An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure

When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster by taking a more challenging path. This paper proposes a new approach to planning a control sequence with a guaranteed risk bound. Given a stochastic dynamic model, the problem is to find a control sequence that optimizes a performance metric, while satisfying chance constraints i.e. constraints on the upper bound of the probability of failure. We propose a two-stage optimization approach, with the upper stage optimizing the risk allocation and the lower stage calculating the optimal control sequence that maximizes reward. In general, the upper-stage is a non-convex optimization problem, which is hard to solve. We develop a new iterative algorithm for this stage that efficiently computes the risk allocation with a small penalty to optimality. The algorithm is implemented and tested on the autonomous underwater...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Masahiro Ono, Brian C. Williams
• Keywords: AAAI 2008, Control Sequence, Intelligent Agents, Non-convex Optimization Problem, Risk Allocation