Distributed optimal control for multi-agent trajectory optimization

Foderaro, Greg; Ferrari, Silvia; Wettergren, Thomas A.

doi:10.1016/j.automatica.2013.09.014

Cited by 74 publications

(54 citation statements)

References 9 publications

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“…The energy consumption can be modeled as a quadratic function of the vehicle control vector u. By introducing a repulsive potential function U rep generated from the obstacle geometries [19], [40], the obstacle avoidance objective can be expressed as the product of ℘ and U rep . Then, the total sensor network performance can expressed as the integral cost function,…”

Section: Distributed Optimal Control Problemmentioning

confidence: 99%

“…Because the dynamic constraints (1) are a function of the sensor (microscopic) state and control, x and u, the next step is to determine the macroscopic evolution equation for ℘ from (1). It was shown in [19], [21] that if agents are never created nor destroyed and are advected by a known velocity field (1), then the evolution of the PDF ℘ can be described by the advection equation. The advection equation is a hyperbolic partial differential equation (PDE) that governs the motion of a conserved, scalar quantity, such as a PDF, through a known velocity field [41].…”

Section: Distributed Optimal Control Problemmentioning

confidence: 99%

“…To date, DOC optimality conditions have been derived and used to solve network control problems in multi-agent path planning and navigation [19], [21]. This paper presents conservation law results that show the closed-loop DOC system is Hamiltonian.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, because existing methods assume stationary agent distributions, they cannot fully exploit the capabilities of mobile sensors, or take into account timevarying environmental conditions. The DOC approach, recently developed by the authors in [19], overcomes these limitations by optimizing a time-varying agent PDF subject to the agent dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking

Foderaro

Zhu

Wei

et al. 2018

IEEE Trans. Control Netw. Syst.

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This paper presents a distributed optimal control approach for managing omnidirectional sensor networks deployed to cooperatively track moving targets in a region of interest. Several authors have shown that, under proper assumptions, the performance of mobile sensors is a function of the sensor distribution. In particular, the probability of cooperative track detection, also known as track coverage, can be shown to be an integral function of a probability density function representing the macroscopic sensor network state. Thus, a mobile sensor network deployed to detect moving targets can be viewed as a multiscale dynamical system in which a time-varying probability density function can be identified as a restriction operator, and optimized subject to macroscopic dynamics represented by the advection equation. Simulation results show that the distributed control approach is capable of planning the motion of hundreds of cooperative sensors, such that their effectiveness is significantly increased compared to that of existing uniform, grid, random and stochastic gradient methods.

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Section: Distributed Optimal Control Problemmentioning

confidence: 99%

Section: Distributed Optimal Control Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking

Foderaro

Zhu

Wei

et al. 2018

IEEE Trans. Control Netw. Syst.

Self Cite

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“…Closely related to such systems is the concept of ensemble control, which has been exploited to achieve global control of multi-agent systems [28]. An optimal control approach using space-and time-dependent velocity fields [29] has been used to address a multi-agent trajectory planning problem. The problem that we address is a variant of the concurrent planning and task allocation problem addressed in [30].…”

mentioning

confidence: 99%

Optimal control of stochastic coverage strategies for robotic swarms

Elamvazhuthi

Berman

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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This paper addresses a trajectory planning and task allocation problem for a swarm of resource-constrained robots that cannot localize or communicate with each other and that exhibit stochasticity in their motion and task-switching policies. We model the population dynamics of the robotic swarm as a set of advection-diffusion-reaction partial differential equations (PDEs), a linear parabolic PDE model that is bilinear in the robots' velocity and task-switching rates. These parameters constitute a set of time-dependent control variables that can be optimized and broadcast to the robots prior to their deployment. The planning and allocation problem can then be formulated as a PDE-constrained optimization problem, which we solve using techniques from optimal control. Simulations of a commercial pollination scenario validate the ability of our control approach to drive a robotic swarm to achieve predefined spatial distributions of activity over a closed domain, which may contain obstacles.

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Distributed estimation based on information‐based covariance intersection algorithms

Mahmoud

2015

Adaptive Control & Signal

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A distributed estimation approach is developed in this paper using information matrix filter on a distributed tracking system in which multiple sensors are tracking the same target. The information matrix filter version is derived from covariance intersection, weighted covariance and Kalman-like particle filter, respectively. The steady performance of these filters is evaluated with different feedback strategies. The developed filters are then validated on an industrial utility boiler.± and are optimal in the sense of minimum variance. At the end of each n sampling interval, each sensor transmits its local estimate to fusion center where track association and fusion are performed.

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Distributed optimal control for multi-agent trajectory optimization

Cited by 74 publications

References 9 publications

Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking

Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking

Optimal control of stochastic coverage strategies for robotic swarms

Distributed estimation based on information‐based covariance intersection algorithms

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