In: 2019 IEEE/SICE International Symposium on System Integration (SII) (2019). Kato, Y., Morioka, K.: Autonomous robot navigation system without grid maps based on double deep Q-network and RTK-GNSS localization in outdoor environments. Oishi, S., Inoue, Y., Miura, J., Tanaka, S.: SeqSLAM: view-based robot localization and navigation. Santos, D.H.D., Goncalves, L.M.G.: A gain-scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot.
Ĭhoi, J.-H., Choi, B.-J.: Indoor moving and implementation of a mobile robot using hall sensor and Dijkstra algorithm. Wang, L., et al.: Multi-channel convolutional neural network based 3D object detection for indoor robot environmental perception. Neloy, A.A., Arman, A., Islam, M.S., Motahar, T.: Automated mobile robot with RFID scanner and self obstacle avoiding system. Morales, Y., Carballo, A., Takeuchi, E., Aburadani, A., Tsubouchi, T.: Autonomous robot navigation in outdoor cluttered pedestrian walkways. Pillai, S., Leonard, J.: Monocular SLAM supported object recognition. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Islam, M.Z.: Meals by mail getting popular. Stacking all these 3 modules, the ADR is built, tested and validated which shows significant improvement in terms of performance and usability comparing with other service robots. In the result analysis phase, the ODM exhibits an accuracy of 83.75%, RRS shows 92.3% accuracy and the PPS maintains an accuracy of 85.3%. This PPS requires a minimum speed of 200 RPM and 75 s duration for the robot to successfully relocate its position by reading an RFID tag. The Path Planning System (PPS) is combined with the output of the GCM, the RFID Reading System (RRS) and also by the binary results of ODM. On the other hand, an Object Detection Module (ODM) is built by Faster R-CNN with VGGNet-16 architecture for supporting path planning by detecting and recognizing obstacles.
#Fme shortest path finder algorithm verification
Landmark determination with Radio Frequency Identification (RFID) tags are placed in the path for identification and verification of source and destination, and also for the re-calibration of the current position. It uses a vector map of the path and calculates the shortest path by Grid Count Method (GCM) of Dijkstra’s Algorithm. The ADR is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. Alpha - N - A self-powered, wheel-driven Automated Delivery Robot (ADR ) is presented in this paper.
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