The motion planning problem for mobile robots is typically formulated as follows: given a robot and a description of an environment, plan a path of the robot between two specified locations, which is collision-free and satisfies certain optimization criteria. Traditionally there are two approaches to the problem: Off-line planning, which assumes perfectly known and stable environment, and on-li...

rapid urban growth is resulting into increase in travel demand and private vehicle ownership in urban areas. in the present scenario the existing infrastructure has failed to match the demand that leads to traffic congestion, vehicular pollution and accidents. with traffic congestion augmentation on the road, delay of commuters has increased and reliability of road network has decreased. four s...

Recently, the distributed agent concept has become a new computing paradigm in the Internet distributed computing, including the mobile computing. Mobile agent planning is one of the most important techniques for completing a given task efficiently. The static planning technique may not be the best approach in real network environments. This is mainly due to the fluctuation of network traffic, ...

The ever-growing complexity of software systems makes it increasingly challenging to foresee at design time all interactions between a system and its environment. Most self-adaptive systems trigger adaptations through operators that are statically configured for specific environment and system conditions. However, in the occurrence of uncertain conditions, self-adaptive decisions may not be eff...

| This paper addresses motion planning for a car-like robot moving in a changing planar workspace, i.e. with moving obstacles. First, this motion planning problem is formulated in the state-time space framework. The state-time space of a robot is its state space plus the time dimension. In this framework part of the constraints at hand are translated into static forbidden regions of state-time ...

In this paper we describe the version of the planner ROLLER submitted to the learning track of the International Planning Competition. This version, learns domain dependent general policies with the aim of improving a lookahead strategy for forward search planning. ROLLER performs the policy learning in a two-step classification process with the relational classifier TILDE. At the first step th...

Along with the development of robot technology, a robot not only need to complete a specific task, but aslo need to do path planning in the process of performing the task. So, path planning is widly studied. This paper introduce a method of robot path planning based on reinforcement learning,which aimed at Markovian decision process. In this paper, we introduce the basic concept, principle and ...