Incremental sampling-based algorithm for risk-aware planning under motion uncertainty

This paper considers the problem of motion planning for linear systems subject to Gaussian motion noise and proposes a risk-aware planning algorithm: CC-RRT*-D. The proposed CC-RRT*-D employs the chance-constraint approximation and leverages the asymptotically optimal property of RRT* framework to c...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings - IEEE International Conference on Robotics and Automation s. 2051 - 2058
Hlavní autoři: Wei Liu, Ang, Marcelo H.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2014
Témata:
Approximation algorithms
Approximation methods
Noise
Planning
Probabilistic logic
Uncertainty
Vehicles
ISSN:1050-4729
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper considers the problem of motion planning for linear systems subject to Gaussian motion noise and proposes a risk-aware planning algorithm: CC-RRT*-D. The proposed CC-RRT*-D employs the chance-constraint approximation and leverages the asymptotically optimal property of RRT* framework to compute risk-aware and asymptotically optimal trajectories. By explicitly considering the state dependence for prior state estimate, the over-conservative problem of chance-constraint approximation can be provably solved. Computational experiment results show that CC-RRT*-D is efficient and robust compared with related algorithms. The real-time experiment on an autonomous vehicle shows that our proposed algorithm is applicable to real-time obstacle avoidance.
ISSN:1050-4729
DOI:10.1109/ICRA.2014.6907131