Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

The RoCKIn Project

The goal of the project “Robot Competitions Kick Innovation in Cognitive Systems and Robotics” (RoCKIn), funded by the European Commission under its 7th Framework Program, has been to speed up the progress toward smarter robots through scientific competitions. Two challenges have been selected for the competitions due to their high relevance and impact on Europe’s societal and industrial needs: domestic service robots (RoCKIn@Home) and innovative robot applications in industry (RoCKIn@Work). The RoCKIn project has taken an approach to boosting scientific robot competitions in Europe by (i) specifying and designing open domain test beds for competitions targeting the two challenges; (ii) developing methods for scoring and benchmarking that allow to assess both particular subsystems as well as the integrated system; and (iii) organizing camps to build up a community of new teams, interested to participate in robot competitions. A significant number of dissemination activities on the relevance of robot competitions were carried out to promote research and education in robotics, as to researchers and lay citizens. The lessons learned during RoCKIn paved the way for a step forward in the organization and research impact of robot competitions, contributing for Europe to become a world leader in robotics research, education, and technology transfer

Robot Formations Control Using Fast Marching

This paper presents the application of the Voronoi Fast Marching (V FM) method to the Control of Robot Formations. The V FM method uses the propagation of a wave (Fast Marching) operating on the world model to de- termine a motion plan over a viscosity map (similar to the refraction index in optics) extracted from the updated map model. The computational effciency of the method allows the planner to operate at high rate sensor frequencies. This method allows us to maintain good response time and smooth and safe planned trajectories. The navigation function can be classiffed as a type of potential field, but it has no local minima, it is complete (it finds the solu- tion path if it exists) and it has a complexity of order n (O(n)), where n is the number of cells in the environment map. The results presented in this paper show how the proposed method behaves with mobile robot formations and generates trajectories of good quality without problems of local minima when the formation encounters non-convex obstacles

DeepThought: An Architecture for Autonomous Self-motivated Systems

The ability of large language models (LLMs) to engage in credible dialogues with humans, taking into account the training data and the context of the conversation, has raised discussions about their ability to exhibit intrinsic motivations, agency, or even some degree of consciousness. We argue that the internal architecture of LLMs and their finite and volatile state cannot support any of these properties. By combining insights from complementary learning systems, global neuronal workspace, and attention schema theories, we propose to integrate LLMs and other deep learning systems into an architecture for cognitive language agents able to exhibit properties akin to agency, self-motivation, even some features of meta-cognition

Robot formation motion planning using Fast Marching

This paper presents the application of the Voronoi Fast Marching (VFM) method to path planning of mobile formation robots. The VFM method uses the propagation of a wave (Fast Marching) operating on the world model to determine a motion plan over a viscosity map (similar to the refraction index in optics) extracted from the updated map model. The computational efficiency of the method allows the planner to operate at high rate sensor frequencies. This method allows us to maintain good response time and smooth and safe planned trajectories. The navigation function can be classified as a type of potential field, but it has no local minima, it is complete (it finds the solution path if it exists) and it has a complexity of order n(O(n)), where n is the number of cells in the environment map. The results presented in this paper show how the proposed method behaves with mobile robot formations and generates trajectories of good quality without problems of local minima when the formation encounters non-convex obstacles.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid.Publicad

RoCKIn@Home: Domestic Robots Challenge

Service robots performing complex tasks involving people in houses or public environments are becoming more and more common, and there is a huge interest from both the research and the industrial point of view. The RoCKIn@Home challenge has been designed to compare and evaluate different approaches and solutions to tasks related to the development of domestic and service robots. RoCKIn@Home competitions have been designed and executed according to the benchmarking methodology developed during the project and received very positive feedbacks from the participating teams. Tasks and functionality benchmarks are explained in detail

Vision-Based Autonomous Following of a Moving Platform and Landing for an Unmanned Aerial Vehicle

Interest in Unmanned Aerial Vehicles (UAVs) has increased due to their versatility and variety of applications, however their battery life limits their applications. Heterogeneous multi-robot systems can offer a solution to this limitation, by allowing an Unmanned Ground Vehicle (UGV) to serve as a recharging station for the aerial one. Moreover, cooperation between aerial and terrestrial robots allows them to overcome other individual limitations, such as communication link coverage or accessibility, and to solve highly complex tasks, e.g., environment exploration, infrastructure inspection or search and rescue. This work proposes a vision-based approach that enables an aerial robot to autonomously detect, follow, and land on a mobile ground platform. For this purpose, ArUcO fiducial markers are used to estimate the relative pose between the UAV and UGV by processing RGB images provided by a monocular camera on board the UAV. The pose estimation is fed to a trajectory planner and four decoupled controllers to generate speed set-points relative to the UAV. Using a cascade loop strategy, these set-points are then sent to the UAV autopilot for inner loop control. The proposed solution has been tested both in simulation, with a digital twin of a solar farm using ROS, Gazebo and Ardupilot Software-in-the-Loop (SiL); and in the real world at IST Lisbon’s outdoor facilities, with a UAV built on the basis of a DJ550 Hexacopter and a modified Jackal ground robot from DJI and Clearpath Robotics, respectively. Pose estimation, trajectory planning and speed set-point are computed on board the UAV, using a Single Board Computer (SBC) running Ubuntu and ROS, without the need for external infrastructure.This research was funded by the ISR/LARSyS Strategic Funding through the FCT project UIDB/50009/2020, the DURABLE project, under the Interreg Atlantic Area Programme through the European Regional Development Fund (ERDF), the Andalusian project UMA18-FEDERJA-090 and the University of Málaga Research Plan. Partial funding for open access charge: Universidad de Málag

RoCKIn Benchmarking and Scoring System

The main innovation brought forth by the European Project RoCKIn is the definition, implementation and application to an actual robot competition of the novel paradigm of benchmarking through competitions. By doing so, RoCKIn set in motion an evolutionary process to transform robot competitions from successful showcases with limited scientific impact into benchmarking tools for the consistent and objective evaluation of the performance of autonomous robot systems. Our work began by revisiting, in the light of the features and limitations of a competition setting, the very foundations of the scientific method; then we built on these by designing a novel type of competitions where the concepts of benchmark and objective performance metrics are the key points; finally, we arrived to the implementation of such concepts in the form of a real-world robot competition. This chapter describes the above process, explaining how each of its several aspects (theoretical, technical, procedural) has been tackled by RoCKIn. Special attention will be devoted to the problems of defining performance metrics and of capturing the ground truth needed to reliably assess robot perceptions and actions