Lehigh University

Aerialhitches: Forming and Controlling Hitches for Fully Autonomous Transportation Using Aerial Robots With Cables -this Project Will Improve Aerial Robots So They Can Grasp, Manipulate and Transport Objects Better and Without Human Intervention. Conventional Aerial Robots Rely on Grippers (i.e., End Effectors) and Robotic Arms for Manipulating and Transporting Objects. These Parts Not Only Add Significant Weight, But Also Reduce Flight Duration and Complicate the Control of the Robots. This Project Introduces an Innovative Alternative, Wherein the Robots Utilize Lightweight Cables to Form 'hitches' for Performing Manipulation Tasks, Bypassing the Need for Weighty External Components. the Benefit of Cables Is That They Are Lightweight and Flexible, Allowing for Versatile Manipulation Techniques That Traditional Rigid Grippers Cannot Provide. Cables Can Also Conform to Objects of Various Shapes and Sizes, Increasing the Range of Tasks an Aerial Robot Can Perform. Additionally, Using Cables Dramatically Reduces the Overall Weight of the System, Resulting in Increased Flight Duration. This Groundbreaking Concept Has the Potential to Greatly Improve Productivity in Fields Such as Construction, Logistics, and Disaster Management, Where High-frequency, Repetitive Transportation Tasks Are Common. in These Sectors, Using a Cable-based Aerial Robotic System Could Reduce Safety Risks Associated With Manual Transportation and Work in Elevated or Hazardous Areas. on a Technical Front, the Project Entails Abstracting Common Cable Configurations Into a Finite-dimensional Space to Manage the Physical Dynamics of the Cables. a Reinforcement Learning Algorithm Will Be Formulated Based on This Abstraction, Expediting the Convergence to Optimal Actions by Utilizing an Ideal Dynamics Model. Topology-based Planning Algorithms Will Be Developed to Form the Hitches, Accounting for the Specific Movements Required. in Lieu of Creating Plans From Scratch, a Library of Hitches Will Be Established to Streamline the Transportation Process, Which Can Be Adjusted According to Transportation Requirements. Through This Project, the Potential Contributions Include Advancements in Reinforcement Learning for Controlling Interlaced Cables, Modeling Cables Using Topological Frameworks in Aerial Robotics, and Progressing Our Understanding of the Principles Governing Aerial Robot Behavior and Manipulation. This Award Reflects NSF'S Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.- Subawards Are Not Planned for This Award.