The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges
Open accessAbstract
Robot navigation is an indispensable component of any mobile service robot. Many path planning algorithms generate a path which has many sharp or angular turns. Such paths are not fit for mobile robot as it has to slow down at these sharp turns. These robots could be carrying delicate, dangerous, or precious items and executing these sharp turns may not be feasible kinematically. On the contrary, smooth trajectories are often desired for robot motion and must be generated while considering the static and dynamic obstacles and other constraints like feasible curvature, robot and lane dimensions, and speed. The aim of this paper is to succinctly summarize and review the path smoothing techniques in robot navigation and discuss the challenges and future trends. Both autonomous mobile robots and autonomous vehicles (outdoor robots or self-driving cars) are discussed. The state-of-the-art algorithms are broadly classified into different categories and each approach is introduced briefly with necessary background, merits, and drawbacks. Finally, the paper discusses the current and future challenges in optimal trajectory generation and smoothing research.
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Cites
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343 · cited 2×
“Other widely used path planning algorithms include potential fields [24] algorithm, and probabilistic road map (PRM) [25] planner.”
BackgroundMethodPast, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Making Bertha Drive—An Autonomous Journey on a Historic Route
Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N., Keller, C. G., et al. (2014) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2014.2306552 · Cited by 854
Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments
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Trajectory planning for Bertha — A local, continuous method
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Analysis and Observations From the First Amazon Picking Challenge
Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J. M., & Wurman, P. R. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2600527 · Cited by 425 · cited 1×
“Today, these robot are used for cleaning, industry automation, and moving stuff in the warehouse [4].”
Method
Cited by
Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment
Tang, G., Tang, C., Claramunt, C., Hu, X., & Zhou, P. (2021) · IEEE Access · doi:10.1109/access.2021.3070054 · Cited by 474
Rule-Based Reinforcement Learning for Efficient Robot Navigation With Space Reduction
Zhu, Y., Wang, Z., Chen, C., & Dong, D. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2021.3072675 · Cited by 57
Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios
Vachmanus, S., Ravankar, A. A., Emaru, T., & Kobayashi, Y. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3077029 · Cited by 53
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Metadata
- Authors
- Ravankar, A., Ravankar, A. A., Kobayashi, Y., Hoshino, Y., & Peng, C.-C.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-09-19
- Volume
- 18
- Issue
- 9
- Pages
- 3170
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2890807499
- Cited by
- 314
- References
- 7
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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