Visual Based Autonomous Navigation for Legged Robots

Segarra, David; Caballeros, Jessica; Aguilar, Wilbert G.

doi:10.1007/978-3-030-02698-1_3

Cited by 3 publications

(1 citation statement)

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“…When navigating in a complex environment, a mobile robot must estimate its location through onboard sensors, such as GPS, IMU, laser rangefinder, and camera. 16,17 Depending on the degree of environmental awareness, localizing the robot can be achieved using geometric and/or visual features. Recently, simultaneous localization and mapping (SLAM) becomes a popular topic in the mobile robotics community.…”

Section: Introductionmentioning

confidence: 99%

Development of a vision-based target exploration system for snake-like robots in structured environments

Waldum

Grindvik

et al. 2020

International Journal of Advanced Robotic Systems

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Applying snake-like robots to environmental exploration has been a hot topic for years. How to achieve free navigation for target search in a complex environment in a safe and efficient manner is one of the main tasks that researchers in the field of robotics currently face. This article presents a target exploration system that takes advantages of visual sensing to navigate the snake-like robot in structured environments. Two cameras are utilized in the system. The first one is mounted on the head of the snake-like robot for target recognition and the other is an overhead camera which is responsible for locating the robot and identifying surrounding obstacles. All dead ends in the environment can thus be recognized using a template-based method. A search strategy for traversal of the dead ends is employed for generating exploration paths. Several gaits are developed for the snake-like robot. By switching between these gaits, the snake-like robot is able to follow the paths to search for the target. Two experiments are conducted in a maze environment. The experimental results validate the effectiveness of the proposed system for snake-like robots exploring in structured environments.

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Section: Introductionmentioning

confidence: 99%