Rui P. Rocha

Cooperative multi-robot systems:: A study of vision-based 3-D mapping using information theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article introduces a distributed architecture, within a probabilistic framework for vision-based 3-D mapping, whereby each robot is committed to cooperate with other robots through information sharing. An entropy-based measure of information utility is defined, which a robot uses for communicating to its teammates the most useful measurements, thus preventing the robot to overwhelm communication resources with redundant information. Experiments with real robots, equipped with stereo-vision, yielded important conclusions about the way robots should cooperate on sharing information.

A novel multi-robot exploration approach based on Particle Swarm Optimization algorithms

This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively named as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biological-inspired techniques to the domain of multi-robot systems, by taking into account obstacle avoidance. These novel algorithms are demonstrated for groups of simulated robots performing a distributed exploration task. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a “punish-reward” mechanism enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment show that biological and sociological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue).

A Survey on Multi-robot Patrolling Algorithms

This article presents a survey on cooperative multi-robot patrolling algorithms, which is a recent field of research. Every strategy proposed in the last decade is distinct and is normally based on operational research methods, simple and classic techniques for agent’s coordination or alternative, and usually more complex, coordination mechanisms like market-based approaches or reinforcement-learning. The variety of approaches differs in various aspects such as agent type and their decision-making or the coordination and communication mechanisms. Considering the current work concerning the patrolling problem with teams of robots, it is felt that there is still a great potential to take a step forward in the knowledge of this field, approaching well-known limitations in previous works that should be overcome.

An evaluation of 2D SLAM techniques available in Robot Operating System

In this work, a study of several laser-based 2D Simultaneous Localization and Mapping (SLAM) techniques available in Robot Operating System (ROS) is conducted. All the approaches have been evaluated and compared in 2D simulations and real world experiments. In order to draw conclusions on the performance of the tested techniques, the experimental results were collected under the same conditions and a generalized performance metric based on the k-nearest neighbors concept was applied. Moreover, the CPU load of each technique is examined. This work provides insight on the weaknesses and strengths of each solution. Such analysis is fundamental to decide which solution to adopt according to the properties of the intended final application.

MSP algorithm: multi-robot patrolling based on territory allocation using balanced graph partitioning

This article addresses the problem of efficient multi-robot patrolling in a known environment. The proposed approach assigns regions to each mobile agent. Every region is represented by a subgraph extracted from the topological representation of the global environment. A new algorithm is proposed in order to deal with the local patrolling task assigned for each robot, named Multilevel Subgraph Patrolling (MSP) Algorithm. It handles some major graph theory classic problems like graph partitioning, Hamilton cycles, non-Hamilton cycles and longest path searches. The flexible, scalable, robust and high performance nature of this approach is testified by simulation results.

Benchmark of swarm robotics distributed techniques in a search task

This paper presents a survey on multi-robot search inspired by swarm intelligence by further classifying and discussing the theoretical advantages and disadvantages of the existing studies. Subsequently, the most attractive techniques are evaluated and compared by highlighting their most relevant features. This is motivated by the gradual growth of swarm robotics solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow for a better avoidance of local solutions but require too much time to find the optimal one. Moreover, such techniques tend to fail in finding targets within dynamic and unstructured environments. This paper presents experiments conducted to benchmark five state-of-the-art algorithms for cooperative exploration tasks. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue). Moreover, the RDPSO is further compared with the best performing algorithms within a population of 14 e-pucks. It is observed that the RDPSO algorithm converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.

A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.

Multi-robot patrolling algorithms: examining performance and scalability

Abstract In this paper the problem of patrolling an environment with a dynamic team of robots is targeted. Lately, the interest of the research community has been focused in the development of patrol strategies; however there is a deficit of studies comparing such strategies, namely in terms of their performance and team scalability in different environments. For this reason, an evaluation of five representative patrol approaches is presented in this article. Aiming to analyze the performance, ability to scale and the behavior resulting from interactions between teammates, extensive realistic simulation using ROS together with Stage was conducted. The metric used to compare the performance is the average idleness of the topological environment (i.e. graph), that represents the area to patrol. The results presented help to identify which strategies enable enhanced team scalability and which are the most suitable approaches given any environment, supporting future research directions in the field.

Ensuring ad hoc connectivity in distributed search with Robotic Darwinian Particle Swarms

This paper presents an enforcing multi-hop network connectivity algorithm experimentally validated using a modified version of the Darwinian Particle Swarm Optimization (DPSO), denoted as RDPSO (Robotic DPSO) on groups of simulated robots performing a distributed exploration task. This work aims to overcome limitations of multi-robot systems (MRS) in difficult scenarios (e.g., search and rescue) concerning the need and the ability to actively maintain an available inter-robot communication channel, through the development of effective multi-robot cooperation without relying on a preexisting communication network. Although there is no linear relationship between the number of robots (i.e., nodes) and the maximum communication range, experimental results show that the decreased performance by the developed algorithm under communication constraints can be overcome by slightly increasing the number of robots as the maximum communication range is decreased.

On the performance and scalability of multi-robot patrolling algorithms

Several distinct multi-robot patrolling strategies have been presented for the last decade in the context of security applications. However, there is a deficit of studies comparing these strategies, namely in terms of their performance and the scalability in the number of robots. For that reason, in this paper, an evaluation of five representative patrolling approaches is presented. This analysis is based on realistic simulation results using ROS and a performance metric represented by the average idleness of the topological environment (i.e., graph) that represents the area to patrol. The results presented help to identify which strategies enable enhanced team scalability and which are the most suitable approaches given any environment.