Secure learning-based coordinated UAV–UGV framework design for medical waste transportation - Sharma, Desh Deepak; Lin, Jeremy (Frontiers in Remote Sensing, 2024)
A cost-effective solution with less human involvement has to be developed for Medical Waste (MW ) transportation. A learning-based coordinated UAV-UGV (CUU) framework is suggested for medical waste transportation. The transfer learning algorithm is proposed for medical waste transportation with a coordinated UAV-UGV framework. A transfer learning algorithm is implemented for the prediction of collision-free optimal path planning. In the framework, mobile ground robots are used for collecting medical waste from waste disposal centers through the pick-and-place technique. The networked drones lift the collected medical waste and fly through a predefined optimal predicted trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. For each successful or unsuccessful action by the framework, a deep reinforcement learning mechanism has been incorporated to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of medical waste packets as the payload capacity of a CUU framework, cost of steering of UAV and UGV, and time of transportation of MW. The effectiveness of CUU framework for MW transportation has been tested by obtaining the results using MATLAB.The MW transportation data has been encrypted using the encryption key for security and authenticity.