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Self-organized graph partitioning approach for multi-agent patrolling in generic graphs
... Sugiyama et al. [13] proposed a negotiation-based learning method to address the multi-robots patrol problem. Wiandt et al. [14] proposed a self-organized partition algorithm to achieve distributed partition, in which robots transmit information by broadcasting to other robots without a third-party coordinator. Portugal et al. [1] thought that the local patrolling path in graph partitions could be improved by minimizing the longest local path. ...
... The constraint in Equation (13) guarantees that the number of arrivals and departures at a target are the same. Moreover, Equation (14) guarantees that all the targets are visited with the required number of visits. ...
... Then, we generate a new solution for the area patrol problem from the three feasible sets above. We generate a velocity set A by velocity cut for the updated velocity V i with a probability Pr v and shuffle the elements in A to get a feasible velocity F V , which meets the constraint set Ω cs containing the constraints in Equations (10)- (14). The purpose of probabilistic constraints Pr v d scrambling operations randperm is to increase the diversity of velocities. ...
Multi-robot cooperative patrolling systems have been extensively employed in the civilian and military fields, including monitoring forest fires, marine search-and-rescue, and area patrol. Multi-robot area patrol problems refer to the activity that a team of robots works cooperatively and regularly to visit the key targets in the given area for security. Following consideration of the low cost and high safety of unmanned surface vehicles (USV), a team of USVs is organized to perform area patrol in a sophisticated maritime environment. In this paper, we establish a mathematical model considering the characteristics of the cooperative patrol task and the limited conditions of USVs. A hybrid partition-based patrolling scheme is proposed for a multi-USV system to visit targets with different importance levels in a maritime area. Firstly, a centralized area partition algorithm is utilized to partition the patrolling area according to the number of USVs. Secondly, a distributed path planning algorithm is applied to planning the patrolling path for each USV to visit the targets in a maritime environment to minimize the length of the patrolling path for the USV team. Finally, comparative experiments between the proposed scheme and other methods are carried out to validate the performance of the hybrid partition-based patrolling scheme. Simulation results and experimental analysis show the efficiency of the proposed hybrid partition-based patrolling scheme compared to several previous patrolling algorithms.
... In this article our goal is to further elaborate the performance characteristics of our proposed PBPS algorithm [18]. In our model the only global knowledge the agents have is the map they are patrolling on. ...
... PBPS was published in [18] along with the first results obtained on well-known graphs. Here we give only a short introduction to the algorithm. ...
... We have elaborated on the performance characteristics of the PBPS algorithm first published in [18]. The result of the partitioning algorithm is a set of subgraphs formed by the individual agents cooperating with each other that is used by patrolling agents as their patrolling tasks. ...
... In this work, it is assumed that the environment is known and the proposed method assigns exclusive regions to each agent to ensure that work redundancy is reduced and collision between the operating robots is eliminated. In [33], a distributed self-organized graph partitioning approach was proposed that can partition a graph into nonoverlapping subgraphs without the presence of a centralized entity. e proposed self-organized autonomous algorithm required less synchronization and only local information since it required no central entity. ...
In this paper, we propose a dynamic territorializing approach for the problem of distributing tasks among a group of robots. We consider the scenario in which a task comprises two subtasks—detection and completion; two complementary teams of agents, hunters and gatherers, are assigned for the subtasks. Hunters are assigned with the task of exploring the environment, i.e., detection, whereas gatherers are assigned with the latter subtask. To minimize the workload among the gatherers, the proposed algorithm utilizes the center of mass of the known targets to form territories among the gatherers. The concept of center of mass has been adopted because it simplifies the task of territorial optimization and allows the system to dynamically adapt to changes in the environment by adjusting the assigned partitions as more targets are discovered. In addition, we present a game-theoretic analysis to justify the agents’ reasoning mechanism to stay within their territory while completing the tasks. Moreover, simulation results are presented to analyze the performance of the proposed algorithm. First, we investigate how the performance of the proposed algorithm varies as the frequency of territorializing is varied. Then, we examine how the density of the tasks affects the performance of the algorithm. Finally, the effectiveness of the proposed algorithm is verified by comparing its performance against an alternative approach.
Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing, which involves repeated visits by a group of agents to specified points in an environment. While the problem is well-studied, most works do not provide performance guarantees and either do not consider agent attrition or impose significant communication requirements to enable adaptation. In this work, we present the Adaptive Heuristic-based Patrolling Algorithm, which is capable of adaptation to agent loss using minimal communication by taking advantage of Voronoi partitioning, and which meets guaranteed performance bounds. Additionally, we provide new centralized and distributed mathematical programming formulations of the patrolling problem, analyze the properties of Voronoi partitioning, and finally, show the value of our adaptive heuristic algorithm by comparison with various benchmark algorithms using physical robots and simulation based on the Robot Operating System (ROS)2.
Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing which involves repeated visits by a group of agents to specified points in an environment. While the problem is well-studied, most works either do not consider agent attrition or impose significant communication requirements to enable adaptation. In this work, we present the Adaptive Heuristic-based Patrolling Algorithm, which is capable of adaptation to agent loss using minimal communication by taking advantage of Voronoi partitioning. Additionally, we provide new centralized and distributed mathematical programming formulations of the patrolling problem, analyze the properties of Voronoi partitioning, and show the value of our adaptive heuristic algorithm by comparison with various benchmark algorithms using a realistic simulation environment based on the Robot Operating System (ROS) 2.
