Title :
Incremental sampling-based algorithm for risk-aware planning under motion uncertainty
Author :
Wei Liu ; Ang, M.H.
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore
fDate :
May 31 2014-June 7 2014
Abstract :
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.
Keywords :
Gaussian noise; collision avoidance; mobile robots; motion control; optimal control; sampling methods; trajectory control; CC-RRT*-D; Gaussian motion noise; RRT* framework; asymptotically optimal property; asymptotically optimal trajectories; autonomous vehicle; chance-constraint approximation; incremental sampling-based algorithm; linear systems; motion planning; motion uncertainty; over-conservative problem; real-time obstacle avoidance; risk-aware planning algorithm; risk-aware trajectories; Approximation algorithms; Approximation methods; Noise; Planning; Probabilistic logic; Uncertainty; Vehicles;
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
DOI :
10.1109/ICRA.2014.6907131