Soft Robotic Actuation and Triboelectric Energy Harvesting: Towards Sustainable Autonomous Systems

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Abstract:

The increasing demand for sustainable agriculture and autonomous monitoring systems has accelerated the need for intelligent technologies capable of operating independently in remote environments. This research presents the design and development of a self-powered smart agriculture platform that integrates hybrid renewable energy harvesting with Edge Artificial Intelligence (Edge-AI) for real-time weather monitoring, rainfall prediction, and intelligent irrigation management. The proposed system combines three renewable energy sources: a Vertical Axis Wind Turbine (VAWT), a rotary Triboelectric Nanogenerator (TENG), and solar photovoltaic panels. The VAWT is designed to efficiently harvest low-speed wind energy, while the rotary TENG converts rotational mechanical motion into electrical energy through the triboelectric effect. Solar energy complements the system by providing additional power during daylight hours. Together, these renewable sources form a hybrid energy harvesting architecture capable of delivering continuous and reliable power for autonomous agricultural applications. A custom hybrid power management circuit was developed to integrate, regulate, and store energy generated from the three renewable sources. The circuit incorporates rectification, voltage regulation, battery charging and protection, DC-DC conversion, and intelligent energy management to ensure a stable power supply for embedded electronics while maximizing energy utilization. The harvested energy powers a Raspberry Pi-based Edge-AI platform equipped with environmental sensors and a camera module. Deep learning models process visual weather data together with sensor measurements to classify weather conditions, predict rainfall, and generate intelligent irrigation recommendations. Performing inference locally eliminates dependence on cloud connectivity, reduces latency, improves reliability, and enables continuous operation in rural areas with limited internet access. This interdisciplinary research integrates renewable energy harvesting, mechanical system design, circuit development, embedded electronics, and artificial intelligence into a unified autonomous platform for precision agriculture. The proposed framework demonstrates the feasibility of powering intelligent agricultural monitoring systems entirely through renewable energy while enabling real-time decision-making using Edge-AI. The research contributes toward scalable, energy-independent technologies that support sustainable farming, efficient resource utilization, and climate-resilient agricultural practices.


  Date and Time

  Location

  Hosts

  Registration


  • Add_To_Calendar_icon Add Event to Calendar
  • 21000 West Ten Mile Road
  • Southfield, MI, 48075-1058
  • Southfield, Michigan
  • United States 48075
  • Building: College of Engineering, Lawrence Technological University
  • Room Number: Conference Room at Department of ECE
  • Contact Event Host

  • Dr. Manisha Guduri

    mguduri@ltu.edu

    linkedin.com/in/dr-manisha-guduri-a2b307b4

  • Starts 03 August 2026 04:00 AM UTC
  • Ends 28 August 2026 04:00 AM UTC
  • No Admission Charge

  Speakers

Preetham of Lawrence Technological University

Topic:

Soft Robotic Actuation and Triboelectric Energy Harvesting: Towards Sustainable Autonomous Systems

Abstract (300 -500 words)*
The increasing demand for sustainable agriculture and autonomous monitoring systems has accelerated the need for intelligent technologies capable of operating independently in remote environments. This research presents the design and development of a self-powered smart agriculture platform that integrates hybrid renewable energy harvesting with Edge Artificial Intelligence (Edge-AI) for real-time weather monitoring, rainfall prediction, and intelligent irrigation management. The proposed system combines three renewable energy sources: a Vertical Axis Wind Turbine (VAWT), a rotary Triboelectric Nanogenerator (TENG), and solar photovoltaic panels. The VAWT is designed to efficiently harvest low-speed wind energy, while the rotary TENG converts rotational mechanical motion into electrical energy through the triboelectric effect. Solar energy complements the system by providing additional power during daylight hours. Together, these renewable sources form a hybrid energy harvesting architecture capable of delivering continuous and reliable power for autonomous agricultural applications. A custom hybrid power management circuit was developed to integrate, regulate, and store energy generated from the three renewable sources. The circuit incorporates rectification, voltage regulation, battery charging and protection, DC-DC conversion, and intelligent energy management to ensure a stable power supply for embedded electronics while maximizing energy utilization. The harvested energy powers a Raspberry Pi-based Edge-AI platform equipped with environmental sensors and a camera module. Deep learning models process visual weather data together with sensor measurements to classify weather conditions, predict rainfall, and generate intelligent irrigation recommendations. Performing inference locally eliminates dependence on cloud connectivity, reduces latency, improves reliability, and enables continuous operation in rural areas with limited internet access. This interdisciplinary research integrates renewable energy harvesting, mechanical system design, circuit development, embedded electronics, and artificial intelligence into a unified autonomous platform for precision agriculture. The proposed framework demonstrates the feasibility of powering intelligent agricultural monitoring systems entirely through renewable energy while enabling real-time decision-making using Edge-AI. The research contributes toward scalable, energy-independent technologies that support sustainable farming, efficient resource utilization, and climate-resilient agricultural practices.

Biography:

Preetham Reddy Aerrabolu is a Mechanical Engineering researcher pursuing a Master of Science in Mechanical Engineering at Lawrence Technological University, Michigan, USA. His research interests span robotics, soft actuators, artificial intelligence, edge computing, renewable energy systems, and autonomous engineering applications. His work focuses on developing intelligent mechanical systems by integrating advanced design methodologies, computational modeling, and machine learning techniques. His primary research investigates bio-inspired robotic locomotion using Electrohydraulic (HASEL) soft actuators for legged robotic systems. His work includes the design, modeling, simulation, and experimental evaluation of robotic legs using MATLAB, ANSYS, SolidWorks, and rapid prototyping technologies. As part of this research, he authored the paper “Designing an Electrohydraulic Actuated Hopping Robot,” which was accepted for presentation at the ASME International Mechanical Engineering Congress and Exposition (IMECE 2025). His research contributes to the advancement of lightweight, compliant robotic systems capable of efficient and adaptive locomotion. In addition to robotics, Preetham conducts interdisciplinary research in Edge Artificial Intelligence for precision agriculture. His work integrates deep learning, computer vision, environmental sensor fusion, and hybrid renewable energy systems to develop autonomous, real-time rainfall prediction and smart irrigation solutions operating on embedded edge devices. His research aims to improve agricultural sustainability by enabling intelligent decision-making without reliance on cloud computing. His broader research interests include intelligent robotic systems, soft robotics, AI-enabled autonomous machines, sustainable energy technologies, digital manufacturing, and intelligent control systems. Through his research, he seeks to bridge mechanical engineering and artificial intelligence to develop innovative engineering solutions that address challenges in robotics, sustainable infrastructure, climate resilience, and next-generation autonomous systems.

Email:

Address:21000 West Ten Mile Road, Southfield, MI, 48075-1058, Southfield, Michigan, United States, 48075




Agenda

10.00 am - Speaker Introduction

10.05 am - Presentation

10.45 am - Q&A Session

11.00 am - Conclusion