BSL: Navigation Method Considering Blind Spots Based on ROS Navigation Stack and Blind Spots Layer for Mobile Robot

TL;DR

This paper introduces a navigation method for wheeled robots that incorporates blind spot detection using environmental data from sensors, enhancing safety by preventing collisions in complex environments.

Contribution

It presents a novel blind spots layer (BSL) integrated with ROS navigation stack, utilizing RGB-D and laser data for improved local path planning.

Findings

Effective blind spot estimation from sensor data

Enhanced local path planning reduces collision risk

Validated through simulations and real-world experiments

Abstract

This paper proposes a navigation method considering blind spots based on the robot operating system (ROS) navigation stack and blind spots layer (BSL) for a wheeled mobile robot. In this paper, environmental information is recognized using a laser range finder (LRF) and RGB-D cameras. Blind spots occur when corners or obstacles are present in the environment, and may lead to collisions if a human or object moves toward the robot from these blind spots. To prevent such collisions, this paper proposes a navigation method considering blind spots based on the local cost map layer of the BSL for the wheeled mobile robot. Blind spots are estimated by utilizing environmental data collected through RGB-D cameras. The navigation method that takes these blind spots into account is achieved through the implementation of the BSL and a local path planning method that employs an enhanced cost function of dynamic window approach. The effectiveness of the proposed method was further demonstrated through simulations and experiments.