António Paulo Moreira

Practical Approach of Modeling and Parameters Estimation for Omnidirectional Mobile Robots

This paper presents a nonlinear modeling approach for omnidirectional mobile robots. Three experimental methods of estimation of the parameters related to dynamic equations of the robots model are developed. The estimation methods are based on least-squares methods to obtain the viscous friction coefficients, the coulomb friction coefficients, and the moment of inertia of the robot. Simulation results and real results of navigation are provided to demonstrate the performance of the proposed modeling approach.

Localization of Mobile Robots Using an Extended Kalman Filter in a LEGO NXT

The inspiration for this paper comes from a successful experiment conducted with students in the “Mobile Robots” course in the fifth year of the integrated Masters program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots course is “ Localization of Mobile Robots using the Extended Kalman Filter in a LEGO NXT,” which gives the students the opportunity to study the concepts of localization. This experiment comes within the framework of teaching localization concepts in mobile robotics and focuses primarily on explaining the Kalman filter concept. It involves a specific tool developed by the authors and based on LEGO NXT technology. The work presented here could be a helpful guide for teaching concepts related to localization in mobile robotics to ensure adequate understanding of the concept and of the use of the extended Kalman filter (EKF). The LegoFeup robot described here was built using a LEGO Mindstorms NXT and tested both in simulation and in real scenarios. Based on the results obtained, the authors concluded that the developed tool is effective in motivating students. The implementation of the tool, the structure of the Mobile Robots course, and the criteria for student assessment are described in this paper.

Multi-robot nonlinear model predictive formation control: Moving target and target absence

This paper describes a novel approach in formation control for mobile robots in the active target tracking problem. A nonlinear model predictive formation controller (NMPFC) for target perception was implemented to converge a group of mobile robots toward a desired target. The team must also maintain a desired formation following a target while it is moving, or follow a leader in the case of targets absence. The structure details of the controller, as well as a mathematical analysis of the formation model used, are presented. Furthermore, results of simulations and experiments with real robots are presented and discussed.

Concept and Design of the Intellwheels Platform for Developing Intelligent Wheelchairs

Many people with severe disabilities find it difficult or even impossible to use traditional powered wheelchairs independently by manually controlling these electrical devices. Intelligent wheelchairs are a very good solution to assist severely handicapped people who are unable to operate classical electrical wheelchair by themselves in their daily activities. This paper describes a development platform for intelligent wheelchairs called IntellWheels. The intelligent system developed may be added to commercial powered wheelchairs with minimal modifications in a very straightforward manner. The paper describes the concept and design of the platform, including the hardware and software, multimodal input interface and the intelligent wheelchair prototype developed to validate the approach. Preliminary results concerning automatic movement of the IntellWheels prototype are also described showing the autonomous movement capabilities of the prototype.

Real-time and continuous hand gesture spotting: An approach based on artificial neural networks

New and more natural human-robot interfaces are of crucial interest to the evolution of robotics. This paper addresses continuous and real-time hand gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture patterns are recognized by using artificial neural networks (ANNs) specifically adapted to the process of controlling an industrial robot. Since in continuous gesture recognition the communicative gestures appear intermittently with the non-communicative, we are proposing a new architecture with two ANNs in series to recognize both kinds of gesture. A data glove is used as interface technology. Experimental results demonstrated that the proposed solution presents high recognition rates (over 99% for a library of ten gestures and over 96% for a library of thirty gestures), low training and learning time and a good capacity to generalize from particular situations.

IntellWheels MMI: a flexible interface for an intelligent wheelchair

With the rising concern about the needs of people with physical disabilities and with the aging of the population there is a major concern of creating electronic devices that may improve the life of the physically handicapped and elderly person. One of these new solutions passes through the adaptation of electric wheelchairs in order to give them environmental perception, more intelligent capabilities and more adequate Human – Machine Interaction. This paper focuses in the development of a user-friendly multimodal interface, which is integrated in the Intellwheels project. This simple multimodal human-robot interface developed allows the connection of several input modules, enabling the wheelchair control through flexible input sequences of distinct types of inputs (voice, facial expressions, head movements, keyboard and, joystick). The system created is capable of storing user defined associations, of input’s sequences and corresponding output commands. The tests performed have proved the system efficiency and the capabilities of this multimodal interface.

Shared control for obstacle avoidance in intelligent wheelchairs

Intelligent wheelchairs operating in dynamic environments need to sense its neighborhood and adapt the control signal, in real-time, to avoid collisions and protect the user. In this paper we propose a robust, real-time obstacle avoidance extension of the classic potential field methodology. Our algorithm is specially adapted to share the wheelchairs control with the user avoiding risky situations. This method relies on the idea of virtual forces, generated by the user command (attractive force) and by the objects detected on each ultrasonic sensor (repulsive forces), acting on the wheelchair. The resultant wheelchairs behavior is obtained by the sum of the attractive force and all the repulsive forces at a given position. Experimental results from drive tests in a cluttered office environment provided statistical evidence that the proposed algorithm is effective to reduce the number of collisions and still improve the users safety perception.

Perception-driven multi-robot formation control

Maximizing the performance of cooperative perception of a tracked target by a team of mobile robots while maintaining the teams formation is the core problem addressed in this work. We propose a solution by integrating the controller and the estimator modules in a formation control loop. The controller module is a distributed non-linear model predictive controller and the estimator module is based on a particle filter for cooperative target tracking. A formal description of the integration followed by simulation and real robot results on two different teams of homogeneous robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked targets cooperative estimate while complying with the performance criteria such as keeping a pre-set distance between the team-mates and/or the target and obstacle avoidance.

A Nonlinear Model Predictive Control of an Omni-Directional Mobile Robot

This paper presents a nonlinear model based predictive controller (NMPC) for trajectory tracking of a mobile robot. Methods of numerical optimization to perform real time nonlinear minimization of the cost function are used. The cost function penalizes the robot position error, the robot orientation angle error and the control effort. Experimental results of the trajectories following and the performance of the methods of optimization are presented.

A low-cost laser scanning solution for flexible robotic cells: spray coating

In this paper, an adaptive and low-cost robotic coating platform for small production series is presented. This new platform presents a flexible architecture that enables fast/automatic system adaptive behaviour without human intervention. The concept is based on contactless technology, using artificial vision and laser scanning to identify and characterize different workpieces travelling on a conveyor. Using laser triangulation, the workpieces are virtually reconstructed through a simplified cloud of three-dimensional (3D) points. From those reconstructed models, several algorithms are implemented to extract information about workpieces profile (pattern recognition), size, boundary and pose. Such information is then used to on-line adjust the “base” robot programmes. These robot programmes are off-line generated from a 3D computer-aided design model of each different workpiece profile. Finally, the robotic manipulator executes the coating process after its “base” programmes have been adjusted. This is a low-cost and fully autonomous system that allows adapting the robot’s behaviour to different manufacturing situations. It means that the robot is ready to work over any piece at any time, and thus, small production series can be reduced to as much as a one-object series. No skilled workers and large setup times are needed to operate it. Experimental results showed that this solution proved to be efficient and can be applied not only for spray coating purposes but also for many other industrial processes (automatic manipulation, pick-and-place, inspection, etc.).