Papers
11
Total Citations
68
H-Index
5
About
Chinwe Ekenna is a leading researcher in robotics motion planning, whose work bridges algorithmic foundations and real-world autonomous systems. Her primary research areas include probabilistic roadmap methods (PRMs), topology-driven planning, and multi-robot surveillance. Ekenna’s most influential contribution is her 2013 paper on adaptive neighbor connection for PRMs, which has garnered 19 citations and introduced a natural framework for heterogeneous environments and parallel computing. She has also pioneered novel approaches to approximating configuration space topology using Vietoris-Rips complexes (2019, 8 citations) and applied discrete Morse theory to optimize safe motion planning paths (2022, 7 citations). Her work on air-to-ground surveillance using predictive pursuit (2019, 7 citations) demonstrates a practical impact, improving tracking accuracy through probabilistic Markov decision processes. Ekenna has further advanced the field by investigating heterogeneous planning spaces (2018, 6 citations) and developing neural network-based collision prediction (2020, 5 citations). Notably, she has extended motion planning algorithms to protein folding (2012, 5 citations), showcasing interdisciplinary reach. Her recent work on fault-tolerant motion planning (2023) addresses critical system failures, reflecting her commitment to robust, deployable robotics. With a career spanning foundational theory to applied systems, Ekenna continues to shape how robots navigate complex, dynamic worlds.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Air-to-Ground Surveillance Using Predictive Pursuit7 citations · 2019
- 4
- 5Investigating heterogeneous planning spaces6 citations · 2018
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- 7
- 8Predicting Sample Collision with Neural Networks5 citations · 2020
- 9
- 10Uncertainty Measured Markov Decision Process in Dynamic Environments2 citations · 2020