This project aims to develop a planning algorithm under partially unknown 3D discrete
space. Value iteration is employed since MDP complies with the state transition and …

In terms of navigation, a central problem in the field of autonomous robotics is obstacle
avoidance. This research explores how to navigate as well as avoid obstacles by leveraging …

This paper presents a new navigation architecture for autonomous mobile robots working in
uncertain domains. Partially Observable Markov Decision Processes (POMDPs) are suitable …

Path Planning in a collaborative mobile robot system has been a research topic for many
years. Uncertainty in robot states, actions, and environmental conditions makes finding the …

Decomposing an MDP consists in (1) partitioning the state space into regions;(2) solving
each region independently;(3) combining the local solutions to generate a global one. In our …

In this article we present an approach to improve the execution time of the Markov decision
process (MDP) used in robotics for path planning. We've improved it for both value iteration …

In this paper, the multi-goal motion planning problem of an environment with some
background information about its map is addressed in detail. The motion planning goal is to …

An important problem in robotics is planning and selecting actions for goal-directed behavior
in noisy uncertain environments. The problem is typically addressed within the framework of …

Designing mobile robots that navigate indoor environments autonomously is known to be a
difficult problem. A critical issue is in the formulation of robust motion control algorithms …

Deterministic motion planners perform well in simulated environments, where sensors and
actuators are perfect. However, these assumptions are restrictive and consequently motion …