110 research outputs found
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
An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections
In this paper, we provide a decentralized optimal control framework for
coordinating connected and automated vehicles (CAVs) in two interconnected
intersections. We formulate a control problem and provide a solution that can
be implemented in real time. The solution yields the optimal
acceleration/deceleration of each CAV under the safety constraint at "conflict
zones," where there is a chance of potential collision. Our objective is to
minimize travel time for each CAV. If no such solution exists, then each CAV
solves an energy-optimal control problem. We evaluate the effectiveness of the
efficiency of the proposed framework through simulation.Comment: 8 pages, 5 figures, IEEE CONFERENCE ON CONTROL TECHNOLOGY AND
APPLICATIONS 201
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
Conditions for State and Control Constraint Activation in Coordination of Connected and Automated Vehicles
Connected and automated vehicles (CAVs) provide the most intriguing
opportunity to reduce pollution, energy consumption, and travel delays. In
earlier work, we addressed the optimal coordination of CAVs using Hamiltonian
analysis. In this paper, we investigate the nature of the unconstrained problem
and provide conditions under which the state and control constraints become
active. We derive a closed-form analytical solution of the constrained
optimization problem and evaluate the solution using numerical simulation
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