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 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.