Research in Purdue SMART Lab

Description:  Machine learning is a field of artificial intelligence focused on algorithms that allow computers to learn, and has come to receive tremendous attention from the entire scientific community. Within this field, the SMART lab focuses on advanced deep learning and deep reinforcement learning methods with the goal of making robots more intelligent. We are specifically studying two major areas in detail. First, we study cognitive computing methods that model and learn the capacity for fast and accurate decision-making characteristic of humans, the better to improve decision-making by robots. Second, we study recognition technology so as to enable robots to recognize and judge the identity of an object/scene in real-time with facility equivalent to humans, even when faced with dynamic environments and limited information. We currently aim to apply our developments to diverse applications including navigation of autonomous robots/cars in dynamic environments, detection of malware/cyberattacks, prediction of the cognitive and affective states of humans, and allocating workloads within human-robot teams.

Grants: NSF, Purdue University
People: Wonse Jo, Shyam Sundar Kannan, Su Sun, Go-Eum Cha, Vishnunandan Venkatesh, Ruiqi Wang

Selected Publications:

• Ruiqi Wang, Weizheng Wang, and Byung-Cheol Min, "Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation", 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 23-27, 2022. Paper Link, Video Link, GitHub Link
• Su Sun and Byung-Cheol Min, "Active Tapping via Gaussian Process for Efficient Unknown Object Surface Reconstruction", 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on RoboTac 2021: New Advances in Tactile Sensation, Interactive Perception, Control, and Learning. A Soft Robotic Perspective on Grasp, Manipulation, & HRI, Prague, Czech Republic, Sep 27 – Oct 1, 2021. Paper Link
• Manoj Penmetcha and Byung-Cheol Min, "A Deep Reinforcement Learning-based Dynamic Computational Offloading Method for Cloud Robotics", IEEE Access, Vol. 9, pp. 60265-60279, 2021. Paper Link, Video Link
• Shyam Sundar Kannan, Wonse Jo, Ramviyas Parasuraman, and Byung-Cheol Min, "Material Mapping in Unknown Environments using Tapping Sound", 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Las Vegas, NV, USA, 25-29 October, 2020. Paper Link, Video Link

Description:  All life depends on water, and we are all citizens of watersheds; however, human activities often lead to contamination that can disrupt and disorganize both biological and social communities. Moreover, contamination of sediments with pollutants such as heavy metals can damage habitats for fish and other aquatic life, and even affect human health. Belated recognition of a water crisis can incur tremendous costs and require considerable recovery time, and also foster social and political strife. As such, there is great benefit in regular monitoring of water and sediment quality through sampling activities. The SMART lab is currently developing a novel cyber-physical system for water and sediment sampling, integrating control software and mobile robots to conduct autonomous monitoring and analysis. Overall, our research will increase knowledge of how to effectively sample water and sediments with robotic systems and verify the ability of cyber-physical systems to enable real-time data processing when monitoring water quality. We believe these contributions will significantly enhance the state-of-the-art in robotic environmental monitoring.

Grants: NSF, UNSA, Purdue University
People: Wonse Jo, Pou Hei Chan
Project Website: https://engineering.purdue.edu/PRWQ

Selected Publications:

• Jun Han Bae, Wonse Jo, Jee Hwan Park, Richard M. Voyles, Sara K. McMillan and Byung-Cheol Min, "Evaluation of Sampling Methods for Robotic Sediment Sampling Systems", IEEE Journal of Oceanic Engineering, Vol. 46, No. 2, pp. 542-554, April 2021. Paper Link, Video Link
• Jun Han Bae, Shaocheng Luo, Shyam Sundar Kannan, Yogang Singh, Bumjoo Lee, Richard M. Voyles, Mauricio Postigo-Malaga, Edgar Gonzales Zenteno, Lizbeth Paredes Aguilar, and Byung-Cheol Min, "Development of an Unmanned Surface Vehicle for Remote Sediment Sampling with a Van Veen Grab Sampler", 2019 MTS/IEEE OCEANS, Seattle, WA, USA, October 27-31, 2019. Paper Link, Video Link
• Wonse Jo, Jee Hwan Park, Yuta Hoashi, and Byung-Cheol Min, "Development of an Unmanned Surface Vehicle for Harmful Algae Removal", 2019 MTS/IEEE OCEANS, Seattle, WA, USA, October 27-31, 2019. Paper Link, Video Link
• Shaocheng Luo, Yogang Singh, Hanyao Yang, Jun Han Bae, J. Eric Dietz, Xiumin Diao, and Byung-Cheol Min, "Image Processing and Model-Based Spill Coverage Path Planning for Unmanned Surface Vehicles", 2019 MTS/IEEE OCEANS, Seattle, WA, USA, October 27-31, 2019. Paper Link
• Wonse Jo, Yuta Hoashi, Lizbeth Leonor Paredes Aguilar, Mauricio Postigo-Malaga, José Garcia-Bravo, and Byung-Cheol Min, "A Low-cost and Small USV Platform for Water Quality Monitoring", HardwareX, Vol. 6, e00076, October 2019. Paper Link, Source Codes Link, Video Link
  

