Giovanni Pedroncelli

Discrete consensus in networks with constrained capacity

This paper improves a previous result on the multi-agent assignment problem, in which a group of agents has to reach a consensus on an optimal distribution of tasks, under communication and assignment constraints. Indeed, the drawbacks of the previously proposed algorithm are that it must start from an initial feasible assignment state and that an analysis on the goodness of the reached consensus is not given. In this paper, starting from any unfeasible solution, we develop an extended version of the previous gossip algorithm able to iteratively find an initial feasible assignment state in order to reach the consensus state among the agents. Moreover, a study is performed in order to evaluate how the solution obtained by the discrete consensus algorithm is close to the optimal distribution of tasks. Finally, an example shows the application of the distributed discrete consensus algorithm.

Decentralized deadlock-free control for AGV systems

This paper proposes a decentralized control strategy to assign tasks to Autonomous Guided Vehicles (AGV) and coordinate their paths to avoid deadlock and collisions. We consider a zone-controlled guidepath network where a set of intelligent vehicles (agents) has to autonomously reach a consensus about the distribution of a set of tasks, i.e., a set of zones to be reached. To this aim the agents apply a discrete consensus algorithm in order to locally minimize the global cost for reaching the destination zone. Moreover, we present a decentralized coordination protocol that is based on a zone-controlled approach with the aim of avoiding deadlock and collisions.

Fleet sizing for electric car sharing system via closed queueing networks

This paper addresses the problem of determining the optimal fleet size of electric car sharing systems. We model the system as a Discrete Event System in a closed queueing network framework considering the specific requirements of the electric vehicle utilization. Hence, we describe the asymptotic behavior of the vehicles and develop an optimization problem for maximizing the system revenue by determining the optimal fleet size. The large-scale of real-world systems results in computational difficulties in obtaining the exact solution, and so an approximate formulation is provided. Some numerical results illustrate and validate the solution method.

Assignment of electrical vehicles to charging stations by a distributed approach

This paper presents a solution for the distributed dynamic assignment of a set of electric vehicles to a network of charging stations. The drivers of the vehicles send requests for the charging of their own vehicle; then they receive the location of the station to reach for the battery charging. The problem is solved using some distributed multi-agent assignment algorithms: the stations reach a consensus solving some local integer linear programming problems. Moreover, the convergence of the algorithms is proved and an example shows the efficiency of the proposed solution.

Effects of weather condition on aircraft emissions in climb phase

Aircraft trajectory optimization is highly sensitive to atmospheric conditions; pressure, relative humidity, temperature, wind intensity and direction have various influences on thrust needed and the resulting air pollutant emissions. The airline flight plans are generally pre-calculated before take off in order to optimize fuel consumption, using information from weather predictions that may not be accurate enough. In this paper an evaluation of weather prediction accuracy and, in the case of inaccurate predictions, a comparison of estimated emissions of some flights in climb phase for different weather conditions are presented. Weather data used are from National Oceanic and Atmospheric Administration (NOAA) public domain data, specifically the GRIB (Gridded Binary) files of the 20 Km RAP (Rapid Refresh) model, containing the analysis of real weather of a certain day/hour and the forecasts of the following 18 hours. In order to better understand the relation between weather conditions and aircraft emissions we report a comparison between estimated emissions (fuel, CO2 and NOX) of a real trajectory calculated with real weather data and with predicted weather data (forecasts for 1h, 3h and 6h). In order to evaluate accuracy of forecasts we consider radar reflectivity and wind. Regarding evaluation of the presence of potentially dangerous clouds (level 2 or more), a threshold filter has been used to select regions above 36 dBZ in the weather analysis and in a previous forecast related to the same hour. In the first step, the radar reflectivity and wind of real USA weather data from four days of June 2012 were compared with the forecasts, using the Tanimoto Similarity Index for measuring accuracy. Given the exact shape on a grid of the region (in this case, the current weather analysis) and its approximation (in this case the forecast), the Tanimoto Index (TI) is defined as the number of “pixels” of intersection on the number of pixels of the union of the two images. Then each one of the weather analyses for the 4 days considered (96 hours total) was compared with the forecasts for that time from 1 to 6 hours before, computing the Tanimoto index and the total cloud coverage with a threshold at 36 dBz. Furthermore, the wind intensity and direction forecasts were analyzed, and the mean value and variance of the difference between real weather condition and forecasts are presented. In a second step, from the analysis of the results of the first step, we selected some regions where cloud and wind analysis were substantially different from forecasts. In this scenario, the climb phases from real aircraft trajectories were collected from the FlightAware database. In the region of bad weather, we selected the trajectories that were significantly different from those made from the same aircraft in days of good weather. The related emissions were estimated and compared with the emissions of the same trajectory using forecasted weather. The emission estimation model is based on BADA (Base of Performance Data) from EUROCONTROL, ICAO and weather data.

