Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection

This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an mTSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms.

Bio - Shashwata Mandal is from India and completed his undergrad in Computer Science and Engineering from West Bengal University of Technology, India in 2016. He worked for a year after that at NRIFintech before coming to the US in 2017 where he completed the graduate program in Computer Science from Pennsylvania State University in 2019. Shashwata moved to Iowa State in 2019 for his PhD and has been working with Dr Sourabh Bhattacharya since Fall 2020. He is currently working on robotics, pursuit evasion, computational geometry and path planning.

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