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

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System

The location determination in obstructed area can be very challenging especially if Global Positioning System are blocked. Users will find it difficult to navigate directly on-site in such condition, especially indoor car park lot or obstructed environment. Sometimes, it needs to combine with other sensors and positioning methods in order to determine the location with more intelligent, reliable and ubiquity. By using ubiquitous positioning in mobile navigation system, it is a promising ubiquitous location technique in a mobile phone since as it is a familiar personal electronic device for many people. However, as research on ubiquitous positioning systems goes beyond basic methods there is an increasing need for better comparison of proposed ubiquitous positioning systems. System developers are also lacking of good frameworks for understanding different options during building ubiquitous positioning systems. This paper proposes taxonomy to address both of these problems. The proposed taxonomy has been constructed from a literature study of papers and articles on positioning estimation that can be used to determine location everywhere on mobile navigation system. For researchers the taxonomy can also be used as an aid for scoping out future research in the area of ubiquitous positioning.Comment: 15 Pages, 3 figure

Cooperative Carrying Control for Mobile Robots in Indoor Scenario

openIn recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions. In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints. Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage.In recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions. In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints. Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment

A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue

The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.</p