Analysis and Comparison of Obstacle Avoidance Algorithms for Mobile Robots

Purpose of research. Mobile robotics is a discipline of great interest today due to the wide range of applications for which it has potential for; ranging from industry, services, military, to space exploration. One of the most challenging aspects in the development of this technology is the implementation of accurate and efficient navigation and positioning systems, since this function will ensure the autonomous operation of this equipment, providing flexibility and reliability in the tasks to which these mechanisms are assigned to. In this research work, an analysis and comparison of the performance and behavior of 5 different algorithms of obstacles evasion was made, with the implements of the navigation system from a differential drive mobile robot (MR), from an initial point to a target point.

Methods. Routes for MR take place within a structured map with various obstacles in its environment. The MR is modeled using the inverse kinematics equations provided by the robotics. In order to guarantee the expected behavior of the algorithms, this project started from the primordial logic of each one. Therefore, the sequence that each algorithm follows was analyzed and encoded using the MatLab software, since its Simulink plug-in is very useful and versatile for test simulations. For the tests, 10 routes were defined within the structured map, which was called the “test map”. To obtain the results, each algorithm was used to guide the mobile robot through each of the defined routes evaluating the distance and time used for each of them.

Results. For the analysis and comparison of the different simulated algorithms, an evaluation of the time and distance traveled was carried out to comply with 10 test routes with obstacles.

Conclusion. Algorithms can be classified into two classes: global planification (GP) and local planification (LP). GP is characterized by planning the route to be followed by the mobile robot prior to its movement, while LP plans in real time the route to be followed by the MR, a route which is calculated and recalculated iteratively based on the information from the environment outside the robot that is collected by the sensors. According to the results obtained, it can be concluded that LP algorithms have a superior performance to GP algorithms, so they are the most efficient for real applications. Although a correct combination of a GP algorithm with a LP could result in an optimal navigation system, which can overcome any type of obstacle and guide an MR efficiently through any type of environment no matter how complicated it is.

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