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Batch Informed Trees

Project Report | Project Slides | Turtlebot Demo - (Wall Gap Scenario)

BIT* Algorithm - Introduction

Path Planning in Robotics has always relied on simple approximations to identify solutions. This is due to the difficulty to find one due to the high dimensional nature of the problem.

Generally, we can divide the approximations into 2 types: Search-based and Sampling-based.

Heuristics are used by search-based planners like A* to effectively search across graphs, but their efficiency is limited by the resolution of the selected approximation. To approximate the problem, sample-based planners such as RRT* employ random sampling. Here, the resolution can be increased until we find a suitable solution. These random samples approximate the region in all directions at the same time, making the search ineffective.

A recent approach called Batch Informed Trees (BIT*) combines the strengths of both Search-based sampling-based planners. Heuristics and Sampling is used by BIT* to alternate between searching and approximating. In this work, we have used the pseudo-code from the paper and coded the algorithm from scratch, and tested its performance in R2 space for different motion planning scenarios.

Installation

git clone https://github.com/marleyshan21/Batch-informed-trees.git
cd BIT-star
pip install -r requirements.txt

Usage

Python

To use our python implementation of BIT-star, we provide a run.py file with options to specify all the arguments passed to the algorithm. A full list of options can be seen by running

cd python
python3 run.py --help

Example Usage

To run BIT-star on a default map and only obtain the final path once the stop time has been reached, simply run:

python3 run.py --map_name Default --start 0 0 --goal 99 99 --seed 1 --stop_time 20

To run BIT-star on a more complex map (Maze) and obtain visualizations once the stop time ahs been reached, run:

python3 run.py --map_name Maze --start 0 0 --goal 99 99 --seed 1 --stop_time 60 --vis --fast

We also provide options to change the rbit (maximum edge length), and number of samples when running. You can also visualize every edge addition and removal by disabling the --fast option.

Examples

  • Empty Scenario

Empty Scenario

  • Enclosure Scenario

Enclosure Scenario

  • Maze Scenario

Maze Scenario

  • Wall Scenario

Wall Scenario

Collaboration

Done in collaboration with - Saharajit Anantharamakrishnan, Drake Moore, Thejaswini Gopiparthi, and Francis Jacob Kalliath