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Creators/Authors contains: "Ramshanker, Sneha"

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  1. ; ; (, 17th International Symposium on Distributed Autonomous Robotic Systems (DARS'24) ; Springer Advanced Proceedings in Robotics (SPAR))
    Robot swarms offer significant potential for inspecting di- verse infrastructure, ranging from bridges to space stations. However, effective inspection requires accurate robot localization, which demands substantial computational resources and limits productivity. Inspired by biological systems, we introduce a novel cooperative localization mech- anism that minimizes collective computation expenditure through self- organized sacrifice. Here, a few agents bear the computational burden of localization; through local interactions, they improve the inspection pro- ductivity of the swarm. Our approach adaptively maximizes inspection productivity for unconstrained trajectories in dynamic interaction and environmental settings. We demonstrate the optimality and robustness using mean-field analytical models, multi-agent simulations, and hard- ware experiments with metal climbing robots inspecting a 3D cylinder. 
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    Accepted Manuscript Full Text Available