Intelligent Adaptive Curiosity: a source of Self-Development

This paper presents the mechanism of Intelligent Adaptive Curiosity. This is a drive which pushes the robot towards situations in which it maximizes its learning progress. It makes the robot focus on situations which are neither too predictable nor too unpredictable. This mechanism is a source of self-development for the robot: the complexity of its activity autonomously increases. Indeed, we show that it first spends time in situations which are easy to learn, then shifts progressively its attention to situations of increasing difficulty, avoiding situations in which nothing can be learnt

Topological Navigation of Simulated Robots using Occupancy Grid

Formerly I presented a metric navigation method in the Webots mobile robot simulator. The navigating Khepera-like robot builds an occupancy grid of the environment and explores the square-shaped room around with a value iteration algorithm. Now I created a topological navigation procedure based on the occupancy grid process. The extension by a skeletonization algorithm results a graph of important places and the connecting routes among them. I also show the significant time profit gained during the process

Fast Simulation of Vehicles with Non-deformable Tracks

This paper presents a novel technique that allows for both computationally fast and sufficiently plausible simulation of vehicles with non-deformable tracks. The method is based on an effect we have called Contact Surface Motion. A comparison with several other methods for simulation of tracked vehicle dynamics is presented with the aim to evaluate methods that are available off-the-shelf or with minimum effort in general-purpose robotics simulators. The proposed method is implemented as a plugin for the open-source physics-based simulator Gazebo using the Open Dynamics Engine.Comment: Submitted to IROS 201

Realistic simulation of spatial computers and robot swarms

The goal of Amorphous Computing is defined as: “To identify organizational principles and create programming technologies for obtaining intentional, pre-specified behavior from the cooperation of myriad unreliable parts that are arranged in unknown, irregular, and time-varying ways” [1]. Amorphous Facades are stationary formations of amorphous computers used in building environments and are constructed as a wall. One of the desired functionalities of the Amorphous walls is to be able to track occupancy within an interior environment. Pymorphous is a spatial computing library for Python. Currently, Pymorphous has its own simulator, but the simulator is very abstract and doesn\u27t realistically simulate physical robots or device hardware limitations. Webots is a virtual robot simulation program that is much less abstract that the Pymorphous simulator and that accurately simulates physics and realistic hardware. The simulator-runtime for Pymorphous is very specific to its own simulator. To allow Pymorphous to be simulated in a less abstract environment, Webots, I will create a new runtime which will facilitate communication between amorphous computing robots within Webots and Pymorphous. To demonstrate the functionality of the Webots-runtime for Pymorphous, I will develop three simulations within Webots. A simple neighborhood simulation will be used to show the functionality of Pymorphous neighborhood calculation between amorphous wall panels in Webots. A velocity tracking simulation will be used to demonstrate the functionality of simple tracking algorithms within Webots, similar to algorithms that the wall might actually use to track occupancy. Lastly, the setup of the Amorphous Wall within Webots will be changed to reflect mobile robots to illustrate the ability of Webots to simulate more complex Pymorphous flocking algorithms on mobile robots

An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on Artificial Immune Systems (ICARIS2008), Phuket, Thailan