# Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler

## TL;DR

This paper introduces MIT*, an advanced sampling-based path planner that uses adaptive informed sets and sampling strategies to significantly improve convergence speed and efficiency in high-dimensional robotic path planning tasks.

## Contribution

MIT* is a novel planner that constructs estimated informed sets from prior solution costs and employs an adaptive sampler, enhancing efficiency over existing methods.

## Key findings

- MIT* outperforms existing planners in R^4 to R^16 problems.
- It achieves faster initial convergence and higher success rates.
- Demonstrated effectiveness in real-world robot manipulation tasks.

## Abstract

Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-sample and explore the entire configuration space, which is time-consuming and computationally expensive. This article introduces Multi-Informed Trees (MIT*), a novel planner that constructs estimated informed sets based on prior admissible solution costs before finding the initial solution, thereby accelerating the initial convergence rate. Moreover, MIT* employs an adaptive sampler that dynamically adjusts the sampling strategy based on the exploration process. Furthermore, MIT* utilizes length-related adaptive sparse collision checks to guide lazy reverse search. These features enhance path cost efficiency and computation times while ensuring high success rates in confined scenarios. Through a series of simulations and real-world experiments, it is confirmed that MIT* outperforms existing single-query, sampling-based planners for problems in R^4 to R^16 and has been successfully applied to real-world robot manipulation tasks. A video showcasing our experimental results is available at: https://youtu.be/30RsBIdexTU