Version 1
: Received: 8 October 2023 / Approved: 11 October 2023 / Online: 13 October 2023 (03:52:26 CEST)
How to cite:
Francis, S.; Dam, T.; Anavatti, S.; Garratt, M.A. Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios. Preprints2023, 2023100738. https://doi.org/10.20944/preprints202310.0738.v1
Francis, S.; Dam, T.; Anavatti, S.; Garratt, M.A. Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios. Preprints 2023, 2023100738. https://doi.org/10.20944/preprints202310.0738.v1
Francis, S.; Dam, T.; Anavatti, S.; Garratt, M.A. Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios. Preprints2023, 2023100738. https://doi.org/10.20944/preprints202310.0738.v1
APA Style
Francis, S., Dam, T., Anavatti, S., & Garratt, M.A. (2023). Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios. Preprints. https://doi.org/10.20944/preprints202310.0738.v1
Chicago/Turabian Style
Francis, S., Sreenatha Anavatti and Matthew A Garratt. 2023 "Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios" Preprints. https://doi.org/10.20944/preprints202310.0738.v1
Abstract
Beginning with navigation system design, this paper presents a comprehensive strategy for enhancing the search and rescue capabilities of agile mobile robots. Towards this, the autonomous ground vehicle (AGV) utilizes surface classification to determine and prioritize the terrain it is traversing. Our developed system design incorporates real-time terrain data with task objectives at a high level, ensuring that the robot can effectively navigate complex and ever-changing environments. This design, in conjunction with the introduction of a novel lightweight surface classification model, forms the basis of our adaptive terrain perception and decision-making systems, enabling robots such as Jackal to adapt rapidly and make the decisions necessary to complete the task. Subsequently, we exhaustively validated these systems through a series of extensive experiments in a variety of terrains, including normal and mixed terrains, demonstrating their robustness and efficacy in real-life situations.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
