The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
Abstract
In this paper we present LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will be used during the execution of the robot’s path. LQG-MP is based on the linear-quadratic controller with Gaussian models of uncertainty, and explicitly characterizes in advance (i.e. before execution) the a priori probability distributions of the state of the robot along its path. These distributions can be used to assess the quality of the path, for instance by computing the probability of avoiding collisions. Many methods can be used to generate the required ensemble of candidate paths from which the best path is selected; in this paper we report results using rapidly exploring random trees (RRT). We study the performance of LQG-MP with simulation experiments in three scenarios: (A) a kinodynamic car-like robot, (B) multi-robot planning with differential-drive robots, and (C) a 6-DOF serial manipulator. We also present a method that applies Kalman smoothing to make paths C k-continuous and apply LQG-MP to precomputed roadmaps using a variant of Dijkstra’s algorithm to efficiently find high-quality paths.
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Cites
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Path Planning with Modified a Star Algorithm for a Mobile Robot
Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T., & Jurišica, L. (2014) · Procedia Engineering · doi:10.1016/j.proeng.2014.12.098 · Cited by 869
A Survey of Research on Cloud Robotics and Automation
Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2376492 · Cited by 833
Self-citationPerception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
Intention-aware online POMDP planning for autonomous driving in a crowd
Bai, H., Cai, S., Ye, N., Hsu, D., & Lee, W. S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139219 · Cited by 321
Motion planning under uncertainty using iterative local optimization in belief space
van den Berg, J., Patil, S., & Alterovitz, R. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364912456319 · Cited by 299
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Metadata
- Authors
- van den Berg, J., Abbeel, P., & Goldberg, K.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2011-06
- Volume
- 30
- Issue
- 7
- Pages
- 895-913
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2002201291
- Cited by
- 395
- References
- 31
- FWCI
- 27.26
- Type
- journal article
- Language
- en
- Open access
- closed
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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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