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Low-Cost Autonomous Vehicle for Inventory Movement in Warehouses
- 1. Low-Cost Autonomous Vehicle for Inventory Movement in Warehouses FAISAL ALAM ASSISTANT PROFESSOR, COMPUTER ENGINEERING DEPTT. ZHCET, AMU ALIGARH, INDIA ALAMFAISAL654@GMAIL.COM ARPIT VARSHNEY STUDENT, COMPUTER ENGINEERING ZHCET, AMU ALIGARH, INDIA VARSHNEYARPIT158@GMAIL.COM KHAN SAAD BIN HASAN STUDENT, COMPUTER ENGINEERING ZHCET, AMU ALIGARH, INDIA KHANSAADBINHASAN@GMAIL.COM
- 2. Project Objectives • Automation can help Warehouses become more efficient, productive, robust, and cost-effective. • A variety of Autonomous Robots are used in large warehouses which can be expensive. • We intend to build cost-effective Autonomous Robots for small and medium scale Warehouses with reasonable accuracy.
- 3. Other approaches • Amazon[1] robots are monitored by a central administrator. The robots navigate by using QR Codes, which are pasted on the ground. • Diop et al,[2] Attached two cameras on the robot. The authors developed the algorithm to process the images from cameras to achieve robot position and orientation. • Deepu et al,[3] Demonstrates the approach of Robot Navigation using Laser Light and the single-camera attached to the robot.
- 6. Our approach • The Source and Destination are provided by the user in the environment visible to camera. • A path is made from source to destination using the A- star Algorithm. • A colored marker is placed on the robot which is used to estimate the robot’s location and the heading.
- 7. Our approach • A line is drawn from the yellow to the purple marker which gives the Heading. • The decision to move the next step is taken using the heading of the robot, the robot’s current location, and the next coordinate in the path to follow.
- 8. Evaluation Parameters • Cost and Accuracy: There is a trade-off between the accuracy of the robot and the cost required to build it. • Scalability: Using an overhead camera for tracking, robots can easily be scaled up, to track and guide multiple robots . • Backup system: Redundancy should be available that could be used in case of any failure with the primary system. • Path Following and Reaching Destination: To track the deviation while the robot is traversing the estimated path and check whether the robot can reach the destination or not.
- 9. Evaluation Methodology • We Evaluate the results on the success rate of the robot in reaching the destination and the deviation in the path to reach the destination. • We have considered that if the distance of the robot and destination is less than 20cm or 100px, then the robot is on the destination point. • We draw the path that the robot should move on and the path that the robot moved on. We compare these paths to measure how close the robot was to the destination.
- 10. Evaluation Results • The computed path is shown in orange color and the actual path traversed by the robot is shown using the blue color.
- 11. Evaluation Results • Out of 10 times, The robot was able to reach the destination 6 times. Hence, the accuracy of our model should be close to 60%. • The robot is built with cheap hardware which is easily available making our robot very affordable. • The robot also uses an overhead camera which facilitates greater scalability.
- 12. Conclusion and Further work • We have built a cheap and fairly accurate robot. It is versatile and can work in varied environments. Given the backup system and an overhead camera, the robot is fairly secure and scalable. • The scope of the work can be enhanced in the future by employing novel ways of increasing accuracy with little changes in cost.
- 13. References [1] D'Andrea, Raffaello. "Guest editorial: A revolution in the warehouse: A retrospective on kiva systems and the grand challenges ahead." IEEE Transactions on Automation Science and Engineering 9.4 (2012): 638-639. [2] Diop, M., Ong, L. Y., Lim, T. S., & Lim, C. H. (2016, April). A computer vision-aided motion- sensing algorithm for mobile robot's indoor navigation. In 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC) (pp. 400-405). IEEE. [3] Deepu, R., B. Honnaraju, and S. Murali. "Path generation for robot navigation using a single camera." Procedia Computer Science 46 (2015): 1425-1432.