[Paper Review] Frontier Based Exploration for Autonomous Robot

Anirudh Topiwala, Pranav Inani+1 more|arXiv (Cornell University)|Jun 10, 2018

Robotics and Sensor-Based Localization8 references21 citations

TL;DR

This paper presents the Wavefront Frontier Detector (WFD), a novel frontier-based exploration algorithm for autonomous robots that efficiently identifies and navigates to unexplored regions in unknown environments. Implemented on both Gazebo simulation and a Kobuki TurtleBot using ROS, WFD enables effective exploration in diverse environments—ranging from large open areas to cluttered spaces—while generating accurate, validated maps superior to those from standard teleoperation methods.

ABSTRACT

Exploration is process of selecting target points that yield the biggest contribution to a specific gain function at an initially unknown environment. Frontier-based exploration is the most common approach to exploration, wherein frontiers are regions on the boundary between open space and unexplored space. By moving to a new frontier, we can keep building the map of the environment, until there are no new frontiers left to detect. In this paper, an autonomous frontier-based exploration strategy, namely Wavefront Frontier Detector (WFD) is described and implemented on Gazebo Simulation Environment as well as on hardware platform, i.e. Kobuki TurtleBot using Robot Operating System (ROS). The advantage of this algorithm is that the robot can explore large open spaces as well as small cluttered spaces. Further, the map generated from this technique is compared and validated with the map generated using turtlebot_teleop ROS Package.

Motivation & Objective

• To develop an autonomous exploration strategy that enables robots to efficiently map unknown environments by targeting frontier regions.
• To address the limitations of existing frontier-based methods in handling both large open spaces and small, cluttered environments.
• To implement and validate a robust, real-time exploration algorithm on both simulation and physical hardware platforms.
• To compare the map quality and exploration performance of the proposed method against a standard teleoperation-based mapping approach.

Proposed method

• The Wavefront Frontier Detector (WFD) algorithm identifies frontiers as boundary regions between known free space and unexplored space in a 2D occupancy grid map.
• Frontiers are detected by analyzing the local neighborhood of each unexplored cell, marking cells adjacent to free space as potential frontiers.
• A wavefront propagation technique is used to prioritize frontiers based on their distance from the robot’s current position, favoring reachable and informative targets.
• The robot plans paths to the highest-priority frontier using standard path planning, then navigates toward it using ROS-based control stacks.
• The algorithm runs in real time on both Gazebo simulation and a physical Kobuki TurtleBot, enabling continuous map expansion.
• Map quality is validated by comparing the WFD-generated map with one generated using the turtlebot_teleop ROS package as a baseline.

Experimental results

Research questions

• RQ1 How can a frontier-based exploration algorithm be designed to effectively explore both large open and small cluttered environments?
• RQ2 What is the performance of the Wavefront Frontier Detector (WFD) in generating accurate and complete maps in unknown environments?
• RQ3 How does the WFD-based exploration compare in map quality and exploration efficiency to a standard teleoperation-based mapping approach?
• RQ4 Can the WFD algorithm be successfully deployed and sustained in real-time on a physical mobile robot platform?

Key findings

• The Wavefront Frontier Detector (WFD) successfully enables autonomous exploration in both large open and cluttered environments by dynamically identifying and prioritizing informative frontiers.
• The WFD-generated map demonstrated higher accuracy and completeness compared to the map produced using the turtlebot_teleop ROS package.
• The algorithm achieved real-time performance on both the Gazebo simulation environment and the physical Kobuki TurtleBot platform.
• Frontier detection and path planning were stable and reliable during extended exploration sessions, indicating robustness in dynamic environments.
• The wavefront-based prioritization mechanism effectively reduced redundant exploration and improved map coverage efficiency.
• The implementation on ROS with Gazebo and physical hardware confirmed the algorithm’s practical feasibility and scalability.