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.

Abstract
Object tracking in a moving frame is becoming a common requirement in a lot of mobile robotic applications, such as search and rescue, monitoring and surveillance, and even in some scientific applications, such as robotic soccer. In all these applications, the robots must be capable of estimating the target position and, sometimes, velocity on their own. Depending on the application and on the current scene situation, the estimates must be more or less accurate, depending on the robot intention to interact with the target, whether to catch it, follow it, etc. The problem is that a robot moves along the working area, having some uncertainty in its pose estimation. This paper proposes an approach based on a Kalman Filter to estimate the object position and velocity, regardless the robot pose. As a testbed, a Middle-Size League soccer robot tracking a moving ball example will be used. This approach allows the agent to follow and interact with a moving object, even if its localization is not available. © Springer International Publishing AG 2018.

Abstract

Abstract
Supply chains are complex interdependent structures in which tasks' accomplishment is the result of a compromise between all the entities involved. This complexity is particularly pronounced when dealing with chipping and transportation tasks within a forest-based biomass energy production supply chain. The logistic costs involved are significant and the number of network nodes are usually in a considerable number. For this reason, efficient optimization tools should be used in order to derive cost effective scheduling. In this work, soft computing optimization tools, namely genetic algorithms (GA) and particle swarm optimization (PSO), are integrated within a discrete event simulation model to define the vehicles operational schedule in a typical forest biomass supply chain. The presented simulation results show the proposed methodology effectiveness in dealing with the addressed systems.

Abstract