Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions
Author(s)Ahmed, Nisar; Luders, Brandon Douglas; Sample, Eric; Shah, Danelle; Campbell, Mark; How, Jonathan P.; Ponda, Sameera S.; ... Show more Show less
MetadataShow full item record
This paper introduces a novel planning and estimation framework for maximizing infor- mation collection in missions involving cooperative teams of multiple autonomous vehicles and human agents, such as those used for multi-target search and tracking. The main contribution of this work is the scalable uni cation of e ective algorithms for distributed high-level task planning, decentralized information-based trajectory planning, and hybrid Bayesian information fusion through a common Gaussian mixture uncertainty representa- tion, which can accommodate multiple mission objectives and constraints as well as het- erogeneous human/robot information sources. The proposed framework is validated with promising results on real hardware through a set of experiments involving a human-robot team performing a multi-target search mission.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
Proceedings of the AIAA Guidance, Navigation, and Control Conference
American Institute of Aeromautics and Astronautics
Sameera Ponda, Nisar Ahmed, Brandon Luders, Eric Sample, Tauhira Hoossainy, Danelle Shah, Mark Campbell, and Jonathan How. "Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions" AIAA Guidance, Navigation, and Control Conference. August.
Author's final manuscript