A software tool for the decentralized control of AGV systems

This paper presents a software tool for the simulation of a decentralized control strategy to assign tasks to Autonomous Guided Vehicles (AGV) and coordinate their paths to avoid deadlock and collisions. We consider a zone-controlled guidepath network where a set of intelligent vehicles (agents) has to autonomously reach a consensus about the distribution of a set of tasks, i.e., a set of zones to be reached. To this aim, first the agents apply a discrete consensus algorithm in order to locally minimize the global cost for reaching the destination zone, then they move according to a decentralized coordination protocol that is based on a zone-controlled approach with the aim of avoiding deadlock and collisions. The software tool allows the user to define the guidepath network, then randomly generates the positions of AGVs and destinations and runs the two algorithms visually showing their behavior.

A Quantized Consensus Algorithm for a Multi-agent Assignment Problem

This paper improves a previous result on the multi-agent assignment problem, in which a group of agents has to reach a consensus on an optimal distribution of tasks, under communication and assignment constraints. However, the drawback of the proposed distributed algorithm was that the initial feasible assignment state is given. In this paper we develop a start-up algorithm to find an initial feasible assignment state based on synchronous communications among agents. Moreover, the agents exchange the messages and update autonomously and iteratively the task assignment. Some simulation results prove that the proposed consensus algorithm not only is able to reach a feasible solution but such a solution is close to the optimal one.

Discrete consensus for asynchronous distributed task assignment

This paper deals with the problem of assigning tasks to a set of nodes communicating in a connected graph topology to satisfy the following requirements: assigning all the tasks to the agents; assigning to each agent no more than M tasks; minimizing the maximum total load of each agent. A gossip-based algorithm is presented: starting from an unfeasible solution, at each iteration a node solves a Local-Integer Linear Programming problem with its neighbors (i.e., the connected nodes in the communication graph). The convergence of the algorithm is proved and the expected convergence time is evaluated. A simulation campaign shows experimental results on the performance of the proposed approach.

A framework for the distributed management of charging operations

This paper proposes a solution for the distributed dynamic assignment of a set of electric vehicles to a network of charging stations. Drivers of the electric vehicles and charging stations exchange messages using a communication protocol. Drivers of the vehicles send requests for the charging of their own vehicle in prefixed timeslots; charging stations perform a series of distributed optimizations in order to reach a common assignment of the vehicles needing to recharge and communicate the reached assignment to the drivers. The optimization problem is solved using some distributed multi-agent assignment algorithms: the stations reach a consensus solving some local integer linear programming problems.

Modeling and Optimization of Aircraft Trajectories: A review

An important problem of the Air Traffic Management is the determination and optimization of safe aircraft trajectories in order to avoid hazardous weather regions and other aircrafts. This paper presents a review of the main approaches about the modeling and optimization of the aircraft trajectories pursuing two main objectives: i) resolving conflicts with other aircrafts; ii) avoiding hazardous weather.