This article presents the first steps toward an automatic indoor environmental monitoring system through the use of a group of mobile robots. A metric map is provided a priori to the robots, ensuring the navigation, localization and extraction of points of interests for patrolling. The communica-tion layer is robust and allows any robot to enter or leave the patrolling task. The monitoring of the environment is achieved by acquiring environmental information during the patrolling. If an abnormal condition is detected, the system should react, providing a fine coverage of the suspicious area and an accurate identification of its source. Experiments were conducted inside a building and data was gathered and post processed. The yet preliminary results demonstrate the effectiveness of the method employed to control the system, showing good area coverage from the patrolling algorithm and acceptable representations of the collected variables (alcohol concentration and temperature).
This paper compares two algorithms to solve a multiagent patrol problem with uncertain durations. The first algorithm is reactive and allows adaptive and robust behavior, while the second one uses planning to maximize longterm information retrieval. Experiments suggest that on the considered instances, using a reactive and local coordination algorithm performs almost as well as planning for long-term, while using much less computation time.
A group of agents can be used to perform patrolling tasks in a variety of domains ranging from computer network administration to computer wargame simulations. Despite its wide range of potential applications, multi- agent architectures for patrolling have not been studied in depth yet. First state of the art approaches used to deal with related problems cannot be easily adapted to the patrolling task specificity. Second, the existing patrolling- specific approaches are still in preliminary stages. In this paper, we present an original in-depth discussion of multi-agent patrolling task issues, as well as an empirical evaluation of possible solutions. In order to accomplish this study we have proposed different architectures of multi-agent systems, various evaluation criteria, two experimental scenarios, and we have implemented a patrolling simulator. The results show which kind of architecture can patrol an area more adequately according to the circumstances.
Patrolling is a complex multi-agent task, which usually requires agents to coordinate their decision-making in order to achieve optimal performance of the group as a whole. In previous work, many patrolling strategies have been developed, based on different approaches: heuristic agents, negotiation mechanisms, reinforcement learning techniques, techniques based on graph-theory and others. In this paper, we complement these studies by comparing all the approaches developed so far for this domain, using patrolling problem instances that were not dealt with before (i.e. new map topologies). The final results constitute a valuable benchmark for this domain, as well as a survey of the strategies developed so far for this task.
A group of agents can be used to perform patrolling tasks in a variety of domains ranging from computer network administration to computer wargame simulations. The multi-agent patrolling problem has recently received growing attention from the multi-agent community, due to the wide range of potential applications. Many algorithms based on reactive and cognitive architectures have been developed, giving encouraging results. However, no theoretical analysis of this problem has been conducted. In this paper, various classes of patrolling strategies are proposed and compared. More precisely, these classes are compared to the optimal strategy by means of a standard complexity analysis.
Multi-robot patrol (MRP) is essentially a collective decision-making problem, where mobile robotic units must coordinate their actions effectively in order to schedule visits to every critical point of the environment. The problem is commonly addressed using centralized planners with global knowledge and/or calculating a priori routes for all robots before the beginning of the mission. However, distributed strategies for MRP have very interesting advantages, such as allowing the team to adapt to changes in the system, the possibility to add or remove patrol units during the mission, and leading to trajectories that are much harder to predict by an external observer. In this work, we present a distributed strategy to solve the patrolling problem in a real world indoor environment, where each autonomous agent decides its actions locally and adapts to the system’s needs using distributed communication. Experimental results show the ability of the team to coordinate so as to visit every important point of the environment. Furthermore, the approach is able to scale to an arbitrary number of robots as well as overcome communication failures and robot faults.
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.
Seminal paper on algebraic connectivity of a network
Multi-agent systems can be used to perform patrolling tasks in various domains. In this work, we compare the results obtained
by new negotiation based approaches with previous ones. By splitting the nodes of the world graph, the negotiator agents reduce
the path they have to walk and the number of nodes to patrol, making it easier to maintain a low average idleness in world
nodes. Auctions are the negotiation mechanisms used to split the nodes of the world, the agents bid on nodes based in their
utility function. Empirical evaluation has shown the effectiveness of this distributed approach, as the results obtained are
substantially better than those previously achieved by negotiator agents. The agent types presented in this work are more
scalable and reactive since they can perform patrolling in worlds of all sizes and topology types. Besides, they are more
stable as indicated by the low standard deviation obtained in node idleness.
This research focuses on how a multi-robot system can work cooperatively to complete patrol missions. The planning and assignment of patrol points to the robots are the major issues to tackle with. A cooperative auction system (CAS) is proposed to solve the problem of patrol planning. Here, each mobile robot picks its own patrol points via the cooperative auction system and the system will continuously re-auction, based on the team work performance, to further improve the performance of cooperation for a large scale multi-robot system. From the experimental results, the proposed approach demonstrates several advantages, such as less time complexity, lower routing path cost, better workload balancing among robots.
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.
Cooperative multi-robot patrol in an indoor infrastructure
- David Portugal
- P Rui
- Rocha
David Portugal and Rui P Rocha. Cooperative multi-robot patrol in an
indoor infrastructure. In Human Behavior Understanding in Networked
Sensing, pages 339-358. Springer, 2014.