Large-Scale High-Fidelity Agent-Based Simulation in Air Traffic Domain

Abstract

We present the concept of dynamic partitioning of scalable, high-fidelity multi-agent simulation complemented with intelligent load-balancing processes. The simulation framework is designed to simulate entities to high details that require extended computation resources. To be able to simulate a huge amount of entities, distributed simulation is introduced using spatial partitioning and dynamic load balancing. A novel and important feature is the combination of the synchronous and asynchronous parts in the simulation. We use the domain of the air traffic simulation to verify the simulation framework. We present a method to perform spatial and temporal planning within 3D space and multilayer architecture using several collision avoidance algorithms to illustrate the high computational demands of each airplane. The platform has been used to support simulation of an entire civilian air traffic touching the national airspace of United States. A thorough evaluation of the system has been performed, confirming that it 502 can scale up to a very high number of complex agents operating simultaneously (thousands of aircrafts) with full detailed models.

A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment

“…More detail about the algorithm can be found in [15]. Particle position and velocity gets updated using (5).…”

“…The main issue addressed by previous research on route planning system is how to direct vehicle(s) to destination(s) in a network while providing efficient maneuvers and reducing travel time. Some instances of route planning systems applications are in the areas of traffic control [5], real time routing and trip planning [6], and so on. Briefly reviewing the most highlights in route planning works in the state of the art, in [7] a route planning strategy is employed for transportation purpose in a form of multi-agent decisions in which the agent is in charge of order distribution to customers, traversing edges, competing vendors, increasing production and etc.…”

Biogeography-based combinatorial strategy for efficient autonomous underwater vehicle motion planning and task-time management

“…Each given solution hi is modified according to probability of Ps(t) that is the probability of existence of the S species at time t in habitat hi. To have S species at time (t+Δt) in a specific habitat hi, one of the following conditions must hold: [1] The S species exist in hi at t, and no emigration or immigration is occurred from t to (t+Δt); [2] One species immigrated onto an island already occupied by (S-1) species at t; [3] One species emigrated from an island occupied by (S+1) species at t;…”

“…Many attempts have been carried out in scope of single or multiple vehicles motion planning and task allocation using different strategies. Some instances of route planning systems applications are in the areas of traffic control [1], real time routing and trip planning [2], modelling the transportation network to find the shortest paths [3], etc. The application of genetic algorithm is investigated by [4] in a dynamic route guidance system.…”

“…In most of previous researches, vehicles routing strategies mostly have been investigated for vehicles' mission planning, task allocation and time scheduling purposes [1][2][3][4][5][6][7][8][9], while the path planning strategies have been proposed to find the optimum safe path to the predefined target point [10][11][12][13][14][15][16]. Combining these two strategies helps to cover shortcoming associated with each of them and furnishes objectives of both vehicle task assign/routing and collision free path planning problem taking uncertainty and dynamicity of the environment into account.…”