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Journal of Field Robotics is a peer-reviewed journal published by Wiley, covering Robotics and Sensor-Based Localization, Robotic Path Planning Algorithms, Underwater Vehicles and Communication Systems.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Journal of Field Robotics 12 (3): 45–58.
Formats any DOI in Journal of Field Robotics style. No sign-up. |
| Publishes research in | Robotics and Sensor-Based Localization Robotic Path Planning Algorithms Underwater Vehicles and Communication Systems Modular Robots and Swarm Intelligence Robotic Locomotion and Control |
| ISSN | 1556-4959 |
| Citation impact (2-yr) | 3.49 |
| h-index | 130 |
| i10-index | 980 |
| Total citations | 76,912 |
| Article processing charge | $4,430 |
| Top institutions publishing here | Carnegie Mellon University |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high‐speed desert driving without manual intervention. The robot's software system relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results…
Abstract The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and…
Abstract Boss is an autonomous vehicle that uses on‐board sensors (global positioning system, lasers, radars, and cameras) to track other vehicles, detect static obstacles, and localize itself relative to a road model. A three‐layer planning system combines mission, behavioral, and motion planning to drive in urban environments. The mission planning layer considers which street to…
Abstract This article presents the architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously. In doing so, the vehicle is able to select its own routes, perceive and interact with other traffic, and execute various urban driving skills including lane changes, U‐turns, parking, and merging into moving traffic. The vehicle successfully finished…
Abstract Distributed as an open‐source library since 2013, real‐time appearance‐based mapping (RTAB‐Map) started as an appearance‐based loop closure detection approach with memory management to deal with large‐scale and long‐term online operation. It then grew to implement simultaneous localization and mapping (SLAM) on various robots and mobile platforms. As each application brings its own set of…
