A Decentralized Control Framework for Energy-Optimal Goal Assignment and Trajectory Generation

This paper proposes a decentralized approach for solving the problem of moving a swarm of agents into a desired formation. We propose a decentralized assignment algorithm which prescribes goals to each agent using only local information. The assignment results are then used to generate energy-optimal trajectories for each agent which have guaranteed collision avoidance through safety constraints. We present the conditions for optimality and discuss the robustness of the solution. The efficacy of the proposed approach is validated through a numerical case study to characterize the framework's performance on a set of dynamic goals.Comment: 6 pages, 3 figures, to appear at the 2019 Conference on Decision and Control, Nice, F

Beyond Reynolds: A Constraint-Driven Approach to Cluster Flocking

In this paper, we present an original set of flocking rules using an ecologically-inspired paradigm for control of multi-robot systems. We translate these rules into a constraint-driven optimal control problem where the agents minimize energy consumption subject to safety and task constraints. We prove several properties about the feasible space of the optimal control problem and show that velocity consensus is an optimal solution. We also motivate the inclusion of slack variables in constraint-driven problems when the global state is only partially observable by each agent. Finally, we analyze the case where the communication topology is fixed and connected, and prove that our proposed flocking rules achieve velocity consensus.Comment: 6 page

A Parametric Investigation and Optimization of a Cylindrical Explosive Charge

Explosive device design has a wide impact in the space, manufacturing, military, and mining industries. As a step toward computer assisted design of explosives, an optimization framework was developed using the Design Analysis Kit for Optimization and Terrascale Applications (Dakota). This software was coupled with the hydrocode CTH. This framework was applied to three exploding cylinder models, two in 1D and one in 2D. Gradient descent, dividing rectangles, and a genetic algorithm were each applied to the one-dimensional models. Parametric studies were performed as a basis for comparison with the optimization algorithms, as well as qualifying the 1D model\u27s accuracy. The gradient descent algorithm performed the best, when it converged on the optimum. Dividing rectangles took approximately twice as many iterations to converge as gradient descent, and the genetic algorithm performed marginally better than a full parametric study

Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout

Roundabouts in conjunction with other traffic scenarios, e.g., intersections, merging roadways, speed reduction zones, can induce congestion in a transportation network due to driver responses to various disturbances. Research efforts have shown that smoothing traffic flow and eliminating stop-and-go driving can both improve fuel efficiency of the vehicles and the throughput of a roundabout. In this paper, we validate an optimal control framework developed earlier in a multi-lane roundabout scenario using the University of Delaware's scaled smart city (UDSSC). We first provide conditions where the solution is optimal. Then, we demonstrate the feasibility of the solution using experiments at UDSSC, and show that the optimal solution completely eliminates stop-and-go driving while preserving safety.Comment: 6 Pages, 4 Figures, 1 tabl

A Game-Theoretic Analysis of the Social Impact of Connected and Automated Vehicles

In this paper, we address the much-anticipated deployment of connected and automated vehicles (CAVs) in society by modeling and analyzing the social-mobility dilemma in a game-theoretic approach. We formulate this dilemma as a normal-form game of players making a binary decision: whether to travel with a CAV (CAV travel) or not (non-CAV travel) and by constructing an intuitive payoff function inspired by the socially beneficial outcomes of a mobility system consisting of CAVs. We show that the game is equivalent to the Prisoner's dilemma, which implies that the rational collective decision is the opposite of the socially optimum. We present two different solutions to tackle this phenomenon: one with a preference structure and the other with institutional arrangements. In the first approach, we implement a social mechanism that incentivizes players to non-CAV travel and derive a lower bound on the players that ensures an equilibrium of non-CAV travel. In the second approach, we investigate the possibility of players bargaining to create an institution that enforces non-CAV travel and show that as the number of players increases, the incentive ratio of non-CAV travel over CAV travel tends to zero. We conclude by showcasing the last result with a numerical study

An Optimal Control Approach to Flocking

Flocking behavior has attracted considerable attention in multi-agent systems. The structure of flocking has been predominantly studied through the application of artificial potential fields coupled with velocity consensus. These approaches, however, do not consider the energy cost of the agents during flocking, which is especially important in large-scale robot swarms. This paper introduces an optimal control framework to induce flocking in a group of agents. Guarantees of energy minimization and safety are provided, along with a decentralized algorithm that satisfies the optimality conditions and can be realized in real time. The efficacy of the proposed control algorithm is evaluated through simulation in both MATLAB and Gazebo.Comment: 6 pages, 4 figures. To appear at the 2020 American Control Conferenc