Supplementary Materials (Anonymous)

This website includes the code and supplementary materials related to the paper submission.

Contents

• Installation
• Code and Key Results
  • Code of Use Case A
  • Code of Use Case B

Installation

This section provides instructions on how to install RoboSandbox and verify the installation.

You can install RoboSandbox using pip by running the following command:

pip install robosandbox

It is recommended to use a virtual environment. To set up a conda and uv environment with RoboSandbox installed, you can use the following commands:

conda create -n rsb_env python=3.10
conda activate rsb_env
pip install uv
uv pip install robosandbox

To verify the installation, we can launch the app from RoboSandbox:

python -c "import robosandbox as rsb; rsb.visualization.RobotArmDesignAppStandalone().run_app()"

Code and Key Results

This section provides links to the code and showcases the results of the paper.

Use Case A

• Code of Use Case A

• Part of Plotted Robots

Use Case B

• Optimization of P1

Code-enriched literature of optimization problem P1:

\[\min_{\mathbf{a}, \mathbf{d}}\quad f_{1}(\mathbf{a}, \mathbf{d}; \boldsymbol{\alpha^0}) = \delta\]

• Optimization of P2

Code-enriched literature of optimization problem P2:

\[\min_{\mathbf{a}, \mathbf{d}}\quad f_{2}(\mathbf{a}, \mathbf{d}; \boldsymbol{\alpha^0}) = \delta - G_{n}\]

• P1 RRT

Example of robot P1 reaching a target using RRT.

• P2 RRT

Example of robot P2 reaching a target using RRT.

• UR5 RRT

Example of UR5 reaching a target using RRT.

• Compare Gₙ and δ Python script to generate Table IV in the paper.

• Compare RRT Performance Python script to generate Table V in the paper.