Achieving cooperation in a distributed multi-robot team

DMAPS (Distributed Multi-Agent Planning System) is a planning system developed for distributed multi-robot teams based on MAPS(Multi-Agent Planning System). MAPS assumes that each agent has the same global view of the environment in order to determine the most suitable actions. This assumption fails when perception is local to the agents: each agent has only a partial and unique view of the environment. DMAPS addresses this problem by creating a probabilistic global view on each agent by fusing the perceptual information from each robot. The experimental results on consuming tasks show that while the probabilistic global view is not identical on each robot, the shared view is still effective in increasing performance of the team.

Localization of a mobile robot based on odometry and natural landmarks using extended kalman filter

SANTANA, André M.; SOUZA, Anderson A. S.; BRITTO, Ricardo S.; ALSINA, Pablo J.; MEDEIROS, Adelardo A. D. Localization of a mobile robot based on odometry and natural landmarks using extended Kalman Filter. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

The Inverse Jacobian Control Method Applied to a Four Degree of Freedom Confined Space Robot

Thesis (Master's)--University of Washington, 2016-06

Localization of a mobile robot based on odometry and natural landmarks using extended kalman filter

SANTANA, André M.; SOUZA, Anderson A. S.; BRITTO, Ricardo S.; ALSINA, Pablo J.; MEDEIROS, Adelardo A. D. Localization of a mobile robot based on odometry and natural landmarks using extended Kalman Filter. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

Where Are We After Five Editions?: Robot Vision Challenge, a Competition that Evaluates Solutions for the Visual Place Classification Problem

This article describes the Robot Vision challenge, a competition that evaluates solutions for the visual place classification problem. Since its origin, this challenge has been proposed as a common benchmark where worldwide proposals are measured using a common overall score. Each new edition of the competition introduced novelties, both for the type of input data and subobjectives of the challenge. All the techniques used by the participants have been gathered up and published to make it accessible for future developments. The legacy of the Robot Vision challenge includes data sets, benchmarking techniques, and a wide experience in the place classification research that is reflected in this article.

Vision-Based Solutions for Human-Robot Collaboration in Industrial Workcells

Industrial robots are both versatile and high performant, enabling the flexible automation typical of the modern Smart Factories. For safety reasons, however, they must be relegated inside closed fences and/or virtual safety barriers, to keep them strictly separated from human operators. This can be a limitation in some scenarios in which it is useful to combine the human cognitive skill with the accuracy and repeatability of a robot, or simply to allow a safe coexistence in a shared workspace. Collaborative robots (cobots), on the other hand, are intrinsically limited in speed and power in order to share workspace and tasks with human operators, and feature the very intuitive hand guiding programming method. Cobots, however, cannot compete with industrial robots in terms of performance, and are thus useful only in a limited niche, where they can actually bring an improvement in productivity and/or in the quality of the work thanks to their synergy with human operators. The limitations of both the pure industrial and the collaborative paradigms can be overcome by combining industrial robots with artificial vision. In particular, vision can be exploited for a real-time adjustment of the pre-programmed task-based robot trajectory, by means of the visual tracking of dynamic obstacles (e.g. human operators). This strategy allows the robot to modify its motion only when necessary, thus maintain a high level of productivity but at the same time increasing its versatility. Other than that, vision offers the possibility of more intuitive programming paradigms for the industrial robots as well, such as the programming by demonstration paradigm. These possibilities offered by artificial vision enable, as a matter of fact, an efficacious and promising way of achieving human-robot collaboration, which has the advantage of overcoming the limitations of both the previous paradigms yet keeping their strengths.

Development of a Robotic Wires Storage System for Switchgear Manufacturing Automation

Considering the great development of robotics in industrial automation, the Remodel project aims to reproduce, through the use of Cobots, the wiring activity typical of a human operator and to realize an autonomous storage work. My researches focused on this second topic. In this paper, we will see how to realize a gripper compatible with an Omron TM5X-900, able to perform a pick and place of different types of cables, but also how to compute possible trajectories. In particular, what I needed, was a trajectory going from the Komax, the cables production machine, to a Warehouse taking into account the possible entangles of cables with the robot during its motion. The last part has been dedicated to the synchronization between robot and main machine work.

