Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments

TL;DR

Exploration-RRT is a multi-objective path planning algorithm designed for efficient exploration of unknown 3D environments using UAVs, balancing information gain, travel distance, and actuation costs.

Contribution

The paper introduces a novel multi-goal RRT-based exploration algorithm tailored for 3D unknown environments, integrating multiple optimization criteria.

Findings

Demonstrated efficiency in simulated UAV exploration tasks.

Effectively balances exploration and path costs.

Applicable to real-world UAV exploration scenarios.

Abstract

This article establishes the Exploration-RRT algorithm: A novel general-purpose combined exploration and pathplanning algorithm, based on a multi-goal Rapidly-Exploring Random Trees (RRT) framework. Exploration-RRT (ERRT) has been specifically designed for utilization in 3D exploration missions, with partially or completely unknown and unstructured environments. The novel proposed ERRT is based on a multi-objective optimization framework and it is able to take under consideration the potential information gain, the distance travelled, and the actuation costs, along trajectories to pseudo-random goals, generated from considering the on-board sensor model and the non-linear model of the utilized platform. In this article, the algorithmic pipeline of the ERRT will be established and the overall applicability and efficiency of the proposed scheme will be presented on an application with an Unmanned Aerial Vehicle (UAV) model, equipped with a 3D lidar, in a simulated operating environment, with the goal of exploring a completely unknown area as efficiently and quickly as possible