designed to travel at
higher speed and perform heavy duty work. This paper presents and discusses a
new approach to develop a dynamic model and control strategy for wheeled mobile
robot which I modelled as a rigid body that roles on two wheels and a castor.
The motion control strategy consists of two levels. The first level is dealing
with the dynamic of the system and denoted as Low level controller. The second
level is developed to take care of path planning and trajectory generation
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