Papers
8
Total Citations
80
H-Index
6
About
Sandip Aine is a leading researcher in artificial intelligence and robotics, specializing in heuristic search, path planning, and autonomous decision-making. His most significant contribution is the development of **Multi-Heuristic A* (MHA*)** , a groundbreaking framework that simultaneously leverages multiple, arbitrarily inadmissible heuristics alongside a single consistent one to find bounded suboptimal solutions. This innovation, detailed in his highly cited 2014 paper (13 citations), simplifies heuristic design and has become foundational in the field. Aine further advanced search efficiency with **Improved Multi-Heuristic A*** (22 citations), which handles uncalibrated heuristics, and **Anytime Column Search** (13 citations), enabling anytime planning. His work on parallelization is equally impactful, with **ePA*SE** and **MPLP** (9 citations each) introducing edge-based and massively parallelized lazy planning to harness modern multi-core processors for slow evaluations. In robotics, Aine’s **virtual bug planning technique** (9 citations) offers rapid 2D path planning, while his integration of planning and control addresses nonholonomic robots under environmental disturbances. His recent work on learning optimal decision-making for industrial truck unloading robots (2021) demonstrates his commitment to real-world applications. With over 80 total citations across his top papers, Aine’s research consistently pushes the boundaries of efficient, scalable, and practical search algorithms.
Research Focus
Key Achievements
Top Papers
- 1Improved Multi-Heuristic A* for Searching with Uncalibrated Heuristics22 citations · 2021
- 2Multi-Heuristic A*13 citations · 2014
- 3Anytime Column Search13 citations · 2012
- 4MPLP: Massively Parallelized Lazy Planning9 citations · 2022
- 5ePA*SE: Edge-Based Parallel A* for Slow Evaluations9 citations · 2022
- 6A virtual bug planning technique for 2D robot path planning9 citations · 2018
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