Simulation of underwater robots using MS Robot Studio©

One stage in designing the control for underwater robot swarms is to confirm the control algorithms via simulation. To perform the simulation Microsoftpsilas Robotic Studiocopy was chosen. The problem with this simulator and others like it is that it is set up for land-based robots only. This paper explores one possible way to get around this limitation. This solution cannot only work for underwater vehicles but aerial vehicles as well.<br /

Design of a prototype underwater research platform for swarm robotics

To perform under water robotic research requires specialized equipment. A few pieces of electronics atop a set of wheels are not going to cut it. An underwater research platform must be waterproof, reliable, robust, recoverable and easy to maintain. It must also be able to move in 3 dimensions. Also it must be able to navigate and avoid obstacles. Further if this platform is to be part of a swarm of like platforms then it must be cost effective and relatively small. To purchase such a platform can be very expensive. However, for shallow water, a suitable platform can be built from mostly off the shelf items at little cost. This article describes the design of one such underwater robot including various sensors and communications systems that allow for swarm robotics. Whilst the robotic platform performs well, to explore what many of them would do, that is more than are available, simulation is required. This article continues to study how best to simulate these robots for a swarm, or system of systems, approach

Control and simulation of robotic swarms in heterogeneous environments

Simulation provides a low cost method of initial testing of control for robotic swarms. The expansion of robotic swarms to heterogeneous environments drives the need to model cooperative operation in those environments. The Autonomous Control Engineering center at The University of Texas at San Antonio is investigating methods of simulation techniques and simulation environments. This paper presents results from adapting simulation tools for diverse environments.<br /

RF communication between surface and underwater robotic swarms

In order for underwater robots to communicate with land and air based robots on an equal basis, high speed communications is required. If the robots are not to be tethered then wireless communications is the only possibility. Sonar communications is too slow. Unfortunately radio waves are rapidly attenuated under water due to phenomena such as skin depth. These experiments attempt to extend the range of underwater radio communications.<br /

Underwater swarm robotics: Challenges and opportunities

Underwater swarm robotics today faces a series of challenges unique to its aquatic environment. This chapter explores some possible applications of underwater swarm robotics and its challenges. Those challenges include the environment itself, sensor types required, problems with communication and the difficulty in localisation. It notes the serious challenges in underwater communication is that radio communications is practically non-existent in the underwater realm. Localisation also becomes problematic due to the lack of radio waves as GPS cannot be used. It also looks at the platforms required by underwater robots and includes a possible low-cost platform. Also explored is a method of swarm robotics control known as consensus control. It shows possible solutions to the challenges and where swarm robotics may head

Finite time point-stabilization of underwater spherical roving robot

This paper addresses the point stabilization problem for the underwater spherical roving robot (BYSQ-3) in the horizontal plane. The finite-time stable control laws are adopted to steer the robot to the origin fast, accurately and reliably. Firstly, the inner structure and operational principle of the robot is described and the kinematic and dynamic equations are established. Secondly, the diffeomorphism transformation and change of inputs are introduced to decouple the multivariable coupling system into two subsystems. The second subsystem consists of two double integrator systems. The finite-time controller is introduced to ensure part states converge to zero in finite time. Then, the other states are steered to the origin using the same method. Thirdly, the design process has no virtual input and the stability analysis is simple, the controller designed is easy for engineering implementation. The simulation and experiment results are presented to validate the shorter convergence time and better stability character of the controller

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Distributed Control Architecture

This document describes the development and testing of a novel Distributed Control Architecture (DCA). The DCA developed during the study is an attempt to turn the components used to construct unmanned vehicles into a network of intelligent devices, connected using standard networking protocols. The architecture exists at both a hardware and software level and provides a communication channel between control modules, actuators and sensors. A single unified mechanism for connecting sensors and actuators to the control software will reduce the technical knowledge required by platform integrators and allow control systems to be rapidly constructed in a Plug and Play manner. DCA uses standard networking hardware to connect components, removing the need for custom communication channels between individual sensors and actuators. The use of a common architecture for the communication between components should make it easier for software to dynamically determine the vehicle s current capabilities and increase the range of processing platforms that can be utilised. Implementations of the architecture currently exist for Microsoft Windows, Windows Mobile 5, Linux and Microchip dsPIC30 microcontrollers. Conceptually, DCA exposes the functionality of each networked device as objects with interfaces and associated methods. Allowing each object to expose multiple interfaces allows for future upgrades without breaking existing code. In addition, the use of common interfaces should help facilitate component reuse, unit testing and make it easier to write generic reusable software