TY - JOUR
T1 - Magnetic-field-Inspired Navigation for Robots in Complex and Unknown Environments
AU - Ataka, Ahmad
AU - Lam, Hak-Keung
AU - Althoefer, Kaspar
N1 - Funding Information:
This work was supported in-part by King?s College London, the STIFF-FLOP project grant from the European Communities Seventh Framework Programme under grant agreement 287 728, the EPSRC in the framework of the NCNR (National Centre for Nuclear Robotics) project (EP/R02572X/1), q-bot led project WormBot (2308/104 059), and the Indonesia Endowment Fund for Education, Ministry of Finance Republic of Indonesia.
Publisher Copyright:
Copyright © 2022 Ataka, Lam and Althoefer.
PY - 2022/2/18
Y1 - 2022/2/18
N2 - Over the course of the past decade we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, 1) it guarantees obstacle avoidance for both convex and non-convex obstacles, 2) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, 3) no prior knowledge of the environment, such as the position and geometry of the obstacles, is needed, 4) it only requires temporally and spatially local environmental sensor information, and 5) it can be implemented on a wide range of robotic platforms in both 2D and 3D environments. The proposed navigation algorithm is validated in simulation scenarios as well as through experimentation. The results demonstrate that robotic platforms, ranging from planar point-like robots to robot arm structures such as the Baxter robot, can successfully navigate toward desired targets within an obstacle-laden environment.
AB - Over the course of the past decade we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, 1) it guarantees obstacle avoidance for both convex and non-convex obstacles, 2) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, 3) no prior knowledge of the environment, such as the position and geometry of the obstacles, is needed, 4) it only requires temporally and spatially local environmental sensor information, and 5) it can be implemented on a wide range of robotic platforms in both 2D and 3D environments. The proposed navigation algorithm is validated in simulation scenarios as well as through experimentation. The results demonstrate that robotic platforms, ranging from planar point-like robots to robot arm structures such as the Baxter robot, can successfully navigate toward desired targets within an obstacle-laden environment.
UR - http://www.scopus.com/inward/record.url?scp=85125878597&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/frobt.2022.834177
DO - https://doi.org/10.3389/frobt.2022.834177
M3 - Article
SN - 2296-9144
VL - 9
JO - Front. Robot. AI
JF - Front. Robot. AI
M1 - 834177
ER -