# Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics

## TL;DR

This paper introduces GIT*, a path planning method that enhances heuristic functions by integrating environmental data and reinforced genetic programming, leading to improved efficiency and solution quality in complex spaces.

## Contribution

It presents GIT*, a novel path planning approach that combines environmental data integration with reinforced genetic programming for superior heuristic guidance.

## Key findings

- GIT* outperforms existing planners in high-dimensional problems.
- Incorporating environmental data improves heuristic accuracy.
- Reinforced genetic programming enhances computational efficiency.

## Abstract

Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data, such as repulsive forces from obstacles and the dynamic importance of vertices, to refine heuristic functions for better guidance. Furthermore, we integrated reinforced genetic programming (RGP), which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT*. RGP leverages a multitude of data types, thereby improving computational efficiency and solution quality within a set timeframe. Comparative analyses demonstrate that GIT* surpasses existing single-query, sampling-based planners in problems ranging from R^4 to R^16 and was tested on a real-world mobile manipulation task. A video showcasing our experimental results is available at https://youtu.be/URjXbc_BiYg