The Partially Observable Markov Decision Process (POMDP), a mathematical framework for decision-making in uncertain environments suffers from the curse of dimensionality. There are various methods that can handle huge sizes of POMDP matrices to create approximate solutions, but no serious effort has been reported to effectively control the size of the POMDP matrices. Manually creating the high-dimension matrices of a POMDP model is a cumbersome and sometimes even impossible task. The PCMRPP (POMDP file Creator for Mobile Robot Path Planning) software package implements a novel algorithm to programmatically generate these matrices such that: • The sizes of the matrices can be controlled by configuring the granularity of discretization of the...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
One of the fundamental challenges in the design of autonomous robots is to reliably compute motion s...
POMDPs provide a rich framework for planning and control in partially observable domains. Recent new...
Planning under partial observability is both challenging and critical for reliable robot operation. ...
Publisher Copyright: IEEENoisy sensing, imperfect control, and environment changes are defining char...
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomo...
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomo...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for ...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for ...
As agents are built for ever more complex environments, methods that consider the uncertainty in the...
Partially observable Markov decision processes (POMDPs) are a well studied paradigm for programming ...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
Partially observable Markov decision process (POMDP) is a formal model for planning in stochastic do...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
One of the fundamental challenges in the design of autonomous robots is to reliably compute motion s...
POMDPs provide a rich framework for planning and control in partially observable domains. Recent new...
Planning under partial observability is both challenging and critical for reliable robot operation. ...
Publisher Copyright: IEEENoisy sensing, imperfect control, and environment changes are defining char...
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomo...
Motion planning in uncertain and dynamic environments is critical for reliable operation of autonomo...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for ...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for ...
As agents are built for ever more complex environments, methods that consider the uncertainty in the...
Partially observable Markov decision processes (POMDPs) are a well studied paradigm for programming ...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
Partially observable Markov decision process (POMDP) is a formal model for planning in stochastic do...
Standard value function approaches to finding policies for Partially Observable Markov Decision Proc...
One of the fundamental challenges in the design of autonomous robots is to reliably compute motion s...
POMDPs provide a rich framework for planning and control in partially observable domains. Recent new...