Duplication of modules facilitates the evolution of functional specialization

The evolution of simulated robots with three different architectures is studied. We compared a non-modular feed forward network, a hardwired modular and a duplication-based modular motor control network. We conclude that both modular architectures outperform the non-modular architecture, both in terms of rate of adaptation as well as the level of adaptation achieved. The main difference between the hardwired and duplication-based modular architectures is that in the latter the modules reached a much higher degree of functional specialization of their motor control units with regard to high level behavioral functions. The hardwired architectures reach the same level of performance, but have a more distributed assignment of functional tasks to the motor control units. We conclude that the mechanism through which functional specialization is achieved is similar to the mechanism proposed for the evolution of duplicated genes. It is found that the duplication of multifunctional modules first leads to a change in the regulation of the module, leading to a differentiation of the functional context in which the module is used. Then the module adapts to the new functional context. After this second step the system is locked into a functionally specialized state. We suggest that functional specialization may be an evolutionary absorption state

One-Chip Solution to Intelligent Robot Control: Implementing Hexapod Subsumption Architecture Using a Contemporary Microprocessor

This paper introduces a six-legged autonomous robot managed by a single controller and a software core modeled on subsumption architecture. We begin by discussing the features and capabilities of IsoPod, a new processor for robotics which has enabled a streamlined implementation of our project. We argue that this processor offers a unique set of hardware and software features, making it a practical development platform for robotics in general and for subsumption-based control architectures in particular. Next, we summarize original ideas on subsumption architecture implementation for a six-legged robot, as presented by its inventor Rodney Brooks in 1980s. A comparison is then made to a more recent example of a hexapod control architecture based on subsumption. The merits of both systems are analyzed and a new subsumption architecture layout is formulated as a response. We conclude with some remarks regarding the development of this project as a hint at new potentials for intelligent robot design, opened by a recent development in embedded controller market

Synchronizing a modular robot colony for cooperative tasks based on intrainter robot communications

The implementation of robotic cooperative tasks such as pushing an object toward a desired destination or manipulating an object using mobile robots or robotic arms requires motion coordination between the robot colony. When a robot is built by the union of several robots, such as modular robot systems, it is critical to have the complete coordination of each robot configuration within the colony and also overall robot coordination of the colony. The paper presents a demonstration of parallel motion for modular robot configurations through the combination of two types of communications, i.e., Inter-robot and Intra-robot communications. The two types of communications are described and implemented in a real modular robot system. Experiments are executed to show the performance of the robot colony synchronizatio

AltURI: a thin middleware for simulated robot vision applications

Fast software performance is often the focus when developing real-time vision-based control applications for robot simulators. In this paper we have developed a thin, high performance middleware for USARSim and other simulators designed for real-time vision-based control applications. It includes a fast image server providing images in OpenCV, Matlab or web formats and a simple command/sensor processor. The interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications; landing using a reinforcement learning algorithm and altitude control using elementary motion detection. The middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use

Differentiable Algorithm Networks for Composable Robot Learning

This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in order to achieve the best overall system performance. We illustrate the DAN methodology through a case study on a simulated robot system, which learns to navigate in complex 3-D environments with only local visual observations and an image of a partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at https://youtu.be/4jcYlTSJF4

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible