Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments
Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV-based sensing approaches overlook environmental disturbances like wind that drastically impact drone velocity and energy efficiency. Consequently, directly applying existing methods to this joint delivery and sensing paradigm in dynamic environments faces two severe
Researchers propose a Two TimeScale Reinforcement Learning framework for delivery drone-based participatory sensing in dynamic environments. The framework, called TSRL, separates decision-making into macro and micro levels to tackle scalability and decision heterogeneity. TSRL achieves average system profit improvements of 20.1% in Hangzhou and 46.6% in Shanghai.
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