Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk
  This paper presents a model-based planner called the Probabilistic Sulu
Planner or the p-Sulu Planner, which controls stochastic systems in a goal
directed manner within user-specified risk bounds. The objective of the p-Sulu
Planner is to allow users to command continuous, stochastic systems, such as
unmanned aerial and space vehicles, in a manner that is both intuitive and
safe. To this end, we first develop a new plan representation called a
chance-constrained qualitative state plan (CCQSP), through which users can
specify the desired evolution of the plant state as well as the acceptable
level of risk. An example of a CCQSP statement is go to A through B within 30
minutes, with less than 0.001% probability of failure." We then develop the
p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to
enable CCQSP planning, we develop the following two capabilities in this paper:
1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a
continuous domain with temporal constraints. The first capability is to ensures
that the probability of failure is bounded. The second capability is essential
for the planner to solve problems with a continuous state space such as vehicle
path planning. We demonstrate the capabilities of the p-Sulu Planner by
simulations on two real-world scenarios: the path planning and scheduling of a
personal aerial vehicle as well as the space rendezvous of an autonomous cargo
spacecraft.
