Motion planning under uncertainty for robotic tasks with long time horizons

Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation of autonomous robots. By using probabilistic sampling, pointbased POMDP solvers have drastically improved the speed of POMDP planning, enabling POMDPs to handle moderately complex robotic tasks. However, robot motion planning tasks with long time horizons remain a severe obstacle for even the fastest point-based POMDP solvers today. This paper proposes Milestone Guided Sampling (MiGS), a new point-based POMDP solver, which exploits state space information to reduce the effective planning horizon. MiGS samples a set of points, called milestones, from a robot’s state space, uses them to construct a compact, sampled representation of the state space, and then uses this representation of the state space to guide sampling in the belief space. This strategy reduces the effective planning horizon, while still cap...

• Type: Journal
• Year: 2011
• Where: IJRR
• Authors: Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Lee
• Keywords: Fastest Point-based Pomdp, IJRR 2011, Point-based Pomdp Solvers, State Space