High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning

Massively parallel motion planning algorithms under uncertainty using POMDP

We present new parallel algorithms that solve continuous-state partially observable Markov decision process (POMDP) problems using the GPU (gPOMDP) and a hybrid of the GPU and CPU (hPOMDP). We choose the Monte Carlo value iteration (MCVI) method as our base algorithm and parallelize this algorithm using the multi-level parallel formulation of MCVI. For each parallel level, we propose efficient ...

FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

In this paper we present FIRM (Feedback-based Information RoadMap), a multi-query approach for planning under uncertainty, that is a belief-space variant of Probabilistic Roadmap Methods (PRMs). The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the ...

FIRM : Sampling - based feedback motion planning under motion uncertainty and imperfect

In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal...

Sampling-Based Motion Planning Using Uncertain Knowledge

Sampling-based algorithms have dramatically improved the state of the art in robotic motion planning. However, these approaches still make significant assumptions that limit their applicability for real-world planning. This work describes how one of these assumptions: that the world is perfectly known, can be removed. We propose a predictive roadmap planner that incorporates uncertainty directl...

Sampling-Based Motion Planning