Abstract
This paper presents an extension to the Hybrid Information and Plan Consensus Algorithm (HIPC) that accounts for imperfect situational awareness (SA). This algorithm uses implicit coordination to plan for a subset of the team on-board each agent, then uses plan consensus to satisfy assignment constraints. By combining the ideas of implicit coordination and local plan consensus, the algorithm empirically reduces the convergence time for distributed task allocation problems. The contribution of this work is that it extends previous results to account for the likely possibility of imperfect situational awareness across the team. This is accomplished by tracking when predictions are incorrect and removing offending predictions if they are hindering algorithmic convergence. Empirical results are provided to demonstrate that this new approach allows the use of inconsistent situational awareness to improve convergence speed.
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Acknowledgments
This work was sponsored (in part) by the AFOSR and USAF under grant (FA9550-11-1-0134). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Office of Scientific Research or the U.S. Government.
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Johnson, L., Choi, HL., How, J.P. (2016). The Hybrid Information and Plan Consensus Algorithm with Imperfect Situational Awareness. In: Chong, NY., Cho, YJ. (eds) Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 112 . Springer, Tokyo. https://doi.org/10.1007/978-4-431-55879-8_16
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DOI: https://doi.org/10.1007/978-4-431-55879-8_16
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