A reference implementation of EIT* is available in pull request #844 for the Open Motion Planning Library (OMPL) and will hopefully be part of future binary releases. You can find details on how to download and install OMPL on their website.
Effort Informed Trees (EIT*) is an almost-surely asymptotically optimal path planning algorithm. It simultaneously calculates and exploits multiple heuristics with an asymmetric bidirectional search in which both searches continuously inform each other. One of these heuristics guides EIT* towards fast-to-find solutions by considering information about the computational effort required to validate a path. EIT* outperforms other almost-surely asymptotically optimal algorithms (e.g., RRT* and AIT*) on problems with expensive edge evaluations by finding initial solutions as fast as RRT-Connect and converging to the optimum in an anytime manner.
- Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning
- The International Journal of Robotics Research (IJRR)
- D.Phil. Thesis
- University of Oxford