SmartBot
Faculty Mentor
Stuart Steiner
Presentation Type
Poster
Start Date
5-8-2024 11:15 AM
End Date
5-8-2024 1:00 PM
Location
PUB NCR
Primary Discipline of Presentation
Computer Science
Abstract
As traffic management becomes increasingly complex and environmental concerns grow, the creation of autonomous vehicles represents a significant advancement. In this seam, our contribution comes in the form of SmartBot, a bot we've developed using a combination of robust hardware, computer vision, and deep learning technologies. We aim to enhance road safety by reducing accident risks and streamlining traffic control via autonomous navigation. Additionally, SmartBot is set to transform last-mile delivery by ensuring efficient, autonomous transport of products, thereby offering vital logistical support. It further provides guidance and navigation assistance, making navigation through complex environments like malls and airports seamless and precise. Through its applications, SmartBot not only contributes to creating safer, more efficient cities but also paves the way for a more interconnected and accessible world.
For hardware development, NVIDIA Jetson Nano was used. This chip is used for edge computing to navigate the intricacies of urban environments with unmatched precision. Coming to the software and algorithmic side, libraries and frameworks like OpenCV were used for real-time visual data processing, lane tracking, traffic sign recognition, and obstacle avoidance. The integration of TensorFlow and PyTorch enables the development of complex deep learning models for real-time decision-making, while the CUDA Toolkit and JetPack SDK will ensure optimal performance of AI and computer vision tasks. Additionally, we plan to enhance spatial awareness by the Point Cloud Library (PCL), with Pandas and NumPy facilitating the efficient analysis and handling of sensor data for informed navigation decisions.
SmartBot's ability to handle intricate deep learning algorithms and make decisions in real time will allow it to reinvent the performance of autonomous vehicles through the integration of robotics and artificial intelligence technology. The combination of advanced software with specialized hardware, as demonstrated by SmartBot, efficiently overcomes the issues associated with autonomous navigation. In addition to advancing the fields of intelligent robotics and autonomous vehicles, SmartBot also sets new standards in the industry and offers a creative, scalable solution to problems with urban mobility and environmental sustainability. This represents a major advancement in the development of smarter, more livable cities that are both inside and outside of buildings.
Recommended Citation
Bhatia, Rahat, "SmartBot" (2024). 2024 Symposium. 9.
https://dc.ewu.edu/srcw_2024/ps_2024/p2_2024/9
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
SmartBot
PUB NCR
As traffic management becomes increasingly complex and environmental concerns grow, the creation of autonomous vehicles represents a significant advancement. In this seam, our contribution comes in the form of SmartBot, a bot we've developed using a combination of robust hardware, computer vision, and deep learning technologies. We aim to enhance road safety by reducing accident risks and streamlining traffic control via autonomous navigation. Additionally, SmartBot is set to transform last-mile delivery by ensuring efficient, autonomous transport of products, thereby offering vital logistical support. It further provides guidance and navigation assistance, making navigation through complex environments like malls and airports seamless and precise. Through its applications, SmartBot not only contributes to creating safer, more efficient cities but also paves the way for a more interconnected and accessible world.
For hardware development, NVIDIA Jetson Nano was used. This chip is used for edge computing to navigate the intricacies of urban environments with unmatched precision. Coming to the software and algorithmic side, libraries and frameworks like OpenCV were used for real-time visual data processing, lane tracking, traffic sign recognition, and obstacle avoidance. The integration of TensorFlow and PyTorch enables the development of complex deep learning models for real-time decision-making, while the CUDA Toolkit and JetPack SDK will ensure optimal performance of AI and computer vision tasks. Additionally, we plan to enhance spatial awareness by the Point Cloud Library (PCL), with Pandas and NumPy facilitating the efficient analysis and handling of sensor data for informed navigation decisions.
SmartBot's ability to handle intricate deep learning algorithms and make decisions in real time will allow it to reinvent the performance of autonomous vehicles through the integration of robotics and artificial intelligence technology. The combination of advanced software with specialized hardware, as demonstrated by SmartBot, efficiently overcomes the issues associated with autonomous navigation. In addition to advancing the fields of intelligent robotics and autonomous vehicles, SmartBot also sets new standards in the industry and offers a creative, scalable solution to problems with urban mobility and environmental sustainability. This represents a major advancement in the development of smarter, more livable cities that are both inside and outside of buildings.
