Autonomous Navigation in Unstructured Environments: Exploring techniques for enabling robots to autonomously navigate through unstructured environments using sensors and perception
Published 30-06-2021
Keywords
- Autonomous Navigation,
- Unstructured Environments,
- Sensor Fusion
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Autonomous navigation in unstructured environments is a critical capability for robots deployed in various fields such as search and rescue, exploration, and agriculture. This paper reviews the state-of-the-art techniques and technologies used to enable robots to navigate autonomously in complex and dynamic environments. We discuss key challenges such as perception, mapping, path planning, and control, and explore how sensor fusion and machine learning techniques can enhance navigation performance. Additionally, we highlight recent advancements and promising directions for future research in this field.
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References
- Leeladhar Gudala, et al. “Leveraging Artificial Intelligence for Enhanced Threat Detection, Response, and Anomaly Identification in Resource-Constrained IoT Networks”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019, pp. 23-54, https://dlabi.org/index.php/journal/article/view/4.
- Tatineni, Sumanth. "Climate Change Modeling and Analysis: Leveraging Big Data for Environmental Sustainability." International Journal of Computer Engineering and Technology 11.1 (2020).
- Vemori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.