Search

For agile, accurate autonomous robotics, it is desirable to plan motion in the presence of uncertainty. The Partially Observable Markov Decision Process (POMDP) provides a principled framework for this. Despite the tremendous advances of POMDP-based planning, most can only solve problems with a small and discrete set of actions. This paper presents General Pattern Search in Adaptive Belief Tree (GPS-ABT), an approximate and online POMDP solver for problems with continuous action spaces. Generalized Pattern Search (GPS) is used as a search strategy for action selection. Under certain conditions, GPS-ABT converges to the optimal solution in probability. Results on a box pushing and an extended Tag benchmark problem are promising.