Operazioni di assemblaggio automatizzato tramite robot antropomorfo e visione artificiale

Obiettivo dello studio condotto è l’implementazione di cicli di operazioni per l’assemblaggio automatizzato di componenti che costituiscono un sistema di trasporto a catena presente in alcune macchine automatiche. L’automazione del processo, fino ad oggi svolto manualmente, prevede l’utilizzo di un robot e, per il controllo di quest’ultimo, di un sistema di visione artificiale. L’attività di tirocinio associata alla tesi di laurea, che ha incluso una parte sperimentale oltre alla scrittura degli algoritmi di controllo del robot, è stata svolta all’interno del laboratorio TAILOR (Technology and Automation for Industry LabORatory) presso Siropack Italia S.r.l dove è presente una cella dotata di robot antropomorfo (Mitsubishi Electric) e di sistema di visione artificiale con camere 2D (Omron). La presenza di quest’ultimo è risultata strategica in termini di possibilità di adattare il montaggio anche a diversi posizionamenti degli oggetti all’interno dello spazio di lavoro, fermo restando che gli stessi risultassero appoggiati su una superficie piana. In primo luogo, affinché fosse garantita la ripetibilità del processo, sono state testate le prestazioni del sistema di visione tramite opportuna calibrazione della camera e del sistema di illuminazione ad esso collegata, al fine di ottenere un’acquisizione delle immagini che fosse sufficientemente robusta e risoluta mediante lo sfruttamento del software di elaborazione Omron FH Vision System. Un’opportuna programmazione della traiettoria del robot in ambiente di simulazione RT Toolbox 3, software integrato nel sistema di controllo del robot Mitsubishi Electric, ha infine consentito le regolari operazioni di assemblaggio, garantendo un processo affidabile ed, allo stesso tempo, adattabile ad ambienti eventualmente non strutturati in cui esso si trova ad operare.

Cheaper Faster Drug Development Validated By The Repositioning Of Drugs Against Neglected Tropical Diseases.

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

Neural network-based H(infinity) control for fully actuated and underactuated cooperative manipulators

This paper develops H(infinity) control designs based on neural networks for fully actuated and underactuated cooperative manipulators. The neural networks proposed in this paper only adapt the uncertain dynamics of the robot manipulators. They work as a complement of the nominal model. The H(infinity) performance index includes the position errors as well the squeeze force errors between the manipulator end-effectors and the object, which represents a complete disturbance rejection scenario. For the underactuated case, the squeeze force control problem is more difficult to solve due to the loss of some degrees of manipulator actuation. Results obtained from an actual cooperative manipulator, which is able to work as a fully actuated and an underactuated manipulator, are presented. (C) 2008 Elsevier Ltd. All rights reserved.

Coloured Petri nets and graphical simulation for the validation of a robotic cell in aircraft industry

This paper proposes a mixed validation approach based on coloured Petri nets and 3D graphic simulation for the design of supervisory systems in manufacturing cells with multiple robots. The coloured Petri net is used to model the cell behaviour at a high level of abstraction. It models the activities of each cell component and its coordination by a supervisory system. The graphical simulation is used to analyse and validate the cell behaviour in a 3D environment, allowing the detection of collisions and the calculation of process times. The motivation for this work comes from the aeronautic industry. The automation of a fuselage assembly process requires the integration of robots with other cell components such as metrological or vision systems. In this cell, the robot trajectories are defined by the supervisory system and results from the coordination of the cell components. The paper presents the application of the approach for an aircraft assembly cell under integration in Brazil. This case study shows the feasibility of the approach and supports the discussion of its main advantages and limits. (C) 2011 Elsevier Ltd. All rights reserved.

Hybrid fault diagnosis scheme implementation for power distribution systems automation

Power distribution automation and control are import-ant tools in the current restructured electricity markets. Unfortunately, due to its stochastic nature, distribution systems faults are hardly avoidable. This paper proposes a novel fault diagnosis scheme for power distribution systems, composed by three different processes: fault detection and classification, fault location, and fault section determination. The fault detection and classification technique is wavelet based. The fault-location technique is impedance based and uses local voltage and current fundamental phasors. The fault section determination method is artificial neural network based and uses the local current and voltage signals to estimate the faulted section. The proposed hybrid scheme was validated through Alternate Transient Program/Electromagentic Transients Program simulations and was implemented as embedded software. It is currently used as a fault diagnosis tool in a Southern Brazilian power distribution company.