Experiments in the coordination of large groups of robots

The use of large groups of robots, generally called swarms, has gained increased attention in recent years. In this paper, we present and experimentally validate an algorithm that allows a swarm of robots to navigate in an environment containing unknown obstacles. A coordination mechanism based on dynamic role assignment and local communication is used to help robots that may get stuck in regions of local minima. Experiments were performed using both a realistic simulator and a group of real robots and the obtained results showed the feasibility of the proposed approach

Accomplishing adaptability in simulation frameworks: the bubble approach

Enforcing framework adaptability is one of the key points in the process of building an object-oriented application framework. When it comes to simulation, some adaptation mechanisms to configure components on-the-fly are usually required in order to produce good software artifacts and alleviate development effort. The paper reports an experience using a simulation multi-agent framework, initially conceived to be used in fluid flow problems. The framework architecture demonstrated during its evolution a great potential regarding to flexibility and modularity, tackling a wide range of other problems ranging from a network protocol simulation to a soccer simulationI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Robot Soccer Strategy Based on Hierarchical Finite State Machine to Centralized Architectures

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] Coordination among the robots allows a robot soccer team to perform better through coordinated behaviors. This requires that team strategy is designed in line with the conditions of the game. This paper presents the architecture for robot soccer team coordination, involving the dynamic assignment of roles among the players. This strategy is divided into tactics, which are selected by a Hierarchical State Machine. Once a tactic has been selected, it is assigned roles to players, depending on the game conditions. Each role performs defined behaviors selected by the Hierarchical State Machine. To carry out the behaviors, robots are controlled by the lowest level of the Hierarchical State Machine. The architecture proposed is designed for robot soccer teams with a central decision-making body, with global perception. 200 games were performed against a team with constant roles, winning the 92.5% of the games, scoring more goals on average that the opponent, and showing a higher percent of ball possession. Student s t-test shows better matching with measurement uncertainty of the strategy proposed. This architecture allowed an intuitive design of the robot soccer strategy, facilitating the design of the rules for role selection and behaviors performed by the players, depending on the game conditions. Collaborative behaviors and uniformity within the players behaviors during the tactics and behaviors transitions were observedJose Guillermo Guarnizo ha sido financiado por una beca del Departamento Administrativo de Ciencia, Tecnología e Innovación COLCIENCIAS, Colombia.Guarnizo, JG.; Mellado Arteche, M. (2016). Robot Soccer Strategy Based on Hierarchical Finite State Machine to Centralized Architectures. IEEE Latin America Transactions. 14(8):3586-3596. doi:10.1109/TLA.2016.7786338S3586359614

Multi-robot task allocation for safe planning under dynamic uncertainties

This paper considers the problem of multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control policy is challenging not only because of the complexity of incorporating dynamic uncertainties while planning, but also because of the exponential growth in problem size as a function of the number of robots. Leveraging recent works obtaining a tractable safety maximizing plan for a single robot, we propose a scalable two-stage framework to solve the problem at hand. Specifically, the problem is split into a low-level single-agent planning problem and a high-level task allocation problem. The low-level problem uses an efficient approximation of stochastic reachability for a Markov decision process to handle the dynamic uncertainty. The task allocation, on the other hand, is solved using polynomial-time forward and reverse greedy heuristics. The safety objective of our multi-robot safe planning problem allows an implementation of the greedy heuristics through a distributed auction-based approach. Moreover, by leveraging the properties of the safety objective function, we ensure provable performance bounds on the safety of the approximate solutions proposed by these two heuristics. Our result is illustrated through case studies