Performance evaluation of path planning algorithms for blind people

Abstract

Blind people face difficulties in identifying objects of interest and moving to them safely and efficiently in unfamiliar environments. Thanks to highperformance computers, high-quality sensors and artificial intelligence algorithms, it is possible to perform real-time tasks such as locating the user, generating occupancy grids that represent the environment and identifying objects of interest. From this information, paths can be generated that allow the user to reach a point of interest in an optimal way. This paper presents the performance evaluation of four path planning algorithms that were implemented in MATLAB and tested with synthetically generated occupancy grids, varying their size and occupancy percentage. The evaluation criteria include time to reach the goal, number of expanded cells and number of cells in the path. In addition, a single indicator that integrates all performance criteria is proposed to identify the best algorithm. The results show that the A* algorithm presents the best performance in static environments, under certain hardware requirements for data processing and restrictions on grid size for real-time applications. These findings expand the fields of application of path planning algorithms, quantify their performance under different conditions of the environment, and make them attractive for implementation in embedded systems.