Path Planning in a Mobile Robot

Path Planning in a Mobile Robot

Diego Alexander Tibaduiza Burgos, Maribel Anaya Vejar
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-2658-4.ch007
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Abstract

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.
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Brief Review On Path Planning

Research in path planning is an area of great interest in robotics because of the versatility it gives a robot to perform its work with reliability and autonomy. The domain of this ability gives the robot the option of avoiding collisions and obtaining different free collision paths for it to move in a workspace based on the user-defined criteria.

Currently, a large numbers of robots are equipped with sensors that allow them to obtain information from the environment. These sensors can provide global or local information to detect obstacles, which allows calculating free-collision paths using some planning strategies to move the robot step-by-step or to define a complete path in a workspace (Tibaduiza, 2008).

For more than 30 years, mobile robot autonomy has been one of the main motivations for developing path-planning strategies to provide the necessary tools for moving the robots through different environments and under difficult conditions. Below are some jobs mobile robots perform on a regular basis.

Lozano (1983) presents an approach for computing constraints on the position of an object due to the presence of obstacles. The algorithms presented allow persons to characterize the position and orientation of objects as a single point in a Configuration Space when the objects and obstacles are polygons or polyhedral.

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