Marin Lujak

A distributed algorithm for the multi-robot task allocation problem

In this work we address the Multi-Robot Task Allocation Problem (MRTA). We assume that the decision making environment is decentralized with as many decision makers (agents) as the robots in the system. To solve this problem, we developed a distributed version of the Hungarian Method for the assignment problem. The robots autonomously perform different substeps of the Hungarian algorithm on the base of the individual and the information received through the messages from the other robots in the system. It is assumed that each robot agent has an information regarding its distance from the targets in the environment. The inter-robot communication is performed over a connected dynamic communication network and the solution to the assignment problem is reached without any common coordinator or a shared memory of the system. The algorithm comes up with a global optimum solution in O(n3) cumulative time (O(n2) for each robot), with O(n3) number of messages exchanged among the n robots.

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.

Route guidance: bridging system and user optimization in traffic assignment

Abstract In this paper we study the problem of the assignment of road paths to vehicles. Due to the assumption that a low percentage of vehicles follow the routes proposed by route guidance systems (RGS) and the increase of the use of the same, the conventional RGS might shortly result obsolete. Assuming a complete road network information at the disposal of RGSs, their proposed paths are related with user optimization which in general can be arbitrarily more costly than the system optimum. However, the user optimum is fair for the drivers of the same Origin–Destination (O–D) pair but it does not guarantee fairness for different O–D pairs. Contrary, the system optimum can produce unfair assignments both for the vehicles of the same as of different O–D pairs. This is the reason why, in this paper, we propose an optimization model which bridges this gap between the user and system optimum, and propose a new mathematical programming formulation based on Nash Welfare optimization which results in a good egalitarian and utilitarian welfare for all O–D pairs. To avoid the issues with the lack of robustness related with the centralized implementation, the proposed model is highly distributed. We test the solution approach through simulation and compare it with the conventional user- and system-optimization.

Service discovery acceleration with hierarchical clustering

This paper presents an efficient Web Service Discovery approach based on hierarchical clustering. Conventional web service discovery approaches usually organize the service repository in a list manner, therefore service matchmaking is performed with linear complexity. In this work, services in a repository are clustered using hierarchical clustering algorithms with a distance measure from an attached matchmaker. Service discovery is then performed over the resulting dendrogram (binary tree). In comparison with conventional approaches that mostly perform exhaustive search, we show that service-clustering method brings a dramatic improvement on time complexity with an acceptable loss in precision.

Dynamic Coordination in Fleet Management Systems: Toward Smart Cyber Fleets

Fleet management systems are commonly used to coordinate mobility and delivery services in a broad variety of domains. However, their traditional top-down control architecture becomes a bottleneck in open and dynamic environments, where scalability, proactiveness, and autonomy are becoming key factors for their success. Here, the authors present an abstract event-based architecture for fleet management systems that supports tailoring dynamic control regimes for coordinating fleet vehicles, and illustrate it for the case of medical emergency management. Then, they go one step ahead in the transition toward automatic or driverless fleets, by conceiving fleet management systems in terms of cyber-physical systems, and putting forward the notion of cyber fleets.

On the communication range in auction-based multi-agent target assignment

In this paper, we consider a decentralized approach to the multi-agent target assignment problem and explore the deterioration of the quality of assignment solution in respect to the decrease of the quantity of the information exchanged among communicating agents and their communication range when the latter is not sufficient to maintain the connected communication graph. The assignment is achieved through a dynamic iterative auction algorithm in which agents (robots) assign the targets and move towards them in each period. In the case of static targets and connected communication graph, the algorithm results in an optimal assignment solution. The assignment results are compared with two benchmark cases: a centralized one in which all the global information is known and therefore, the optimal assignment can be found, and the greedy one in which each agent moves towards the target with the highest benefit without communication with any other agent.

Towards Smart Open Dynamic Fleets

Nowadays, vehicles of modern fleets are endowed with advanced devices that allow the operators of a control center to have global knowledge about fleet status, including existing incidents. Fleet management systems support real-time decision making at the control center so as to maximize fleet performance. In this paper, setting out from our experience in dynamic coordination of fleet management systems, we focus on fleets that are open, dynamic and highly autonomous. Furthermore, we propose how to cope with the scalability problem as the number of vehicles grows. We present our proposed architecture for open fleet management systems and use the case of taxi services as example of our proposal.

Intelligent People Flow Coordination in Smart Spaces

In this paper, we present a short overview of the people flow coordination methods and propose a multi-agent based route recommender architecture for smart spaces which considers the influence of stress on human reactions to the recommended routes. The objective of the architecture is to ensure that people can efficiently move in and among smart spaces while at the same time improve the overall system performance. The functioning of the architecture is demonstrated on a case study. The proposed approach can be used, among others, in route recommendation in smart cities, large public events, and emergency evacuations.

Coordinating Emergency Medical Assistance

In this Chapter we propose an organization-based multi-agent application for emergency medical assistance (EMA). The application uses different Agreement Technologies (AT) to provide support to the entire process of out-of-hospital assistance to severe emergency patients and to all involved participants. The system is inspired by the operational model of the Emergency Medical Coordination Centre of the Autonomous Region of Madrid in Spain: SUMMA112. The application is also intended to reduce the average travel times of ambulances to emergency patients by making efficient use of the available resources. Regarding the latter, three different coordination mechanisms are proposed to optimize the allocation of ambulances to patients: one based on trust, an auction-based negotiation model, and auction-based negotiation with trust. We test these mechanisms in different experiments. The results empirically confirm that using AT based mechanisms can reduce the average response times of EMA services.

Intelligent event processing for emergency medical assistance

The main objective of Emergency Medical Assistance services is to attend patients with sudden diseases at any possible location within an area of influence. Especially for severe emergency patients, the potential of such systems to reduce mortality is directly related to the response time, e.g., the time a patient has to wait for an ambulance. An efficient coordination of the ambulance fleet is crucial for reducing the response times of a service. And this requires complete, real-time information about the current state of the ambulance fleet. Such information is usually transmitted by the ambulance crew members. However, due to the often stressful work of those professionals, the information is frequently not sent in a timely manner. In this paper we present an approach that addresses this problem. We use a Complex Event Processing architecture to automatically identify and transmit incidents and changes in the operational states of ambulances. As a result, the availability of information in the control centre and, thus, the effectiveness of the service is improved. The system is inspired by the operational model of SUMMA112, the Emergency Medical Coordination Centre of the Autonomous Region of Madrid in Spain.