Design and Development of Autonomous Delivery Robot
Not on the map. Why?
Abstract (arXiv)
The field of autonomous robotics is growing at a rapid rate. The trend to use increasingly more sensors in vehicles is driven both by legislation and consumer demands for higher safety and reliable service. Nowadays, robots are found everywhere, ranging from homes, hospitals to industries, and military operations. Autonomous robots are developed to be robust enough to work beside humans and to carry out jobs efficiently. Humans have a natural sense of understanding of the physical forces acting around them like gravity, sense of motion, etc. which are not taught explicitly but are developed naturally. However, this is not the case with robots. To make the robot fully autonomous and competent to work with humans, the robot must be able to perceive the situation and devise a plan for smooth operation, considering all the adversities that may occur while carrying out the tasks. In this thesis, we present an autonomous mobile robot platform that delivers the package within the VNIT campus without any human intercommunication. From an initial user-supplied geographic target location, the system plans an optimized path and autonomously navigates through it. The entire pipeline of an autonomous robot working in outdoor environments is explained in detail in this thesis.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 56 references that are in the map. First posted on arXiv 16 Mar 2021.
Builds on
16 of 56 references.
References · 16
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
- 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
- 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
- 2015Instance-aware Semantic Segmentation via Multi-task Network Cascadescited by 120
4 more
- 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 109
- 2014Salient Object Detection: A Surveycited by 98
- 2018Recent progress in semantic image segmentationcited by 39
- 2019Understanding Deep Learning Techniques for Image Segmentationcited by 38
Built on by
Not cited yet.