The use of robots as educational tools provides a stimulating environment for students. Some robotics competitions focus on primary and secondary school aged children, and serve as motivation for students to get involved in educational robotics activities. Although very appealing, many students cannot participate on robotics competitions because they cannot afford robotics kits. Hence, several students have no access to educational robotics, especially on developing countries. To minimize this problem and contribute to education equality, we have created RoSoS Robot Soccer Simulator, in which students program virtual robots in a similar way that they would program their real ones. In this chapter we explain some technical details of RoSoS and discuss the implementation of a new league for the robotics competitions: Junior Soccer Simulation league (JSS). Because soccer is the most popular sport in the world, we believe JSS will be a strong motivator for students to get involved with robotics.
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The real-time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch-based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of an interaction field (IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real-time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game-like scenarios where the crowd responds to a user-controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non-expert users to easily enrich any agent-based crowd simulation with new agent interactions.
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Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.
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The percentage of sports and leisure shoes sold worldwide is gradually increasing. However, consumers have little or no objective information on the mechanical properties of the shoes. A justified selection protocol of sports and leisure shoes based on static and dynamic shoe properties considering the intended use is essential. Today, commonly accepted dynamic test protocols for (sports) shoes do not exist. The development of an artificial parametric foot as part of an innovative robot gait simulator is a tool to objectify shoe properties independently from possible compensations encountered during assessment of test persons. This contribution discusses the development of an artificial foot enabling objective testing of the mechanical and functional properties of sports and leisure shoes.
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Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.
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An important issue in the field of motion control of wheeled mobile robots is that the design of most controllers is based only on the robot’s kinematics. However, when high-speed movements and/or heavy load transportation are required, it becomes essential to consider the robot dynamics as well. The control signals generated by most dynamic controllers reported in the literature are torques or voltages for the robot motors, while commercial robots usually accept velocity commands. In this context, we present a velocity-based dynamic model for differential drive mobile robots that also includes the dynamics of the robot actuators. Such model has linear and angular velocities as inputs and has been included in Peter Corke’s Robotics Toolbox for MATLAB, therefore it can be easily integrated into simulation systems that have been built for the unicycle kinematics. We demonstrate that the proposed dynamic model has useful mathematical properties. We also present an application of such model on the design of an adaptive dynamic controller and the stability analysis of the complete system, while applying the proposed model properties. Finally, we show some simulation and experimental results and discuss the advantages and limitations of the proposed model.
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This paper presents a multi-layer scheme to control a formation of three mobile robots. Each layer works as an independent module, dealing with a specific part of the problem of formation control, thus giving to the system more flexibility. In order to reduce formation errors, the proposed architecture includes a layer which performs an adaptive dynamic compensation, using a robust updating law, which compensates for each robot dynamics. The controller is able to guide the robots to the desired formation, including the possibility of time-varying position and/or shape. Stability analysis is performed for the closed-loop system, and the result is that the formation errors are ultimately bounded. Finally, simulation results for a group of three unicycle-like mobile robots are presented, which show that system performance is improved when the adaptive dynamic compensation layer is included in the formation control scheme. © 2009 IEEE.
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The use of robots as educational tools provide a stimulating environment for students. Some robotics competitions focus on primary and secondary school aged children, and serve as a motivation factor for students to get involved in educational robotics activities. But, in most competitions students are required to deal with robot design, construction and programming. Although very appealing, many students cannot participate on robotics competitions because they cannot afford robotics kits and their school do not have the necessary equipment. Because of that, several students have no access to educational robotics, especially on developing countries. To minimize this problem and contribute to education equality, we present a proposal for a new league for the robotics competitions: The Junior Soccer Simulation league (JSS). In such a league, students program virtual robots in a similar way that they would program their real ones. Because there is no hardware involved, costs are very low and participants can concentrate on software development and robot's intelligence improvement. Finally, because soccer is the most popular sport in the world, we believe JSS will be a strong motivator for students to get involved with robotics. In this paper we present the simulator that was developed (ROSOS) and discuss some ideas for the adoption of a Junior Soccer Simulation competition.
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