1,721,028 research outputs found
A sampling-based tree planner for navigation among movable obstacles
No full textThis paper proposes a planner that solves Navigation Among Movable Obstacles problems giving robots the ability to reason about the environment and choose when manipulating obstacles. It finds a path from a robot start configuration S to a goal configuration G taking into consideration the possibility of moving objects if G cannot be reached or if moving objects may significantly shorten the path. The planner combines the A*-Search and the exploration strategy of the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. It is locally optimal and independent from the size of the map and from the number, shape, and position of obstacles. It assumes full world knowledge but it can be easily extended in order to explore unknown environments
Competitions and Industrial Tasks as a Way to Learn Basic Concepts in Robotics
No full textThis paper presents a graduate course project based on a challenging industrial task as a way to learn basic concepts in robotics. The students had to face a simplified version of a task proposed as part of an European Competition. The general aim is to identify an object and place a manipulator in a certain target position with respect to it. Students have to use information provided by a stereo camera in order to guarantee a good pose estimation. The object is not placed in a fixed and predefined spot, but the pose can vary in a range within a maximum of 5 cm in position and 3 degrees in rotation on each direction. The learning objectives of both course and project are introduced and compared with the students' background. We discuss the solutions proposed by the students, together with the amount of time they and their instructors dedicated to solve the task. Answers to a survey have been collected and discussed in order to better evaluate the students' experience
Using Collision Avoidance Algorithms for Designing Multi-robot Emergent Behaviors
No full textWe discuss how to induce a set of collective emergent behaviors into a team of real robots used for soccer robotics. The activation of robot behaviors is organized according to a multi-level control architecture. The emergent cooperative abilities, like exchanging a ball, are achieved through the use of efficient collision avoidance algorithms implemented by a small set of robots able to frequently swap their roles. Our algorithms have been tested on a couple of real robots, Bart and Homer, which played the final game with the middle size league, at RoboCup'99, in Stockholm. This approach can be generalized to allow multi-robot systems to perform various kind of collective tasks in the entertainment field
A learning from demonstration framework for manipulation tasks
No full textThis paper presents a Robot Learning from Demonstration (RLfD) framework for teaching manipulation tasks in an industrial environment: the system is able to learn a task performed by a human demonstrator and reproduce it through a manipulator robot. An RGB-D sensor acquires the scene (human in action); a skeleton tracking algorithm extracts the useful information from the images acquired (positions and orientations of skeleton joints); and this information is given as input to the motion re-targeting system that remaps the skeleton joints into the manipulator ones. After the remapping, a model for the robot motion controller is retrieved by applying first a Gaussian Mixture Model (GMM) and then a Gaussian Mixture Regression (GMR) on the collected data. Two types of controller are modeled: a position controller and a velocity one. The former was presented in [10] inclusive of simulation tests, and here it has been upgraded extended the proves to a real robot. The latter is proposed for the first time in this work and tested both in simulation and with the real robot. Experiments were performed using a Comau Smart5 SiX manipulator robot and let to show a comparison between the two controllers starting from natural human demonstrations
Testing omnidirectional vision-based Monte-Carlo Localization under occlusion
No full textOne of the most challenging issues in mobile robot navigation is the localization problem in densely populated environments. In this paper, we present a new approach for vision-based localization able to solve this problem. The omnidirectional camera is used as a range finder sensitive to the distance of color transitions, whereas classical range finder;, like lasers or sonars, are sensitive to the distance of the nearest obstacles. The well-known Monte-Carlo localization technique was adapted for this new type of range sensor. The system runs in real time on a low-cost pc. In this paper we present experiments, performed in a crowded RoboCup middle-size field, proving the robustness of the approach to the occlusions of the vision sensor by moving obstacles (e.g other robots); occlusions that are very likely to occur in a real environment. Although, the system was implemented for the RoboCup environment, the system can be used in more general environments
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