A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys

In this thesis is presented a novel approach to model, control, and planning the motion of a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a nonholonomic cart (York mail trolley) in a loosely structured environment. The method is based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its kinematics properties. In consequence, two robot configurations are produced by the task of grasping and ungrasping the load, they are: the single-robot configuration and the robot-trolley configuration. Furthermore, in order to comply with the general planar motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for each configuration, the robot has been provided with two motorized steerable wheels in order to have a flexible platform able to adapt to these restrictions. [Continues.

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

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Control of Real Mobile Robot Using Artificial Intelligence Technique

An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently

Advanced Robot Path Planning (RRT)

Tato diplomová práce práce se zabývá plánováním cesty všesměrového mobilního robotu pomocí algoritmu RRT (Rapidly-exploring Random Tree – Rychle rostoucí náhodný strom). Teoretická část popisuje základní algoritmy plánování cesty a prezentuje bližší pohled na RRT a jeho potenciál. Praktická část práce řeší návrh a tvorbu v zásadě multiplatformní C++ aplikace v prostředí Windows 7 za použití aplikačního frameworku Qt 4.8.0, která implementuje pokročilé RRT algoritmy s parametrizovatelným řešičem a speciálním dávkovým režimem. Tento mód slouží k testování efektivnosti nastavení řešiče pro dané úlohy a je založen na post-processingu a vizualizaci výstupu měřených úloh pomocí jazyka Python. Vypočtené cesty mohou být vylepšeny pomocí zkracovacích algoritmů a výsledná trajektorie odeslána do pohonů Maxon Compact Drive všesměrové mobilní platformy pomocí CANopen. Aplikace klade důraz na moderní grafické uživatelské rozhraní se spolehlivým a výkonným 2D grafickým engine.This master's thesis deals with path planning of omnidirectional mobile robot using the RRT algorithm (Rapidly-exploring Random Tree). Theoretical part describes basic algorithms of path planning and presents closer view on RRT and its potential. Practical part deals with designing and creation of essentially multiplatform C++ application in Windows 7 environment with Qt 4.8.0 application framework, which implements advanced RRT algorithms with user-programmable solver and special batch mode. This mode is used for testing the effectiveness of solver on given tasks and it is based on postprocessing and visualization of measurement tasks output by Python language. Computed paths can be enhanced by shortening algorithms and result trajectory sent to Maxon Compact Drives of omnidirectional platform via the CANopen. Application puts emphasis on modern GUI with reliable and powerful 2D graphics engine.