The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Sampling-Based Robot Motion Planning: A Review
Open accessAbstract
Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efficient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difficult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecified. An emphasis is placed on contemporary research directions in this field. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems.
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Cited by
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“A detailed overview of Sampling Based Planning (SBP) methods can be found in [262].”
MethodA Review of Motion Planning Techniques for Automated Vehicles
Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498841 · Cited by 1,670
Heuristic approaches in robot path planning: A survey
Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.08.001 · Cited by 641
Human-Machine Interface for Remote Training of Robot Tasks
Spranger, J., Buzatoiu, R., Polydoros, A., Nalpantidis, L., & Boukas, E. (2018) · 2018 IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2018.8577081 · Cited by 10
Metadata
- Authors
- Elbanhawi, M., & Simic, M.
- Venue
- IEEE Access
- Published
- 2014
- Volume
- 2
- Pages
- 56-77
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2055201760
- Cited by
- 786
- References
- 8
- FWCI
- 23.15
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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