Papers
19
Total Citations
567
H-Index
13
About
Bavani Kannan is a researcher whose work spans robotics, multi-robot systems, and autonomous platforms, with particular expertise in indoor localization, heterogeneous robot team coordination, and fault-tolerant systems. Her most cited contribution, "Robust Indoor Localization on a Commercial Smart Phone" (2012, 99 citations), introduced a practical dead reckoning methodology for low-cost indoor positioning with applications in emergency response and assistive technology. Complementing this, her foundational work on heterogeneous multi-robot teams — including tightly-coupled navigation assistance (79 citations) and mobile sensor network deployment through robot herding (54 citations) — established key frameworks for enabling capable robots to guide sensor-limited counterparts in complex environments. Kannan has also made significant strides in autonomous marine platforms, multi-robot planning with the xBots framework (44 citations), and autonomous recharging coordination for distributed robot missions. Her research extends to high-stakes applications such as planetary cave mapping and flood disaster mitigation. Across fault diagnosis work, she developed adaptive causal models and performance metrics that meaningfully advanced the reliability of intelligent robot teams. With over 460 total citations, Kannan's body of work reflects a sustained commitment to building robust, practical autonomous systems capable of operating in real-world, resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1Robust Indoor Localization on a Commercial Smart Phone99 citations · 2012
- 2Tightly-coupled navigation assistance in heterogeneous multi-robot teams79 citations · 2005
- 3Development of a Low Cost Multi-Robot Autonomous Marine Surface Platform55 citations · 2013
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- 6The Autonomous Recharging Problem: Formulation and a market-based solution30 citations · 2013
- 7Mapping planetary caves with an autonomous, heterogeneous robot team28 citations · 2013
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- 9Adaptive Causal Models for Fault Diagnosis and Recovery in Multi-Robot Teams28 citations · 2006
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