In this paper, we investigate the problem of a last-mile delivery service that selects up to $N$ available vehicles to deliver $M$ packages from a centralized depot to $M$ delivery locations. The objective of the last-mile delivery service is to join
tly maximize customer satisfaction (minimize delivery time) and minimize operating cost (minimize total travel time) by selecting the optimal number of vehicles to perform the deliveries. We model this as an assignment (vehicles to packages) and path planning (determining the delivery order and route) problem, which is equivalent to the NP-hard multiple traveling salesperson problem. We propose a scalable heuristic algorithm, which sacrifices some optimality to achieve a reasonable computational cost for a high number of packages. The algorithm combines hierarchical clustering with a greedy search. To validate our approach, we compare the results of our simulation to experiments in a $1$:$25$ scale robotic testbed for future mobility systems.
Platooning has been exploited as a method for vehicles to minimize energy consumption. In this article, we present a constraint-driven optimal control framework that yields emergent platooning behavior for connected and automated vehicles operating i
n an open transportation system. Our approach combines recent insights in constraint-driven optimal control with the physical aerodynamic interactions between vehicles in a highway setting. The result is a set of equations that describes when platooning is an appropriate strategy, as well as a descriptive optimal control law that yields emergent platooning behavior. Finally, we demonstrate these properties in simulation and with a real-time experiment in a scaled testbed.
As robotic systems increase in autonomy, there is a strong need to plan efficient trajectories in real-time. In this paper, we propose an approach to significantly reduce the complexity of solving optimal control problems both numerically and analyti
cally. We exploit the property of differential flatness to show that it is always possible to decouple the forward dynamics of the systems state from the backward dynamics that emerge from the Euler-Lagrange equations. This coupling generally leads to instabilities in numerical approaches; thus, we expect our method to make traditional shooting methods a viable choice for optimal trajectory planning in differentially flat systems. To provide intuition for our approach, we also present an illustrative example of generating minimum-thrust trajectories for a quadrotor. Furthermore, we employ quaternions to track the quadrotors orientation, which, unlike the Euler-angle representation, do not introduce additional singularities into the model.
The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework to deliver real-time control actions that optimize travel time, energy, and safety. Hardware is an integral part of any practical impleme
ntation of CAVs, and as such, it should be incorporated in any validation method. However, high costs associated with full scale, field testing of CAVs have proven to be a significant barrier. In this paper, we present the implementation of a decentralized control framework, which was developed previously, in a scaled-city using robotic CAVs, and discuss the implications of CAVs on travel time. Supplemental information and videos can be found at https://sites.google.com/view/ud-ids-lab/tfms.
