Goal Directed Navigation with Uncertainty in Adversary Locations
Maxim Likhachev and Anthony Stentz
Carnegie Mellon University
This paper addresses the problem of planning for goal directed navigation in the environment that contains a number of possible adversary locations. It first shows that commonly used approaches such as assumptive planning can result in very long and costly robot traverses. It then shows how one can solve the same problem using a general probabilistic planner we have recently developed called PPCP (Probabilistic Planning with Clear Preferences). The paper also introduces two optimizations to the PPCP algorithm that make it run up to five times faster for our domain. The experimental results show that solving the problem with PPCP can substantially reduce the expected execution cost as compared to assumptive planning.