Abstract
In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end we have adapted the classical ant foraging swarm algorithm to urban morphologies. A perturbed Markov chain model is shown to qualitatively reproduce the observed behaviour. This consists of perturbing the lattice random walk with a set of perturbing sources. As the number of sources increases the exploration times decrease consistently with the swarm algorithm. This model provides a better understanding of underlying process dynamics. An experimental campaign with real prototypes provided experimental validation of our models. This enables us to extrapolate conclusions to optimize electric vehicle routing in real city topologies.
Highlights
A major challenge in Smart Cities (SC) [1] is the dynamic optimization of routes under different criteria
The electrical vehicles (EV) has a map with partial information, with which it can get to know the topology of the environment but not the position of the recharge station
In this work we proposed an algorithm to optimise the arrival times to a target with unknown location for different topologies
Summary
A major challenge in Smart Cities (SC) [1] is the dynamic optimization of routes under different criteria. The objective is to manage a flood of electrical vehicles (EV) efficiently and in a sustainable way. This implies finding the correct position of charging stations and the optimization of routes to recharge the electric vehicles. The problem can be solved using different strategies. A popular method found in the literature is the vehicle routing problem (VRP) [2]. This problem was introduced by Dantzig and Ramser in 1959
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