An Optimal Control Approach to the Multi-Agent Persistent Monitoring Problem

Cassandras, Christos G.; Lin, Xuchao; Ding, Xu Chu

doi:10.1109/tac.2012.2225539

Cited by 114 publications

(76 citation statements)

References 18 publications

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“…Second, long-term operation will enable research on developing autonomous motion planning, control and machine learning algorithms that require long-term operation data. Furthermore, it will allow the experimental validation of various deployment [1,76] and persistent monitoring [77,78] algorithms.…”

Section: Bio-inspired Multimodal Operation Capabilitiesmentioning

confidence: 99%

Energetics in robotic flight at small scales

Karydis

Kumar

2017

Interface Focus.

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Recent advances in design, sensing and control have led to aerial robots that offer great promise in a range of real-world applications. However, one critical open question centres on how to improve the energetic efficiency of aerial robots so that they can be useful in practical situations. This review paper provides a survey on small-scale aerial robots (i.e. less than 1 m 2 area foot print, and less than 3 kg weight) from the point of view of energetics. The paper discusses methods to improve the efficiency of aerial vehicles, and reports on recent findings by the authors and other groups on modelling the impact of aerodynamics for the purpose of building energy-aware motion planners and controllers.

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Section: Bio-inspired Multimodal Operation Capabilitiesmentioning

confidence: 99%

Energetics in robotic flight at small scales

Karydis

Kumar

2017

Interface Focus.

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“…We see that the penalties of the nodes in which an agent is not located increase. Hence, it is important to find an trajectory of an agent minimizing the cost function (2). In a similar way, we can consider the case of multiple agents.…”

Section: Problemmentioning

confidence: 99%

MPC-Based Surveillance over Graphs by Multiple Agents

Kido

Kobayashi

Yamashita

2017

SICE Journal of Control, Measurement, and System Integration

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: The multi-agent surveillance problem over graphs is to find trajectories of multiple agents that travel each node as evenly as possible. This problem has several applications such as city safety management and disaster rescue. In this paper, the finite-time optimal surveillance problem is formulated, and is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, an optimal trajectory is generated by solving the MILP problem at each discrete time, and persistent surveillance can be realized. Finally, a numerical example is presented, and further discussion is also provided.

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“…The modeling of the uncertainty value R i (t) in a 2D environment is a direct extension of [14] in the 1D environment setting where it was described how persistent monitoring can be viewed as a polling system, with each rectangle Ω i associated with a "virtual queue" where uncertainty accumulates with inflow rate A i . Each agent acts as a "server" visiting these virtual queues with a time-varying service rate given by BP i (s(t)), controllable through all agent positions at time t. Metrics other than (4) are of course possible, e.g., maximizing the mutual information or minimizing the spectral radius of the error covariance matrix [17] if specific "point of interest" are identified with known properties.…”

Section: P1mentioning

confidence: 99%

“…In view of the discontinuity in the dynamics of R i (t) in (3), the optimal state trajectory may contain a boundary arc when R i (t) = 0 for any i; otherwise, the state evolves in an interior arc [18]. This follows from the fact, proved in [14] and [19] that it is never optimal for agents to reach the mission space boundary. We analyze the system operating in such an interior arc and omit the state constraint s n (t) ∈ Ω, n = 1, .…”

Section: Optimal Control Solutionmentioning

confidence: 99%

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An optimal control approach to the multi-agent persistent monitoring problem in two-dimensional spaces

Lin

Cassandras

2013

52nd IEEE Conference on Decision and Control

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Abstract-We address the persistent monitoring problem in two-dimensional mission spaces where the objective is to control the trajectories of multiple cooperating agents to minimize an uncertainty metric. In a one-dimensional mission space, we have shown that the optimal solution is for each agent to move at maximal speed and switch direction at specific points, possibly waiting some time at each such point before switching. In a twodimensional mission space, such simple solutions can no longer be derived. An alternative is to optimally assign each agent a linear trajectory, motivated by the one-dimensional analysis. We prove, however, that elliptical trajectories outperform linear ones. With this motivation, we formulate a parametric optimization problem in which we seek to determine such trajectories. We show that the problem can be solved using Infinitesimal Perturbation Analysis (IPA) to obtain performance gradients on line and obtain a complete and scalable solution. Since the solutions obtained are generally locally optimal, we incorporate a stochastic comparison algorithm for deriving globally optimal elliptical trajectories. Numerical examples are included to illustrate the main result, allow for uncertainties modeled as stochastic processes, and compare our proposed scalable approach to trajectories obtained through off-line computationally intensive solutions.

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An Optimal Control Approach to the Multi-Agent Persistent Monitoring Problem

Cited by 114 publications

References 18 publications

Energetics in robotic flight at small scales

Energetics in robotic flight at small scales

MPC-Based Surveillance over Graphs by Multiple Agents

An optimal control approach to the multi-agent persistent monitoring problem in two-dimensional spaces

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