Shashwata Mandal

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About

Shashwata Mandal is a robotics researcher whose work focuses on the intersection of multi-agent systems, aerial robotics, and environmental sensing. His primary research areas include task allocation, path planning, and autonomous coordination for unmanned aerial vehicle (UAV) teams, with a particular emphasis on applications in precision agriculture and ecological monitoring. Mandal’s most cited work, “Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection” (2021), addresses the challenging problem of joint task allocation and path planning for multi-UAV platforms under coverage and sensing constraints. This paper introduces an efficient, implementable solution to a novel variant of the vehicle routing problem, enabling teams of drones to collaboratively collect biometric and phenotypic data over large areas. By integrating sensing requirements directly into the planning framework, Mandal’s approach bridges the gap between theoretical optimization and real-world deployment, offering practical tools for large-scale environmental data collection. His contributions are particularly valuable for researchers and practitioners working on autonomous systems for agriculture, forestry, and wildlife monitoring, where efficient aerial data acquisition is critical. With 3 citations to date, this work represents a foundational step toward scalable, coordinated robotic sensing.