# Paramater Optimization for Manipulator Motion Planning using a Novel Benchmark Set

## TL;DR

This paper investigates parameter optimization for manipulator motion planning across diverse environments, using a novel benchmark set to analyze and recommend optimal settings, improving planning efficiency and robustness.

## Contribution

It introduces a comprehensive analysis of parameter tuning for manipulator motion planning using a new benchmark, providing practical recommendations for various scenarios.

## Key findings

- Optimal parameters vary with environment complexity.
- Benchmark results show improved planning times with tuned parameters.
- Insights link problem characteristics to optimal parameter choices.

## Abstract

Sampling-based motion planning algorithms have been continuously developed for more than two decades. Apart from mobile robots, they are also widely used in manipulator motion planning. Hence, these methods play a key role in collaborative and shared workspaces. Despite numerous improvements, their performance can highly vary depending on the chosen parameter setting. The optimal parameters depend on numerous factors such as the start state, the goal state and the complexity of the environment. Practitioners usually choose these values using their experience and tedious trial and error experiments. To address this problem, recent works combine hyperparameter optimization methods with motion planning. They show that tuning the planner's parameters can lead to shorter planning times and lower costs. It is not clear, however, how well such approaches generalize to a diverse set of planning problems that include narrow passages as well as barely cluttered environments. In this work, we analyze optimized planner settings for a large set of diverse planning problems. We then provide insights into the connection between the characteristics of the planning problem and the optimal parameters. As a result, we provide a list of recommended parameters for various use-cases. Our experiments are based on a novel motion planning benchmark for manipulators which we provide at https://mytuc.org/rybj.