Document Type

Conference Proceeding

Faculty

Faculty of Computing, Health and Science

School

School of Engineering (SOE)

RAS ID

12793

Comments

This article was originally published as: Khusheef, A. S., Kothapalli, G. , & Tolouei Rad, M. (2011) Simulation of a mobile robot navigation system. Paper presented at the 19th International Congress on Modelling and Simulation. Australian Mathematical Sciences Institute. Perth, Australia. Original article available here.

Abstract

Mobile robots are used in various application areas including manufacturing, mining, military operations, search and rescue missions and so on. As such there is a need to model robot mobility that tracks robot system modules such as navigation system and visi on based object recognition. For the navigation system it is important to locate the position of the robot in surr ounding environment. Then it has to plan a path towards desired destination. The navigation system of a robot has to identify all potential obstacles in order to find a suitable path. The objective of this research is to develop a simulation system to identify difficulties facing mobile robot navigation in industrial environments, and then tackle these problems effectively. The simulation makes use of information provided by various sensors including vision, range, and force sensors. With the help of battery operated mobile robots it is possible to move objects around in any industry/manufacturing plant and thus minimize environmental impact due to carbon emissions and pollution. The use of such robots in industry also makes it safe to deal with hazardous materials. In industry, a mobile robot deals with many tools and equipment and therefore it has to detect and recognize these objects and then track them. In this paper, the object detection and recognition is based on vision sensors and then image processing techniques. Techniques cove red include Speeded Up Ro bust Features (SURF), template matching, and colour segmentation. If the robot detects the target in its view, it will track the target and then grasp it. However, if the object is not in the current view, the robot continues its search to find it. To make the mobile robot move in its environment, a number of basic path planning strategies have been used. In the navigation system, the robot navigates to the nearest wall (or similar obstacle) and then moves along that obstacle. If an obstacle is detected by the robot using the built-in ultrasonic range sensor, the robot will navigate around that obstacle and then continue moving along it. While the robot is self-navigating in its environment, it continues to look for the target. The robot used in this work robot is scalable for industrial applications in mining, search and rescue missions, and so on. This robot is environmentally friendly and does not produce carbon emissions. In this paper the simulation of path planning algorithm for an autonomous robot is presented. Results of modelling the robot in a real-world industrial environment for testing the robot’s navigation are also discussed.

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