Path Planning with Obstacle Avoidance
Mobile robots have been globally growing in popularity since the mid-to-late 1990s.They are utilized in a wide range of industries, including space, retail, transport, and telecommunications. Despite their growth in popularity, mobile robots still struggle in terms of pathfinding and obstacle avoida...
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2021
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| Online Access: | https://dspace.auk.edu.kw/handle/11675/8082 |
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| Summary: | Mobile robots have been globally growing in popularity since the mid-to-late 1990s.They are utilized in a wide range of industries, including space, retail, transport, and telecommunications. Despite their growth in popularity, mobile robots still struggle in terms of pathfinding and obstacle avoidance mechanisms. Nonetheless, extensive research has been conducted in the field since the mid-to-late 2000s. Consequently, mobile robots are gradually becoming more accustomed to moving in specific pathway points and simultaneously avoiding obstacles. Pathfinding is an essential part of robotic navigation. There are two types of navigation: local navigation and global navigation. Our research will pertain to both local and global navigation. Numerous distinct methods have been formulated for global navigation issues, including Dijkstra's algorithm, Voronoi graph, artificial potential field method, grids, and cell decomposition method. Within the local navigation field, modern researchers frequently utilize control methods such as fuzzy logic, neural network, neuro-fuzzy, and simulated annealing algorithm. Additionally, within the local navigation field, mobile robots can autonomously control their motion and orientation using equipped sensors, including an ultrasonic range finder, sharp infrared range, and vision. Our research will predominantly focus on the comparative implementation of various obstacle avoidance algorithms in conjunction with pathfinding mechanisms. Furthermore, our research aims to emphasize pathfinding algorithms in static environments. Accordingly, we will construct a mobile robot that runs the implemented algorithm. The proposed algorithm will analyze various directions in order to find suitable open spaces; the algorithm will subsequently pick the most suitable open directions while simultaneously moving the mobile robot towards its final destination |
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