Decision-Theoretic Planning with Linguistic Terms in GOLOG

Abstract In this paper we propose an extension of the action language GOLOG that integrates linguistic terms in non-deterministic argument choices and the reward function for decision-theoretic planning. It is often cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, specifying a reward function is not always easy, even for domain experts. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in GOLOG, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy fluents. In GOLOG's forwardsearch DT planning algorithm, these formulas are evaluated in order to find the agent's optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order

A System for Continuous Underground Site Mapping and Exploration

3D mapping becomes ever more important not only in industrial mobile robotic applications for AGV and production vehicles but also for search and rescue scenarios. In this chapter we report on our work of mapping and exploring underground mines. Our contribution is two-fold: First, we present our custom-built 3D laser range platform SWAP and compare it against an architectural laser scanner. The advantages are that the mapping vehicle can scan in a continuous mode and does not have to do stop-and-go scanning. The second contribution is the mapping tool mapit which supports and automates the registration of large sets of point clouds. The idea behind mapit is to keep the raw point cloud data as a basis for any map generation and only store all operations executed on the point clouds. This way the initial data do not get lost, and improvements on low-level date (e.g. improved transforms through loop closure) will automatically improve the final maps. Finally, we also present methods for visualization and interactive exploration of such maps

Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation

In this chapter, we report on our activities to create and maintain a fleet of autonomous load haul dump (LHD) vehicles for mining operations. The ever increasing demand for sustainable solutions and economic pressure causes innovation in the mining industry just like in any other branch. In this chapter, we present our approach to create a fleet of autonomous special purpose vehicles and to control these vehicles in mining operations. After an initial exploration of the site we deploy the fleet. Every vehicle is running an instance of our ROS 2-based architecture. The fleet is then controlled with a dedicated planning module. We also use continuous environment monitoring to implement a life-long mapping approach. In our experiments, we show that a combination of synthetic, augmented and real training data improves our classifier based on the deep learning network Yolo v5 to detect our vehicles, persons and navigation beacons. The classifier was successfully installed on the NVidia AGX-Drive platform, so that the abovementioned objects can be recognised during the dumper drive. The 3D poses of the detected beacons are assigned to lanelets and transferred to an existing map

Importancia de la aplicación de retos matemáticos para el desarrollo del pensamiento matemático en estudiantes de secundaria

El presente trabajo plasma avances de una investigación en proceso, cuyo objetivo es potencializar el pensamiento matemático mediante la aplicación de retos matemáticos a estudiantes de secundaria e identificar su nivel de comprensión lectora, para coadyuvar al desempeño académico. La metodología es de corte cuantitativo, se utilizarán dos instrumentos, cuestionario y pruebas de comprensión lectora. Este estudio contribuye al beneficio de los alumnos, porque consiste en desarrollar las competencias matemáticas, habilidades y mejorar su desempeño académico, lo cual produce un impacto positivo en la sociedad, al formar jóvenes capaces de enfrentarse a los problemas de la vida

Fear Learning for Flexible Decision Making in RoboCup: A Discussion

In this paper, we address the stagnation of RoboCup com- petitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of research, by allowing robot players to learn the behaviour profile of the opponent team at runtime. Later, players can use this knowledge to predict when an undesirable outcome is imminent, thus having the chance to act towards preventing it. We discuss specific scenarios where SAFEL’s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation