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.

[1]  Marcelo H. Ang,et al.  Autonomy for mobility on demand , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Emilio Frazzoli,et al.  Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  Emilio Frazzoli,et al.  An incremental sampling-based algorithm for stochastic optimal control , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Jonathan P. How,et al.  Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees , 2013 .

[5]  N. Roy,et al.  The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance , 2009, Int. J. Robotics Res..

[6]  P. Abbeel,et al.  LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2011 .

[7]  Hui X. Li,et al.  A probabilistic approach to optimal robust path planning with obstacles , 2006, 2006 American Control Conference.

[8]  Nicholas Roy,et al.  Adapting probabilistic roadmaps to handle uncertain maps , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[9]  Ron Alterovitz,et al.  Estimating probability of collision for safe motion planning under Gaussian motion and sensing uncertainty , 2012, 2012 IEEE International Conference on Robotics and Automation.

[10]  Pieter Abbeel,et al.  LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2010, Int. J. Robotics Res..

[11]  Masahiro Ono,et al.  Iterative Risk Allocation: A new approach to robust Model Predictive Control with a joint chance constraint , 2008, 2008 47th IEEE Conference on Decision and Control.

[12]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[13]  J. How,et al.  Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty , 2010 .

[14]  Nicholas Roy,et al.  Rapidly-exploring Random Belief Trees for motion planning under uncertainty , 2011, 2011 IEEE International Conference on Robotics and Automation.

[15]  Masahiro Ono,et al.  A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control , 2010, IEEE Transactions on Robotics.

[16]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[17]  Reid G. Simmons,et al.  Particle RRT for Path Planning with Uncertainty , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[18]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.