Description:  Low-cost, open-source-based robotic platforms have great value and potential in many respects. For example, they allow researchers in the robotics field to conduct real experiments, advancing their research in a more practical and efficacious way, and they enable K-12 and college students to participate in hand-on activities and so learn robotics more effectively. They also offer opportunities for end users who are interested in robots, but have been priced out by high costs. However, only a very few low-cost, open-source robotic platforms are currently available. The SMART lab is applying software and hardware development experience accumulated over many years to develop a variety of robot types, such as mobile and aquatic robots, that are inexpensive and easy for anyone to build. We share all our source code and hardware-related materials through online repositories such as GitHub.

Grants: NSF, UNSA, Purdue University
People: Wonse Jo, Pou Hei Chan

Selected Publications:

• Wonse Jo, Jaeeun Kim, and Byung-Cheol Min, "ROS2 Open-Source Swarm Robot Platform: SMARTmBot", 2021 International Conference on Robotics and Automation (ICRA), Workshop on Robot Swarms in the Real World: From Design to Deployment - Live Demonstration, Xi'an, China, May 30 - June 5, 2021. GitHub Link, Video Link
  
• Jun Han Bae, Shaocheng Luo, Shyam Sundar Kannan, Yogang Singh, Bumjoo Lee, Richard M. Voyles, Mauricio Postigo-Malaga, Edgar Gonzales Zenteno, Lizbeth Paredes Aguilar, and Byung-Cheol Min, "Development of an Unmanned Surface Vehicle for Remote Sediment Sampling with a Van Veen Grab Sampler", 2019 MTS/IEEE OCEANS, Seattle, WA, USA, October 27-31, 2019. Paper Link, Video Link
• Wonse Jo, Jee Hwan Park, Yuta Hoashi, and Byung-Cheol Min, "Development of an Unmanned Surface Vehicle for Harmful Algae Removal", 2019 MTS/IEEE OCEANS, Seattle, WA, USA, October 27-31, 2019. Paper Link, Video Link
• Wonse Jo, Yuta Hoashi, Lizbeth Leonor Paredes Aguilar, Mauricio Postigo-Malaga, José Garcia-Bravo, and Byung-Cheol Min, "A Low-cost and Small USV Platform for Water Quality Monitoring", HardwareX, Vol. 6, e00076, October 2019. Paper Link, Source Codes Link, Video Link
  

Description:  As delivery robots become more able and also more necessary in delivering goods both quickly and economically, interest in utilizing robots for last-mile delivery also increases. However, existing research endeavors and services involving delivery robots still remain far from adequate to meet the growing demand in this area, not to mention far short of being fully incorporated into our lives. The SMART lab explores various practical and theoretical topics in robot delivery, including vehicle routing for drones, localization of a requested delivery spot, and social interaction between package recipients and delivery robots. To this end, we use mathematical methods to tackle optimization problems and conduct experimental methods based on user studies. We expect that this research will play a major role in allowing delivery robots to deliver packages more intelligently and effectively, like a professional human courier, and that it will improve human-delivery robot interaction while increasing robot autonomy.

Grant: Purdue University
People: Shyam Sundar Kannan

Selected Publications:

• Shyam Sundar Kannan and Byung-Cheol Min, "Autonomous Drone Delivery to Your Door and Yard", 2022 International Conference on Unmanned Aircraft Systems (ICUAS), Dubrovnik, Croatia, June 21-24, 2022. Paper Link, Video Link
• Shyam Sundar Kannan and Byung-Cheol Min, "Investigation on Accepted Package Delivery Location: A User Study-based Approach", 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Virtual, Melbourne, Australia, 17-20 October, 2021. Paper Link
• Shyam Sundar Kannan, Ahreum Lee, and Byung-Cheol Min, "External Human-Machine Interface on Delivery Robots: Expression of Navigation Intent of the Robot", 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Virtual, Vancouver, Canada, 8-12 August, 2021. Paper Link, Video Link
• Patchara Kitjacharoenchai, Byung-Cheol Min, and Seokcheon Lee, "Two Echelon Vehicle Routing Problem with Drones in Last Mile Delivery", International Journal of Production Economics, Vol. 25, 2020. Paper Link