Path planning algorithms for industrial automated guided vehicles (AGVS) /
Smart warehouse becomes a vital component of logistics process automation, which essentially supports the productivity and cost reduction. Most Automated Guided Vehicles (AGVs) operated in warehouses use the traditional line following method to get the mobile robots to move around the factory which...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
Kuala Lumpur :
Kulliyyah of Engineering, International Islamic University Malaysia,
2019
|
| Subjects: | |
| Online Access: | http://studentrepo.iium.edu.my/handle/123456789/5069 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Smart warehouse becomes a vital component of logistics process automation, which essentially supports the productivity and cost reduction. Most Automated Guided Vehicles (AGVs) operated in warehouses use the traditional line following method to get the mobile robots to move around the factory which is convenient but has its flaws when it comes to avoiding collision. This research presents a new design solution of an Automated Guided Vehicles (AGVs) system for smart warehouse. Dijkstra's algorithm and A* algorithm are proposed for efficient global path planning. These algorithms are used to calculate the best path for all cases in the given scenario. All algorithms are tested with varying amount of obstacles with maps growing exponentially up to 500 by 500. The results are put into an equation that mimics the moment of an AGV, these results are compared and show that A* algorithm outperforms Dijkstra's algorithm when it comes to time taken in calculating the route by 53% but falls short when it comes to taking fewer turns by average of triple the amount. The deciding factor depends on the map size, number of obstacles around the map, linear and angular speeds of the AGVs, length between each grid of the map. A case study has been conducted to display how the factors stated previously effect the algorithms in finding the optimal path. In the case study 5 robots are placed randomly around the map with 25 random obstacles. Each robot finds the route to the end point using all proposed algorithms, the best of each robot is selected and all 5 robots are compared. The best algorithm is picked and used to get to the end point. In this case study Dijkstra's algorithm is the optimal path by being 4 seconds faster than A*. |
|---|---|
| Physical Description: | xvii, 94 leaves : colour illustrations ; 30cm. |
| Bibliography: | Includes bibliographical references (leaves 92-94). |