# Deep Fuzzy Optimization for Batch-Size and Nearest Neighbors in Optimal Robot Motion Planning

## TL;DR

This paper introduces LIT*, a deep fuzzy learning-based motion planner that adaptively optimizes batch size and nearest neighbor parameters based on environment obstacle density, improving efficiency and solution quality in high-dimensional robotic planning.

## Contribution

The paper presents a novel deep fuzzy neural network approach for adaptive parameter tuning in sampling-based motion planning, enhancing performance in complex environments.

## Key findings

- LIT* achieves faster convergence than existing planners.
- LIT* produces lower-cost paths in high-dimensional spaces.
- Validated on dual-arm robot manipulation tasks.

## Abstract

Efficient motion planning algorithms are essential in robotics. Optimizing essential parameters, such as batch size and nearest neighbor selection in sampling-based methods, can enhance performance in the planning process. However, existing approaches often lack environmental adaptability. Inspired by the method of the deep fuzzy neural networks, this work introduces Learning-based Informed Trees (LIT*), a sampling-based deep fuzzy learning-based planner that dynamically adjusts batch size and nearest neighbor parameters to obstacle distributions in the configuration spaces. By encoding both global and local ratios via valid and invalid states, LIT* differentiates between obstacle-sparse and obstacle-dense regions, leading to lower-cost paths and reduced computation time. Experimental results in high-dimensional spaces demonstrate that LIT* achieves faster convergence and improved solution quality. It outperforms state-of-the-art single-query, sampling-based planners in environments ranging from R^8 to R^14 and is successfully validated on a dual-arm robot manipulation task. A video showcasing our experimental results is available at: https://youtu.be/NrNs9zebWWk