An Eco-Route Planner for Heavy Duty Vehicles

Driving style,traffic and weather conditions have a significant impact on vehicle fuel consumption and in particular,the road freight traffic significantly contributes to the CO2 increase in atmosphere.This paper proposes an Eco-Route Planner devoted to determine and communicate to the drivers of Heavy-Duty Vehicles(HDVs)the eco-route that guarantees the minimum fuel consumption by respecting the travel time established by the freight companies.The proposed eco-route is the optimal route from origin to destination and includes the optimized speed and gear profiles.To this aim,the Cloud Computing System architecture is composed of two main components:the Data Management System that collects,fuses and integrates the raw external sources data and the Cloud Optimizer that builds the route network,selects the eco-route and determines the optimal speed and gear profiles.Finally,a real case study is discussed by showing the benefit of the proposed Eco-Route planner.

Maximizing Convergence Speed for Second Order Consensus in Leaderless Multi-Agent Systems

The paper deals with the consensus problem in a leaderless network of agents that have to reach a common velocity while forming a uniformly spaced string.Moreover,the final common velocity(reference velocity)is determined by the agents in a distributed and leaderless way.Then,the consensus protocol parameters are optimized for networks characterized by a communication topology described by a class of directed graphs having a directed spanning tree,in order to maximize the convergence rate and avoid oscillations.The advantages of the optimized consensus protocol are enlightened by some simulation results and comparison with a protocol proposed in the related literature.The presented protocol can be applied to coordinate agents such as mobile robots,automated guided vehicles(AGVs)and autonomous vehicles that have to move with the same velocity and a common inter-space gap.