Sensor based navigation for car-like mobile robots using generalized Voronoi graph

Our research objective is to realize sensor based navigation by car-like mobile robots. The generalized Voronoi graph (GVG) can describe a mobile robot's path for sensor based navigation from the point of view of completeness and safety. However, it is impossible to apply the path to a car-like mobile robot directly, because limitation of the minimum turning radius prevents following the non-smooth GVG. To solve the problem, we propose a local smooth path planning algorithm for car-like mobile robots. Basically, an initial path is generated by a conventional path planning algorithm using GVG theory, and it is deformed smoothly to enable car-like robots' following by maximizing an evaluation function proposed in the paper. The key topics are: definition of our evaluation function; and how to modify the GVG. We introduce a local smooth path planning algorithm based on the GVG, and explain a detail of the evaluation function. Simulation results support validity of the algorithm </p

Modelling Human-like Behavior through Reward-based Approach in a First-Person Shooter Game

We present two examples of how human-like behavior can be implemented in a model of computer player to improve its characteristics and decision-making patterns in video game. At first, we describe a reinforcement learning model, which helps to choose the best weapon depending on reward values obtained from shooting combat situations.Secondly, we consider an obstacle avoiding path planning adapted to the tactical visibility measure. We describe an implementation of a smoothing path model, which allows the use of penalties (negative rewards) for walking through \bad" tactical positions. We also study algorithms of path nding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank algorithm. All the approaches demonstrate how human behavior can be modeled in applications with significant perception of intellectual agent actions

Sensor based navigation for car-like mobile robots using generalized Voronoi graph

Our research objective is to realize sensor based navigation by car-like mobile robots. The generalized Voronoi graph (GVG) can describe a mobile robot's path for sensor based navigation from the point of view of completeness and safety. However, it is impossible to apply the path to a car-like mobile robot directly, because limitation of the minimum turning radius prevents following the non-smooth GVG. To solve the problem, we propose a local smooth path planning algorithm for car-like mobile robots. Basically, an initial path is generated by a conventional path planning algorithm using GVG theory, and it is deformed smoothly to enable car-like robots' following by maximizing an evaluation function proposed in the paper. The key topics are: definition of our evaluation function; and how to modify the GVG. We introduce a local smooth path planning algorithm based on the GVG, and explain a detail of the evaluation function. Simulation results support validity of the algorithm </p