David Portugal

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.

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.

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.

Computation Sharing in Distributed Robotic Systems: A Case Study on SLAM

Aiming at increasing team efficiency, mobile robots may act as a node of a Robotic Cluster to assist their teammates in computationally demanding tasks. Having this in mind, we propose two distributed architectures for the Simultaneous Localization And Mapping (SLAM) problem, our main case study. The analysis focuses especially on the efficiency gain that can be obtained. It is shown that the proposed architectures enable us to raise the workload up to values that would not be possible in a single robot solution, thus gaining in localization precision and map accuracy. Furthermore, we assess the impact of network bandwidth. All the results are extracted from frequently used SLAM datasets available in the robotics community and a real world testbed is described to show the potential of using the proposed philosophy.

A collective robotic architecture in search and rescue scenarios

Multi-robot systems (MRS) may be very useful on assisting humans in many distributed activities, especially for search and rescue (SaR) missions in hazardous scenarios. However, there is a lack of full distributed solutions, addressing the advantages and limitations along different aspects of team operation, like communication requirements or scalability. In this paper, the effects of distributed group configurations are studied and results are drawn from collective exploration and collective inspection tasks in SaR scenarios, with the aim of understanding the main tradeoffs, and distilling design guidelines of collective architectures. With this purpose, extensive simulation experiments of MRS in a SaR scenario were carried out.

Finding Optimal Routes for Multi-Robot Patrolling in Generic Graphs

Multi-robot patrolling is a problem that has important applications in security and surveillance. However, the optimal task assignment is known to be NP-hard. We consider evenly spacing the robots in a cyclic Traveling Salesman Problem (TSP) tour or partitioning the graph of the environment. The trade-off in performance, overall team travel cost and coordination is analyzed in this paper. We provide both a theoretical analysis and simulation results across multiple environments. The results demonstrate that generally cyclic-based strategies are superior, especially when small teams are used but at the expense of greater team cost, whereas partitioning strategies are especially suitable for larger teams and unbalanced graph topologies. The reported results show that graph topology and team size are fundamental to determine the best choice for a patrol strategy.

Decision methods for distributed multi-robot patrol

In the Multi-Robot Patrolling Problem, agents have to continuously decide which place to move next, after clearing their current location. This article proposes a distributed solution to the problem based on Bayesian decision. The system is modeled according to its current state, thus automating the local decision-making process in order to effectively patrol an area. Two strategies are presented and compared. In the first one, robots are self-interested and aim to maximize their local gain. The second strategy is more complex, taking into account gains as well as the distribution of agents in the space to reduce interference and foster scalability. In order to validate the proposed solution, realistic simulations, comparing with five state-of-the-art approaches, as well as experiments with physical multi-robot systems were conducted.

The CHOPIN project: Cooperation between human and rObotic teams in catastrophic incidents

Mobile robots can be an invaluable aid to human first responders (FRs) in catastrophic incidents, as they are expendable and can be used to reduce human exposure to risk in search and rescue (SaR) missions, as well as attaining a more effective response. Moreover, parallelism and robustness yielded by multi-robot systems (MRS) may be very useful in this kind of spatially distributed tasks, which are distributed in space, providing augmented situation awareness (SA). However, this requires adequate cooperative behaviors, both within MRS teams and between human and robotic teams. Collaborative context awareness between both teams is crucial to assess information utility, efficiently share information and build a common and consistent SA. This paper presents the foreseen research within the CHOPIN research project, which aims to address these scientific challenges and provide a proof of concept for the cooperation between human and robotic teams in SaR scenarios.