Abstract

Abstract
A solution for intuitive robot command and fast robot programming is presented to assemble pins in car doors. Static and dynamic gestures are used to instruct an industrial robot in the execution of the assembly task. An artificial neural network (ANN) was used in the recognition of twelve static gestures and a hidden Markov model (HMM) architecture was used in the recognition of ten dynamic gestures. Results of these two architectures are compared with results displayed by a third architecture based on support vector machine (SVM). Results show recognition rates of 96 % and 94 % for static and dynamic gestures when the ANN and HMM architectures are used, respectively. The SVM architecture presents better results achieving recognition rates of 97 % and 96 % for static and dynamic gestures, respectively.

Abstract
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.

Abstract

Abstract
This contribution presents a new system for fast and intuitive industrial robot reprogramming. It is based on a luminous marker built with high-intensity LEDs, which are captured by a set of industrial cameras. Using stereoscopy, the marker supplies 6-DoF human wrist tracking with both position and orientation data. This marker can be efficiently attached to any working tool which then provides a way to capture human skills without further intrusion in the tasks. The acquisition technique makes the tracking very robust against lighting conditions so no environment preparation is needed. The robot is automatically programmed from the demonstrated task which delivers complete abstraction of programming concepts. The system is able to perform in real time, and is low-cost starting with a single pair of industrial cameras though more can be used for improved effectiveness and accuracy. The real-time feature means that the robot is ready to perform as soon as the demonstration is over which carries no overhead of reprogramming times. Also, there is no interference with the task itself since the marker is attached to the work tool and the tracking is contactless; the human operator can then perform naturally. The test bed is a real industrial environment: a spray painting application. A prototype has been developed and installed, and is currently in operation. The tests show that the proposed system enables transferring to the machine the human ability of manipulating a spray gun.

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to the Multi Agent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.